Determination of equivalent single axle load (ESAL) factor for Georgia pavement design

GEORGIA DOT RESEARCH PROJECT 18-04 FINAL REPORT
DETERMINATION OF EQUIVALENT SINGLE AXLE LOAD (ESAL) FACTOR FOR
GEORGIA PAVEMENT DESIGN
OFFICE OF PERFORMANCE-BASED MANAGEMENT AND RESEARCH
600 WEST PEACHTREE STREET NW | ATLANTA, GA 30308

TECHNICAL REPORT DOCUMENTATION

1. Report No. FHWA-GA-21-1804

2. Government Accession No. N/A

4. Title and Subtitle Determination of Equivalent Single Axle Load (ESAL) Factor for Georgia Pavement

Design

3. Recipient's Catalog No. N/A 5. Report Date July 2021 6. Performing Organization Code N/A

7. Author(s) S. Sonny Kim, Ph.D., P.E.; Jidong J. Yang, Ph.D., P.E.; Stephan A. Durham, Ph.D., P.E.; In Kee Kim, Ph.D.; Narges Tahaei Yaghoubi

8. Performing Organization Report No. 18-04

9. Performing Organization Name and Address University of Georgia, College of Engineering Driftmier Engineering Center, Athens, GA 30602 Phone: (706) 542-9804, Email: kims@uga.edu
12. Sponsoring Agency Name and Address Georgia Department of Transportation Office of Performance-based Management and Research 600 West Peachtree Street NW, Atlanta, GA 30308

10. Work Unit No. N/A
11. Contract or Grant No. PI# 0016338
13. Type of Report and Period Covered Final Report (October 2018July 2021)
14. Sponsoring Agency Code N/A

15. Supplementary Notes Conducted in cooperation with the U.S. Department of Transportation, Federal Highway Administration.
16. Abstract The Georgia Department of Transportation (GDOT) is currently using the 1972 AASHTO Pavement Design Guide in which the damage caused by traveling vehicles in the pavement's design life is defined in terms of equivalent single axle load (ESAL). The last updates of truck ESAL factors in Georgia were made in 1984. Thus, there is a need to update ESAL factors due to the changes in traffic patterns over time, especially during recent years. In this study, truck ESAL factors were updated using actual traffic loadings from weigh-in-motion (WIM) sensors installed throughout Georgia. As GDOT is adopting the pavement mechanistic empirical (ME) design, customized truck traffic classification (TTC) groups were developed as well to simplify the pavement ME design process, which requires high-dimensional traffic feature inputs by categories, including vehicle class distributions (VCDs), monthly distribution factors (MDFs), hourly distribution factors (HDFs), and normalized axle load spectra (NALS). Specifically, an effective data analytics procedure was developed to reduce the high-dimensional traffic features by stratified principal component analysis (PCA), followed by K-means clustering to establish the appropriate TTC groups. For a case study, the performance of two typical pavement designs was evaluated using the AASHTOWare Pavement ME Design software with respect to two scenarios of traffic inputs: (1) the derived cluster-based groups, and (2) the national default TTC groups. The results indicated that direct application of the national default TTC groups resulted in over-design of pavement structure, especially the jointed plain concrete pavement (JPCP), in Georgia. Therefore, it is recommended that customized TTC groups derived from state-specific WIM data should be used.

17. Key Words

18. Distribution Statement

Weigh-in-Motion (WIM) Sensors, Pavement ME Software, Traffic No Restrictions Inputs

19. Security Classification (of this report) Unclassified

20. Security Classification (of this 21. No. of Pages

page) Unclassified

196

22. Price Free

Research Project 18-04

Final Report

DETERMINATION OF EQUIVALENT SINGLE AXLE LOAD (ESAL) FACTOR FOR GEORGIA PAVEMENT DESIGN

By

S. Sonny Kim, Ph.D., P.E Associate Professor
Civil Engineering, College of Engineering University of Georgia

Jidong J. Yang, Ph.D., P.E. Associate Professor
Civil Engineering, College of Engineering University of Georgia

In Kee Kim, Ph.D. Assistant Professor Department of Computer Science University of Georgia

Stephan A. Durham, Ph.D., P.E. Professor
Civil Engineering, College of Engineering University of Georgia

Narges Tahaei Yaghoubi Graduate Research Assistant Civil Engineering, College of Engineering
University of Georgia

Contract with Georgia Department of Transportation
In cooperation with U.S. Department of Transportation Federal Highway Administration
July 2021
The contents of this report reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Georgia Department of Transportation or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation.

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DISCLAIMER STATEMENT This document is disseminated under the sponsorship of the Georgia Department of Transportation and the United States Department of Transportation in the interest of information exchange. The State of Georgia and the United States Government assume no liability of its contents or use thereof. The contents of this report reflect the views of the authors, who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official policies of the Georgia Department of Transportation or the United States Department of Transportation. The State of Georgia and the United States Government do not endorse products of manufacturers. Trademarks or manufacturers' names appear herein only because they are considered essential to the object of this document.
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Symbol
in ft yd mi
in2 ft2 yd2 ac mi2
fl oz gal ft3 yd3
oz lb T
oF
fc fl
lbf lbf/in2
Symbol
mm m m km
mm2 m2 m2 ha km2
mL L m3 m3
g kg Mg (or "t")
oC
lx cd/m2
N kPa

SI* (MODERN METRIC) CONVERSION FACTORS

APPROXIMATE CONVERSIONS TO SI UNITS

When You Know

Multiply By

To Find

inches feet yards miles

LENGTH
25.4 0.305 0.914 1.61

millimeters meters meters kilometers

square inches square feet square yard acres square miles

AREA
645.2 0.093 0.836 0.405 2.59

square millimeters square meters square meters hectares square kilometers

fluid ounces gallons cubic feet cubic yards

VOLUME

29.57

milliliters

3.785

liters

0.028

cubic meters

0.765

cubic meters

NOTE: volumes greater than 1000 L shall be shown in m3

ounces pounds short tons (2000 lb)

MASS
28.35 0.454 0.907

grams kilograms megagrams (or "metric ton")

Fahrenheit

TEMPERATURE (exact degrees)

5 (F-32)/9

Celsius

or (F-32)/1.8

foot-candles foot-Lamberts

ILLUMINATION
10.76 3.426

lux candela/m2

FORCE and PRESSURE or STRESS

poundforce

4.45

newtons

poundforce per square inch

6.89

kilopascals

APPROXIMATE CONVERSIONS FROM SI UNITS

When You Know

Multiply By

To Find

millimeters meters meters kilometers
square millimeters square meters square meters hectares square kilometers

LENGTH
0.039 3.28 1.09 0.621
AREA
0.0016 10.764
1.195 2.47 0.386

inches feet yards miles
square inches square feet square yards acres square miles

milliliters liters cubic meters cubic meters

VOLUME
0.034 0.264 35.314 1.307

fluid ounces gallons cubic feet cubic yards

grams kilograms megagrams (or "metric ton")

MASS
0.035 2.202 1.103

ounces pounds short tons (2000 lb)

Celsius

TEMPERATURE (exact degrees)

1.8C+32

Fahrenheit

lux candela/m2

ILLUMINATION
0.0929 0.2919

foot-candles foot-Lamberts

newtons kilopascals

FORCE and PRESSURE or STRESS

0.225

poundforce

0.145

poundforce per square inch

Symbol
mm m m km
mm2 m2 m2 ha km2
mL L m3 m3
g kg Mg (or "t")
oC
lx cd/m2
N kPa
Symbol
in ft yd mi
in2 ft2 yd2 ac mi2
fl oz gal ft3 yd3
oz lb T
oF
fc fl
lbf lbf/in2

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TABLE OF CONTENTS
EXECUTIVE SUMMARY .......................................................................................................... 1 CHAPTER 1. INTRODUCTION ................................................................................................ 3
Background and Problem Statement ....................................................................................... 3 Study Objectives......................................................................................................................... 5 CHAPTER 2. LITERATURE REVIEW .................................................................................... 6 ESAL Factors ............................................................................................................................. 6
ESAL Factors for Flexible Pavements .................................................................................... 7 ESAL Factors for Rigid Pavements ........................................................................................ 8 Site-Specific Truck ESAL Factors .......................................................................................... 9 ESAL Factors Used by State DOTs........................................................................................ 10 Georgia DOT ......................................................................................................................... 10 Virginia DOT......................................................................................................................... 11 North Carolina DOT.............................................................................................................. 13 Comparison of ESAL Factors ............................................................................................... 13 Differences Between 1972 and 1993 AASHTO Pavement Design Guide............................ 16 Reliability.................................................................................................................................. 17 CHAPTER 3. WEIGH IN MOTION ........................................................................................ 19 Background .............................................................................................................................. 19 Types of WIM Sensors............................................................................................................. 20 Bending Plate......................................................................................................................... 20 Load Cell ............................................................................................................................... 21 Polymer Piezo Sensor............................................................................................................ 22 Quartz Piezo Sensor .............................................................................................................. 23 Strain Gauge Strip Sensor ..................................................................................................... 24 WIM Sensors in Georgia ....................................................................................................... 25 Missing and Erroneous Data................................................................................................... 25 Quality Control Checks ........................................................................................................... 26 New Mexico DOT ................................................................................................................. 26 North Carolina DOT.............................................................................................................. 27 Georgia DOT ......................................................................................................................... 30 CHAPTER 4. DATA ACQUISITION ...................................................................................... 32 Properties of Georgia WIM Sites ........................................................................................... 32
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Inactive WIM Stations............................................................................................................. 33 WIM Data Analysis.................................................................................................................. 33 WIM Data Quality Control ..................................................................................................... 37 WIM Data After QC Checks................................................................................................... 37 Data Acquisition....................................................................................................................... 38 CHAPTER 5. DEVELOPMENT OF GDOT TRUCK ESAL FACTORS............................. 40 Georgia ESAL Factors ............................................................................................................ 40
ESAL Factors Variation vs. Structural Number .................................................................... 40 ESAL Factors Variation vs. Slab Thickness ......................................................................... 40 ESAL Factors Variation vs. Vehicle Classes ........................................................................ 42 Reliability Inclusion in Georgia ESAL Factors..................................................................... 43 Results ....................................................................................................................................... 49 Cost Analysis ............................................................................................................................ 49 CHAPTER 6. DEVELOPMENT OF PAVEMENT ME INPUTS ......................................... 52 Traffic Inputs in AASHTO MEPDG MOP ........................................................................... 52 Vehicle Class Distribution..................................................................................................... 52 Monthly Distribution Factor .................................................................................................. 53 Hourly Distribution Factor .................................................................................................... 53 Axles per Truck Class ........................................................................................................... 53 Axle Load Distribution Factors or Normalized Axle Load Spectra ...................................... 53 Truck Traffic Classification Groups ...................................................................................... 54 Machine Learning Techniques ............................................................................................... 57 Principal Component Analysis .............................................................................................. 57 Clustering Technique............................................................................................................. 59 Pavement Performance Analysis and Results ....................................................................... 64 CHAPTER 7. CONCLUSIONS AND RECOMMENDATIONS ........................................... 69 Conclusions ............................................................................................................................... 69 Recommendations .................................................................................................................... 70 APPENDIX A: VEHICLE CLASS DISTRIBUTION BY MONTH FOR DIRECTIONAL WIM STATIONS ........................................................................................................................ 72 APPENDIX B: WIM DATA ANALYSIS AFTER QC CHECKS .......................................... 81 APPENDIX C: MEPDG TRAFFIC INPUT DATA EXTRACTED FROM WIM DATA AS FEATURE CATEGORIES........................................................................................................ 95
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APPENDIX D: ESAL FACTOR CALCULATION RESULTS ........................................... 156 APPENDIX E: PROPOSED STANDARD OPERATING PROCEDURE (SOP) .............. 168 ACKNOWLEDGMENTS ........................................................................................................ 175 REFERENCES.......................................................................................................................... 176
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LIST OF FIGURES
Figure 1. Diagram. 18-kip single axle load..................................................................................... 6 Figure 2. Chart. FHWA vehicle classification system.................................................................. 10 Figure 3. Line graph. Truck ESAL factors history for Georgia.................................................... 11 Figure 4. Bar graph. DOTs' ESAL factor comparison results on rural interstate highways for
flexible pavement.................................................................................................................... 14 Figure 5. Bar graph. DOTs' ESAL factor comparison results on urban interstate highways
for flexible pavement. ............................................................................................................. 14 Figure 6. Bar graph. DOTs' ESAL factor comparison results on rural interstate highways for
rigid pavement. ....................................................................................................................... 15 Figure 7. Bar graph. DOTs' ESAL factor comparison results on urban interstate highways
for rigid pavement. .................................................................................................................. 15 Figure 8. Illustration. Bending plate sensor (FHWA 2018).......................................................... 21 Figure 9. Photo. Bending plate installation (FHWA 2018). ......................................................... 21 Figure 10. Illustration. Load cell sensor (FHWA 2018). .............................................................. 22 Figure 11. Illustration. Load cell installation (FHWA 2018). ...................................................... 22 Figure 12. Photo. Polymer piezo sensor (FHWA 2018). .............................................................. 23 Figure 13. Illustration. Quartz piezo sensor (FHWA 2018). ........................................................ 24 Figure 14. Illustration. Quartz piezo installation depiction (FHWA 2018) .................................. 24 Figure 15. Illustration. Strain gauge strip sensor (FHWA 2018) .................................................. 25 Figure 16. Map. Active WIM stations with available data in Georgia ......................................... 32 Figure 17. Screenshot. Inactive WIM station in TADA. .............................................................. 35 Figure 18. Line graph. Vehicle class distribution by month, Site 185-0227 NB.......................... 35 Figure 19. Line graph. Vehicle class distribution by month, Site 185-0227 SB .......................... 36 Figure 20. Line graph. GVW frequency distribution, Site 185-0227. .......................................... 36 Figure 21. Line graph. Monthly distribution factors, Site 185-0227 NB...................................... 37
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Figure 22. Line graph. Monthly distribution factors, Site 185-0227 SB ...................................... 38 Figure 23. Line graph. GVW frequency distribution, Site 185-0227. .......................................... 38 Figure 24. Line graph. ESAL factors with different SNs vs. vehicle classes, Site 185-0227....... 41 Figure 25. Line graph. ESAL factors with different slab thicknesses vs. vehicle classes, Site
185-0227. ................................................................................................................................ 41 Figure 26. Line graph. ESAL factors comparison with different SNs for FHWA vehicle
classes, Site 185-0227. ............................................................................................................ 42 Figure 27. Line graph. ESAL factors comparison with different slab thicknesses for FHWA
vehicle classes, Site 185-0227. ............................................................................................... 43 Figure 28. Line graph. Truck traffic classification groups based on National Cooperative
Highway Research Program (NCHRP) Project 1-37A ........................................................... 55 Figure 29. Bar graph. Determining the optimal number of principal components for the
attributes.................................................................................................................................. 58 Figure 30. Line graph. Elbow method for determining K............................................................. 61 Figure 31. Illustration. Nested PCA procedure ............................................................................. 61 Figure 32. Plot. Clustering result and loading vectors. ................................................................. 62 Figure 33. Map. Clusters of WIM sites......................................................................................... 64 Figure 34. Line graph. Traffic pattern comparison of clusters and default TTC groups. ............. 65 Figure 35. Line graph. JPCP pavement performance comparison of cluster-based traffic
inputs and default TTC groups. .............................................................................................. 67 Figure 36. Line graph. Flexible pavement performance comparison of cluster-based traffic
inputs and default TTC groups. .............................................................................................. 68 Figure 37. Line graph. Vehicle class distribution by month, Site 285-0243 NB.......................... 72 Figure 38. Line graph. Vehicle class distribution by month, Site 285-0243 SB .......................... 72 Figure 39. Line graph. Vehicle class distribution by month, Site 021-w334 NB ......................... 73 Figure 40. Line graph. Vehicle class distribution by month, Site 021-w334 SB.......................... 73 Figure 41. Line graph. Vehicle class distribution by month, Site 127-0312 NB.......................... 74 Figure 42. Line graph. Vehicle class distribution by month, Site 127-0312 SB .......................... 74
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Figure 43. Line graph. Vehicle class distribution by month, Site 143-0126 EB .......................... 75 Figure 44. Line graph. Vehicle class distribution by month, Site 143-0126 WB......................... 75 Figure 45. Line graph. Vehicle class distribution by month, Site 051-0368 EB .......................... 76 Figure 46. Line graph. Vehicle class distribution by month, Site 051-0368 WB......................... 76 Figure 47. Line graph. Vehicle class distribution by month, Site 051-0387 NB.......................... 77 Figure 48. Line graph. Vehicle class distribution by month, Site 051-0387 SB .......................... 77 Figure 49. Line graph. Vehicle class distribution by month, Site 217-0218 EB .......................... 78 Figure 50. Line graph. Vehicle class distribution by month, Site 217-0218 WB......................... 78 Figure 51. Line graph. Vehicle class distribution by month, Site 245-0218 EB .......................... 79 Figure 52. Line graph. Vehicle class distribution by month, Site 245-0218 WB......................... 79 Figure 53. Line graph. Vehicle class distribution by month, Site 175-0247 EB .......................... 80 Figure 54. Line graph. Vehicle class distribution by month, Site 175-0247 WB......................... 80 Figure 55. Line graph. Monthly distribution factors, Site 285-0243 NB...................................... 81 Figure 56. Line graph. Monthly distribution factors, Site 285-0243 SB ...................................... 81 Figure 57. Line graph. Monthly distribution factors, Site 021-w334 NB..................................... 82 Figure 58. Line graph. Monthly distribution factors, Site 021-w334 SB ..................................... 82 Figure 59. Line graph. Monthly distribution factors, Site 127-0312 NB...................................... 83 Figure 60. Line graph. Monthly distribution factors, Site 127-0312 SB ...................................... 83 Figure 61. Line graph. Monthly distribution factors, Site 143-0126 EB ...................................... 84 Figure 62. Line graph. Monthly distribution factors, Site 143-0126 WB..................................... 84 Figure 63. Line graph. Monthly distribution factors, Site 051-0368 EB ...................................... 85 Figure 64. Line graph. Monthly distribution factors, Site 051-0368 WB..................................... 85 Figure 65. Line graph. Monthly distribution factors, Site 051-0387 NB...................................... 86 Figure 66. Line graph. Monthly distribution factors, Site 051-0387 SB ...................................... 86 Figure 67. Line graph. Monthly distribution factors, Site 217-0218 EB ...................................... 87
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Figure 68. Line graph. Monthly distribution factors, Site 217-0218 WB..................................... 87 Figure 69. Line graph. Monthly distribution factors, Site 245-0218 EB ...................................... 88 Figure 70. Line graph. Monthly distribution factors, Site 245-0218 WB..................................... 88 Figure 71. Line graph. Monthly distribution factors, Site 175-0247 EB ...................................... 89 Figure 72. Line graph. Monthly distribution factors, Site 175-0247 WB..................................... 89 Figure 73. Line graph. GVW frequency distribution, Site 285-0243. .......................................... 90 Figure 74. Line graph. GVW frequency distribution, Site 021-w334. ......................................... 90 Figure 75. Line graph. GVW frequency distribution, Site 127-0312. .......................................... 91 Figure 76. Line graph. GVW frequency distribution, Site 143-0126. .......................................... 91 Figure 77. Line graph. GVW frequency distribution, Site 051-0368. .......................................... 92 Figure 78. Line graph. GVW frequency distribution, Site 051-0387. .......................................... 92 Figure 79. Line graph. GVW frequency distribution, Site 217-0218. .......................................... 93 Figure 80. Line graph. GVW frequency distribution, Site 245-0218. .......................................... 93 Figure 81. Line graph. GVW frequency distribution, Site 175-0247. .......................................... 94 Figure 82. Bar graph. Comparison of truck ESAL factors for flexible pavement. ..................... 167 Figure 83. Bar graph. Comparison of truck ESAL factors for rigid pavement. ......................... 167
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LIST OF TABLES
Table 1. Default ESAL factors used by GDOT. ............................................................................. 4 Table 2. Virginia's average truck ESAL factors for flexible pavement by vehicle
classification and administrative roadway classification ........................................................ 12 Table 3. Virginia's average truck ESAL factors for rigid pavement by vehicle classification
and administrative roadway classification .............................................................................. 12 Table 4. Average truck ESAL factors by vehicle classification and administrative roadway
classification ........................................................................................................................... 13 Table 5. Suggested levels of reliability for various functional classifications.............................. 17 Table 6. Standard normal deviates for various levels of reliability .............................................. 18 Table 7. NMDOT QC rule list for WIM data. .............................................................................. 27 Table 8. NCDOT QC rule list for class data. ................................................................................ 28 Table 9. NCDOT QC rule list for weight data .............................................................................. 29 Table 10. GDOT QC rules list for WIM sites. .............................................................................. 30 Table 11. Properties of WIM Sites in Georgia.............................................................................. 34 Table 12. GDOT's default truck ESAL factors. ........................................................................... 43 Table 13. Truck ESAL factors with different reliability levels for flexible pavement design,
structural number 4. ................................................................................................................ 45 Table 14. Truck ESAL factors with different reliability levels for flexible pavement design,
structural number 6. ................................................................................................................ 45 Table 15. Truck ESAL factors with different reliability levels for flexible pavement design,
structural number 8. ................................................................................................................ 46 Table 16. Truck ESAL factors with different reliability levels for rigid pavement design,
slab thickness of 8. .................................................................................................................. 46 Table 17. Truck ESAL factors with different reliability levels for rigid pavement design,
slab thickness of 10. ................................................................................................................ 47 Table 18. Truck ESAL factors with different reliability levels for rigid pavement design,
slab thickness of 12. ................................................................................................................ 47
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Table 19. Type of interstate highway for each Georgia WIM site................................................ 48 Table 20. Truck ESAL factors with different reliability levels for flexible pavement design
in rural WIM stations, structural number 8............................................................................. 48 Table 21. Truck ESAL factors with different reliability levels for flexible pavement design
in urban WIM stations, structural number 8. .......................................................................... 48 Table 22. Truck ESAL factors with different reliability levels for rigid pavement design in
rural WIM stations, slab thickness of 12................................................................................. 49 Table 23. Truck ESAL factors with different reliability levels for rigid pavement design in
urban WIM stations, slab thickness of 12. .............................................................................. 49 Table 24. 1972 pavement design inputs for rural WIM stations................................................... 50 Table 25. 1972 pavement design inputs for urban WIM stations. ................................................ 50 Table 26. 1972 pavement design results for WIM stations using GDOT's default ESAL
factors...................................................................................................................................... 51 Table 27. 1972 pavement design results for WIM stations using 85% reliability level ESAL
factors...................................................................................................................................... 51 Table 28. 1972 pavement design results for WIM stations using 90% reliability level ESAL
factors...................................................................................................................................... 51 Table 29. Average increase in HMA cost. .................................................................................... 51 Table 30. TTC group description and corresponding vehicle class distribution default values
(percentages) (ARA, Inc. 2004).............................................................................................. 56 Table 31. Percent of variance explained by feature categories. .................................................... 59 Table 32. Loading factors of 20 lower-level PCs ......................................................................... 62 Table 33. Vehicle class distribution, Site 185-0227 NB............................................................... 95 Table 34. Vehicle class distribution, Site 185-0227 SB ............................................................... 95 Table 35. Vehicle class distribution, Site 285-0243 NB............................................................... 95 Table 36. Vehicle class distribution, Site 285-0243 SB ............................................................... 95 Table 37. Vehicle class distribution, Site 021-w334 NB .............................................................. 95 Table 38. Vehicle class distribution, Site 021-w334 SB............................................................... 95 Table 39. Vehicle class distribution, Site 127-0312 NB............................................................... 96
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Table 40. Vehicle class distribution, Site 127-0312 SB ............................................................... 96 Table 41. Vehicle class distribution, Site 051-0387 NB............................................................... 96 Table 42. Vehicle class distribution, Site 051-0387 SB ............................................................... 96 Table 43. Vehicle class distribution, Site 217-0218 EB ............................................................... 96 Table 44. Vehicle class distribution, Site 217-0218 WB.............................................................. 96 Table 45. Vehicle class distribution, Site 051-0368 EB ............................................................... 96 Table 46. Vehicle class distribution, Site 051-0368 WB.............................................................. 97 Table 47. Vehicle class distribution, Site 143-0126 EB ............................................................... 97 Table 48. Vehicle class distribution, Site 143-0126 WB.............................................................. 97 Table 49. Vehicle class distribution, Site 245-0218 EB ............................................................... 97 Table 50. Vehicle class distribution, Site 245-0218 WB.............................................................. 97 Table 51. Vehicle class distribution, Site 175-0247 EB ............................................................... 97 Table 52. Vehicle class distribution, Site 175-0247 WB.............................................................. 97 Table 53. Monthly distribution factors, Site 185-0227 NB .......................................................... 98 Table 54. Monthly distribution factors, Site 185-0227 SB ........................................................... 98 Table 55. Monthly distribution factors, Site 285-0243 NB .......................................................... 99 Table 56. Monthly distribution factors, Site 285-0243 SB ........................................................... 99 Table 57. Monthly distribution factors, Site 021-w334 NB ....................................................... 100 Table 58. Monthly distribution factors, Site 021-w334 SB ........................................................ 100 Table 59. Monthly distribution factors, Site 127-0312 NB ........................................................ 101 Table 60. Monthly distribution factors, Site 127-0312 SB ......................................................... 101 Table 61. Monthly distribution factors, Site 051-0387 NB ........................................................ 102 Table 62. Monthly distribution factors, Site 051-0387 SB ......................................................... 102 Table 63. Monthly distribution factors, Site 217-0218 EB......................................................... 103 Table 64. Monthly distribution factors, Site 217-0218 WB ....................................................... 103
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Table 65. Monthly distribution factors, Site 051-0368 EB......................................................... 104 Table 66. Monthly distribution factors, Site 051-0368 WB ....................................................... 104 Table 67. Monthly distribution factors, Site 143-0126 EB......................................................... 105 Table 68. Monthly distribution factors, Site 143-0126 WB ....................................................... 105 Table 69. Monthly distribution factors, Site 245-0218 EB......................................................... 106 Table 70. Monthly distribution factors, Site 245-0218 WB ....................................................... 106 Table 71. Monthly distribution factors, Site 175-0247 EB......................................................... 107 Table 72. Monthly distribution factors, Site 175-0247 WB ....................................................... 107 Table 73. Hourly distribution factors, Site 185-0227 NB........................................................... 108 Table 74. Hourly distribution factors, Site 185-0227 SB ........................................................... 108 Table 75. Hourly distribution factors, Site 285-0243 NB........................................................... 109 Table 76. Hourly distribution factors, Site 285-0243 SB ........................................................... 109 Table 77. Hourly distribution factors, Site 021-w334 NB.......................................................... 109 Table 78. Hourly distribution factors, Site 021-w334 SB........................................................... 110 Table 79. Hourly distribution factors, Site 127-0312 NB........................................................... 110 Table 80. Hourly distribution factors, Site 127-0312 SB ........................................................... 110 Table 81. Hourly distribution factors, Site 051-0387 NB........................................................... 111 Table 82. Hourly distribution factors, Site 051-0387 SB ........................................................... 111 Table 83. Hourly distribution factors, Site 217-0218 EB ........................................................... 111 Table 84. Hourly distribution factors, Site 217-0218 WB.......................................................... 112 Table 85. Hourly distribution factors, Site 051-0368 EB ........................................................... 112 Table 86. Hourly distribution factors, Site 051-0368 WB.......................................................... 112 Table 87. Hourly distribution factors, Site 143-0126 EB ........................................................... 113 Table 88. Hourly distribution factors, Site 143-0126 WB.......................................................... 113 Table 89. Hourly distribution factors, Site 245-0218 EB ........................................................... 113
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Table 90. Hourly distribution factors, Site 245-0218 WB.......................................................... 114 Table 91. Hourly distribution factors, Site 175-0247 EB ........................................................... 114 Table 92. Hourly distribution factors, Site 175-0247 WB.......................................................... 115 Table 93. Single-axle load distribution factors, Site 185-0227 NB ............................................ 116 Table 94. Single-axle load distribution factors, Site 185-0227 SB............................................. 117 Table 95. Single-axle load distribution factors, Site 285-0243 NB ............................................ 118 Table 96. Single-axle load distribution factors, Site 285-0243 SB............................................. 119 Table 97. Single-axle load distribution factors, Site 021-w334 NB ........................................... 120 Table 98. Single-axle load distribution factors, Site 021-w334 SB............................................ 121 Table 99. Single-axle load distribution factors, Site 127-0312 NB ............................................ 122 Table 100. Single-axle load distribution factors, Site 127-0312 SB........................................... 123 Table 101. Single-axle load distribution factors, Site 051-0387 NB .......................................... 124 Table 102. Single-axle load distribution factors, Site 051-0387 SB........................................... 125 Table 103. Single-axle load distribution factors, Site 217-0218 EB .......................................... 126 Table 104. Single-axle load distribution factors, Site 217-0218 WB ......................................... 127 Table 105. Single-axle load distribution factors, Site 051-0368 EB .......................................... 128 Table 106. Single-axle load distribution factors, Site 051-0368 WB ......................................... 129 Table 107. Single-axle load distribution factors, Site 143-0126 EB .......................................... 130 Table 108. Single-axle load distribution factors, Site 143-0126 WB ......................................... 131 Table 109. Single-axle load distribution factors, Site 245-0218 EB .......................................... 132 Table 110. Single-axle load distribution factors, Site 245-0218 WB ......................................... 133 Table 111. Single-axle load distribution factors, Site 175-0247 EB .......................................... 134 Table 112. Single-axle load distribution factors, Site 175-0247 WB ......................................... 135 Table 113. Tandem-axle load distribution factors, Site 185-0227 NB ....................................... 136 Table 114. Tandem-axle load distribution factors, Site 185-0227 SB........................................ 137
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Table 115. Tandem-axle load distribution factors, Site 285-0243 NB ....................................... 138 Table 116. Tandem-axle load distribution factors, Site 285-0243 SB........................................ 139 Table 117. Tandem-axle load distribution factors, Site 021-w334 NB ...................................... 140 Table 118. Tandem-axle load distribution factors, Site 021-w334 SB ....................................... 141 Table 119. Tandem-axle load distribution factors, Site 127-0312 NB ....................................... 142 Table 120. Tandem-axle load distribution factors, Site 127-0312 SB........................................ 143 Table 121. Tandem-axle load distribution factors, Site 051-0387 NB ....................................... 144 Table 122. Tandem-axle load distribution factors, Site 051-0387 SB........................................ 145 Table 123. Tandem-axle load distribution factors, Site 217-0218 EB........................................ 146 Table 124. Tandem-axle load distribution factors, Site 217-0218 WB ...................................... 147 Table 125. Tandem-axle load distribution factors, Site 051-0368 EB........................................ 148 Table 126. Tandem-axle load distribution factors, Site 051-0368 WB ...................................... 149 Table 127. Tandem-axle load distribution factors, Site 143-0126 EB........................................ 150 Table 128. Tandem-axle load distribution factors, Site 143-0126 WB ...................................... 151 Table 129. Tandem-axle load distribution factors, Site 245-0218 EB........................................ 152 Table 130. Tandem-axle load distribution factors, Site 245-0218 WB ...................................... 153 Table 131. Tandem-axle load distribution factors, Site 175-0247 EB........................................ 154 Table 132. Tandem-axle load distribution factors, Site 175-0247 WB ...................................... 155 Table 133. Truck ESAL factors for flexible pavement, Site 185-0227. ..................................... 156 Table 134. Truck ESAL factors for flexible pavement, Site 285-0243. ..................................... 157 Table 135. Truck ESAL factors for flexible pavement, Site 021-w334. .................................... 157 Table 136. Truck ESAL factors for flexible pavement, Site 127-0312. ..................................... 158 Table 137. Truck ESAL factors for flexible pavement, Site 051-0387. ..................................... 158 Table 138. Truck ESAL factors for flexible pavement, Site 217-0218. ..................................... 159 Table 139. Truck ESAL factors for flexible pavement, Site 051-0368. ..................................... 159
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Table 140. Truck ESAL factors for flexible pavement, Site 143-0126. ..................................... 160 Table 141. Truck ESAL factors for flexible pavement, Site 245-0218. ..................................... 160 Table 142. Truck ESAL factors for flexible pavement, Site 175-0247. ..................................... 161 Table 143. Truck ESAL factors for rigid pavement, Site 185-0227........................................... 162 Table 144. Truck ESAL factors for rigid pavement, Site 285-0243........................................... 162 Table 145. Truck ESAL factors for rigid pavement, Site 021-w334.......................................... 163 Table 146. Truck ESAL factors for rigid pavement, Site 127-0312........................................... 163 Table 147. Truck ESAL factors for rigid pavement, Site 051-0387........................................... 164 Table 148. Truck ESAL factors for rigid pavement, Site 217-0218........................................... 164 Table 149. Truck ESAL factors for rigid pavement, Site 051-0368........................................... 165 Table 150. Truck ESAL factors for rigid pavement, Site 143-0126........................................... 165 Table 151. Truck ESAL factors for rigid pavement, Site 175-0247........................................... 166
xviii

EXECUTIVE SUMMARY
The Georgia Department of Transportation (GDOT) is currently using the 1972 AASHTO Guide for Design of Pavement Structures, known as the Pavement Design Guide, in which the damage caused by traveling vehicles in the pavement's design life is defined in terms of equivalent single axle load (ESAL). An update of ESAL factors is needed in Georgia as the existing ESAL factors utilized by GDOT were established in 1984. Since then, traffic has changed dramatically in Georgia due to population and economic growths, especially over recent years. For example, the Savannah Port expansion has inevitably increased the number of multi-trailer trucks in the region. The traffic loading data for updating ESAL factors were obtained from weigh-in-motion (WIM) sensors. WIM sensors collect information, including the number of vehicles, the gross vehicle weight (GVW), and type of the axles for calculation of ESAL factors, which can be used during the transition period prior to full adoption of the pavement mechanistic empirical (ME) design process in Georgia. The ESAL factors were determined with various reliability levels for WIM stations located on rural and urban interstate highways, respectively.
According to the results, the ESAL factors with 85 % reliability are close to the GDOT's current ESAL factors for flexible pavement design in either rural or urban interstate highways. For rigid pavement, ESALs calculated based on 90 % reliability are close to the GDOT's current ESAL Factors. Further, a site specific ESAL factors are recommended for pavement design due to the high variability among the WIM sites.
In the pavement ME design practice, truck traffic classification (TTC) groups are typically used for characterizing traffic inputs. Thus, it is important that TTC groups reflect the actual traffic patterns. In this study, customized TTC groups are developed using the WIM sensor data by
1

considering all pavement ME design traffic inputs, including vehicle class distributions (VCDs), monthly distribution factors (MDFs), hourly distribution factors (HDFs), and normalized axle load spectra (NALS). Given the high-dimensional features of traffic inputs, machine learning techniques are leveraged to (1) reduce the feature's dimension, and (2) characterize (cluster) traffic patterns in a low-dimensional space. Specifically, a category-specific principal component analysis (PCA) was used for feature reduction, followed by K-means cluster analysis. For validation purposes, the performance of two typical pavement designs was evaluated using the AASHTOWare Pavement ME software with respect to two scenarios of traffic inputs: (1) the derived cluster-based groups, and (2) the national default TTC groups. The results showed that the national default TTC groups resulted in over-design of pavement structure, especially the jointed plain concrete pavement (JPCP), in Georgia. Therefore, it is recommended that customized TTC groups derived from state-specific WIM data should be used in practice.
INDEX WORDS: Weigh-in-Motion Sensors, Pavement ME Design, Traffic Inputs, Truck Traffic Classification Groups, Principal Component Analysis, Machine Learning.
2

CHAPTER 1. INTRODUCTION
BACKGROUND AND PROBLEM STATEMENT The Georgia Department of Transportation's (GDOT) pavement design procedure is based on the 1972 American Association of State Highways and Transportation Officials (AASHTO) Interim Guide for Design of Pavement Structures (AASHTO 1972) and 1981 AASHTO rigid pavement design revisions (AASHTO 1981) for the design of pavements in Georgia. In this procedure, a required structural number (SN) for flexible pavement and concrete thickness (D) for rigid pavement are estimated based on the pavement service life, the serviceability of the pavement, and the number of equivalent loads applied, among others. The concept of equivalent single axle loads (ESALs) allows for pavement designers to convert the damage caused by loads of varying magnitudes and axle configurations to an equivalent standard 18-kip single axle load. The Federal Highway Administration (FHWA) divides vehicles into 13 different classes. When ESALs are calculated, the effects of vehicle classes 13 (i.e., motorcycles, passenger cars, and pickup trucks) are minimal and far less as compared to other heavier vehicle classes. For example, one passenger car is equivalent to only 0.0004 ESAL, while one tractor-semitrailer combination is approximately 2.0 ESALs. As such, vehicle classes 13 are commonly disregarded for pavement design.
Pavement damage is computed per axle. However, expressing the damage in terms of the average damage caused by a particular vehicle is more convenient in practice. This is referred to as a truck ESAL factor, which is simply the average number of ESAL applications per vehicle class or per group of vehicle classes. GDOT's current fixed ESAL factors for passenger vehicles, single-unit trucks, and multi-unit trucks are shown in table 1.
3

Table 1. Default ESAL factors used by GDOT.

Vehicle Classification Passenger Cars & Pickup Trucks
Single-unit Trucks Multi-unit Trucks

Flexible Pavement
0.004 0.40 1.50

Rigid Pavement
0.0004 0.50 2.68

Empirical pavement design has been based on the cumulative truck ESALs over the design period of pavement structure. Truck ESAL factors are different from distribution of trucks on different classes of highways (i.e., rural system, urban system, interstate, other principal, minor arterial, and collectors, etc.). Therefore, the determination of correct (up-to-date) ESAL factors is important for a reliable pavement design to minimize over- or under-design of pavement structures, which is directly translated to increased management and rehabilitation costs.
The last updates of truck ESAL factors in Georgia were made in 1984. Further, the ESAL factors may be subject to change in the coming years because of the Savannah Port expansion project that will produce larger/heavier containers to be transported by trucks. There is also a need to update ESAL factors using field-measured actual traffic loadings. Based on Georgia's Traffic Monitoring Guide (Wiegand 2018) published by GDOT, the GDOT Office of Transportation Data (OTD) collects weigh-in-motion (WIM) data at 14 permanent continuous count stations (CCSs) and approximately 35 portable sites located throughout Georgia. WIM technology helps to collect traffic loading related information, such as vehicle counts, axle and gross weights, vehicle classification, etc. It allows for continuous data acquisition and provides an accurate representation of actual traffic loadings on Georgia's highways. Therefore, it is important to leverage the WIM data to update GDOT's truck ESAL factors for more effective and reliable pavement design as an interim practice prior to the full adoption of the AASHTOWare Pavement ME Design in Georgia.

4

STUDY OBJECTIVES The primary objectives of this study are:
To develop a method to calculate the truck ESAL factors using data from permanent WIM sites in Georgia.
To develop updated truck ESAL factors for both flexible and rigid pavements. To update the FHWA's existing default traffic inputs in the AASHTO MEPDG Manual of
Practice (MOP) if needed and develop TTC groups to facilitate the adoption of the MEPDG in Georgia. To develop a standard operating procedure (SOP) that allows GDOT to maintain and update the ESAL factors beyond the project completion, as necessary. The SOP would include references to evaluate/update the ESAL factors and timing of a future update.
5

CHAPTER 2. LITERATURE REVIEW
ESAL FACTORS Damage to pavement caused by the wheel load of vehicles is of primary concern to pavement engineers. However, it is complicated to calculate the axle loads that a pavement section will be subject to over its design life. The historical approach is to convert damage from wheel loads of different magnitudes and repetitions to damage from an equivalent number of standard loads. The single-axle 18-kip (80 kN) load is the commonly used standard load in the U.S., and is referred to as an equivalent single axle load (ESAL). Figure 1 shows the standard 18-kip single axle, i.e., 1.0 ESAL. The development of the ESAL factor dates to the early 1960s when the AASHO Road Test was conducted. The purpose was to use a consistent loading impact unit for capturing various traffic loadings (i.e., different loads and axle configurations) with empirical equations being developed using the AASHO Road Test data.
Figure 1. Diagram. 18-kip single axle load. There are two empirical equations derived from AASHO Road Test results, one for flexible pavements and one for rigid pavements. The load equivalency factor (LEF) for each axle group type per vehicle can be computed based on those equations. The sum of the LEFs results in the ESAL factor for that specific vehicle. The parameters needed for the ESAL factor calculations are:
6

Axle weights. Axle configuration (i.e., single axle, tandem axle, tridem axle, and quad axle). Type of pavement (i.e., flexible or rigid). Structural number for flexible pavements. Slab thickness for rigid pavements. Terminal serviceability.
Terminal serviceability is defined as the lowest acceptable serviceability rating before resurfacing or reconstruction becomes necessary for a particular class of highway. In its Pavement Design Manual, GDOT's default values for terminal serviceability of flexible pavements are 2.5 for interstates and 2.0 for highways with lesser traffic volumes (GDOT 2005).
ESAL Factors for Flexible Pavements For flexible pavement design, the equations used to calculate the LEF are shown as equations 1 through 3, adopted in the 1993 AASHTO design guide (Smith and Diefenderfer 2009). Structural numbers of 4, 6, and 8 were used for LEF calculations for flexible pavements.

log

= 4.79 log(18 + 1) - 4.79 log( + ) + 4.33 log( ) + -

(1)

4.2 - = log
4.2 - 1.5
(2)

0.081( + ) .

= 0.40 + ( + 1) .

.

(3)

Where:

7

= number of applications of given axle 18 = number of standard axle passes (single 18-kip axle) = load in kips of axle group 2 = axle code (1 for single axle, 2 for tandem axles, 3 for tridem axles, and 4 for quad axles) 18 = value of when = 18 and 2 = 1 = terminal serviceability SN = structural number

ESAL Factors for Rigid Pavements The ESAL factor equations for rigid pavements are slightly different from those of flexible pavements. To determine truck ESAL factors for rigid pavements, the same WIM data were used with ESAL equations specifically for rigid pavements as shown in equations 4 through 6. Slab thicknesses of 8, 10, and 12 inches were used for rigid pavement ESAL factor calculation. The same terminal serviceability values for flexible pavements were assumed, as well.

log

= 4.62 log(18 + 1) - 4.62 log( + ) + 3.28 log( ) + -

(4)

4.5 - = log
4.5 - 1.5
(5)

3.63( + ) .

= 1.00 + ( + 1) .

.

(6)

Where: 8

= number of applications of given axle 18 = number of standard axle passes (single 18-kip axle) = load in kips of axle group 2 = axle code (1 for single axle, 2 for tandem axles, 3 for tridem axles, and 4 for quad axles) 18 = value of when = 18 and 2 = 1 = terminal serviceability D = slab thickness in inches Site-Specific Truck ESAL Factors For design purposes, aggregate LEFs, such as state or regional average LEFs, are used for estimating cumulative ESALs over the design period. For example, the Virginia Department of Transportation (VDOT) averages the results to develop site-specific truck ESAL factors by WIM site, pavement type, and vehicle classification. The resulting ESAL factors are simply weighted averages based on their respective vehicle counts (Smith and Diefenderfer 2009). FHWA defined 13 vehicle classes (figure 2), ranging from motorcycles and passenger cars to multi-trailer trucks. However, ESALs are only computed for vehicle classes 4 through 13, as the impacts of classes 1 through 3 are negligible from the pavement design standpoint. The North Carolina Department of Transportation (NCDOT) also uses site-specific traffic, environment, and location information to analyze the damage factor (DF), which is defined as the ratio of the fatigue damage caused by an axle typeload combination to the fatigue damage caused by a standard 18-kip ESAL (Stone et al. 2011).
9

Figure 2. Chart. FHWA vehicle classification system. ESAL FACTORS USED BY STATE DOTS Georgia DOT The current truck ESAL factors in Georgia were established in 1984. Since then, traffic patterns have changed dramatically, especially over recent years, due to continuing growth in the state's population and economy. In addition, the Savannah Port expansion project will produce much larger/heavier containers to be transported by trucks within the state. As such, there is an urgent need to update the ESAL factors to reflect actual traffic loadings. Figure 3 shows the historical ESAL factors used by GDOT from 1964 through 1984.
10

Figure 3. Line graph. Truck ESAL factors history for Georgia. Virginia DOT The Virginia DOT currently uses the 1993 AASHTO Guide for Design of Pavement Structures (AASHTO 1993). VDOT's pavement design procedure divides truck traffic into two categories: single-unit trucks and multi-unit (combination) trucks. VDOT has also divided the Virginia roads into two categories: interstate and primary highways. Table 2 and table 3 show the truck ESAL factors in Virginia derived from WIM data from June 2007 through May 2008 using the MATLAB1 programming language (Smith and Diefenderfer 2009).
1 MATLAB is short for "matrix laboratory" and is a registered trademark of MathWorks.
11

Table 2. Virginia's average truck ESAL factors for flexible pavement by vehicle classification and administrative roadway classification (Smith and Diefenderfer 2009).
Table 3. Virginia's average truck ESAL factors for rigid pavement by vehicle classification and administrative roadway classification (Smith and Diefenderfer 2009).
12

North Carolina DOT NCDOT operates 44 WIM sites, including 19 long-term pavement performance (LTPP) stations (Stone et al. 2011). Table 4 represents NCDOT's truck loading factors of flexible and rigid pavements by roadway classifications. Truck weights from WIM data are used to determine average loadings for two different truck classifications: single-unit single-axle trucks (i.e., duals) and combinations of multiple-unit and multiple-axle trucks (i.e., truck, tractor with semi trailer; TTST). Like other DOTs' policies, loadings from automobiles are negligible (NCDOT 2019).
Table 4. Average truck ESAL factors by vehicle classification and administrative roadway classification (NCDOT 2019).
Comparison of ESAL Factors Figure 4 and figure 5 represent the comparison of GDOT's current ESAL factors with North Carolina and Virginia DOTs' ESAL factors for rural and urban interstate highways for flexible pavement design. The same comparison is provided for rigid pavements in figure 6 and
13

figure 7. As seen, GDOT's default ESAL factors are relatively higher than these other state DOTs' ESALs for either rigid or flexible pavement design.
Figure 4. Bar graph. DOTs' ESAL factor comparison results on rural interstate highways for flexible pavement.
Figure 5. Bar graph. DOTs' ESAL factor comparison results on urban interstate highways for flexible pavement. 14

Figure 6. Bar graph. DOTs' ESAL factor comparison results on rural interstate highways for rigid pavement.
Figure 7. Bar graph. DOTs' ESAL factor comparison results on urban interstate highways for rigid pavement.
As described previously, ESAL factors are weighted averages for each WIM station based on their respective vehicle counts. Many DOTs have developed average ESAL factors for each truck class based on measurements of trucks throughout the state. Class 9 vehicles are the most dominant vehicles in many state DOTs (Selezneva and Hallenbeck 2013, Smith and Diefenderfer 2009). The
15

dominant vehicle class, gross vehicle weight (GVW) of trucks, and axle loads of each axle group are determining factors that differentiate ESAL factors of different states. In addition to the traffic data, the design guide, which is the basis for pavement design, has an essential role in the resultant ESAL factors.

DIFFERENCES BETWEEN 1972 AND 1993 AASHTO PAVEMENT DESIGN GUIDE

The 1972 AASHTO Interim Guide for Design of Pavement Structures does not explicitly consider the reliability factor in the pavement design process. Equation 7 shows the way in which the structural number is calculated by giving an initial estimate and allowing the equation solver to iterate for the solution. The important factors in the 1972 design guide are traffic, terminal serviceability, soil support value, and regional factor.

4.2 -

log( ) = 9.36 log( + 1) - 0.2 + log 4.2 - 1.5 + 0.372(

1 - 3.0) + log

1094

0.4 + ( + 1) .

(7)

Design reliability must account for uncertainties in traffic loading, environmental conditions, and construction materials. The 1993 AASHTO design method accounts for these uncertainties by incorporating a reliability level, R, to provide a factor of safety into the pavement design over its design life. The factors affecting the pavement design in the 1993 design guide are shown in equation 8.

4.2 - p

log(W ) = Z S + 9.36 log(SN + 1) - 0.2 + log 4.2 - 1.5 + 2.32 log(M ) - 8.07

1094

0.4

+ (SN

+

1)

.

(8)

The design inputs for the 1993 AASHTO design guide include: 16

Time constraints. Reliability (corresponding to in equation 8). Standard deviation. Traffic (ESALs). Materials (MR). Design serviceability loss. Design output is required SN.

RELIABILITY
Reliability reflects the inevitable uncertainty and variability in the design inputs and the importance of the project. It is important to incorporate some degree of certainty into the design process to ensure that the structure will perform satisfactorily over the intended design period. The levels of reliability recommended by AASHTO for various classes of roads are summarized in table 5.

Table 5. Suggested levels of reliability for various functional classifications (AASHTO 1993).

Functional Classification
Interstate and Other Freeways Principal Arterials Collectors Local

Recommended Level of Reliability

Urban 8599.9 8099 8095 5080

Rural 8099.9 7595 7595 5080

The reliability level is reflected by ZR, assuming traffic and other uncertainties are jointly captured by a normal distribution. Some commonly used levels of reliability are summarized in table 6. The AASHTO design equations also require specification of the overall standard deviation, S0. For

17

flexible pavements, values for S0 typically range between 0.35 and 0.50. In this report, the value of 0.40 was selected for both flexible and rigid pavement (Christopher et al. 2006).

Table 6. Standard normal deviates for various levels of reliability.

Reliability (%)
50 60 70 75 80 85 90 91 92

Standard Normal Deviate (ZR) 0.000 -0.253 -0.524 -0.674 -0.841 -1.037 -1.282 -1.340 -1.405

Reliability (%)
93 94 95 96 97 98 99 99.9 99.99

Standard Normal Deviate (ZR) -1.476 -1.555 -1.645 -1.751 -1.881 -2.054 -2.327 -3.090 -3.750

The implementation of the reliability concept in GDOT's truck ESAL factors is discussed in detail in chapter 5.

18

CHAPTER 3. WEIGH IN MOTION
BACKGROUND The weigh-in-motion technology, using sensors embedded in pavement, has enabled continuous collection of high-resolution vehicle class and axle weight data, such as gross vehicle weights, axle configurations and weights, axle spacings, vehicle classifications, and speeds. The WIM data are used for various purposes, including the design of pavements or bridges, highway planning, motor vehicle enforcement, and legislative/regulatory studies (FHWA 2018). The main components of WIM systems include:
WIM sensor embedded in the roadway surface or under a bridge deck to detect, weigh, and classify vehicles. A sensor array is the combination of WIM sensor and loop detectors within a weighing lane.
Electronics to control system functions, process sensor outputs, and provide recorded information for display and storage.
Infrastructure, including conduit, bore, cabinet, poles, and junction boxes. Support devices to power the WIM electronics and communication devices to transmitthe
collected data to a remote server. Software installed in the WIM electronics to process sensor measurements, analyze,
format, and store collected data (FHWA 2018).
19

TYPES OF WIM SENSORS Several types of in-road WIM sensors are available, but the most frequently used types include bending plate, load cell, quartz piezo, polymer piezo, and the strain gauge strip sensor. Wide sensors, such as bending plate and load cell, provide the opportunity for the tire to rest fully on the sensor, while other sensors, referred to as narrow or strip sensors, meet only a part of the tire footprint as a vehicle moves over them. The available sensors have a broad range in accuracy and cost that should be considered during the sensor selection process. If high-accuracy weight data collection over a long period of time is required, then either a load cell or bending plate sensor would be the preferred solution. In terms of life cycle costs, the bending plate and load cell sensors are more cost effective in comparison with piezo sensors with high data quality if properly maintained/calibrated. The polymer piezo sensors are sensitive to temperature fluctuations and pavement stiffness due to seasonal changes; thus, these sensors must be calibrated every 6 to 12 months to keep accuracy in weight measurements.
In summary, for projects with a typical life expectancy of 8 to 10 years, load cells or bending plate sensors would be preferred. Quartz piezo or strain gauge strip sensors typically have shorter lifespans of 3 to 5 years (FHWA 2018). The characteristics of each WIM sensor type are discussed in the following subsections.
Bending Plate Strain gauges are utilized under the surface of bending plates to collect loading data. The bending plate WIM sensor illustrated in figure 8 is typically 6 ft long, 20 inches wide, and 1 inch thick. Figure 9 shows a bending plate within the pavement structure. The bending plate system measures
20

the strain on the plate roughly 2,000 times per second, as axles pass over the plate at highway speeds, and then calculates the load required to produce that level of strain. The bending plate is one of the most accurate WIM sensors, and it is not sensitive to temperature changes and speed variation. However, it is recommended to be only used in portland cement concrete (PCC) pavements. In asphalt concrete (AC) pavements, the pavement around the frame is prone to cracking, which makes the frame loose and creates a hazard for the traveling public.
Figure 8. Illustration. Bending plate sensor (FHWA 2018).
Figure 9. Photo. Bending plate installation (FHWA 2018). Load Cell A load cell sensor system includes two weighing platforms, each with a surface size of 6 ft by 3 ft, that can cover a 12-ft traffic lane when placed adjacent to each other. In this scaling system, mechanical or hydraulic transducers measure the applied forces, which are analyzed by system
21

electronics to calculate axle loads. Figure 10 and figure 11 show a load cell sensor and its installment in the pavement structure, respectively. Among the commercially available WIM sensors, the load cell is the most accurate sensor at highway speeds (FHWA 2018). However, the sensors need to be calibrated every 12 to 24 months. Time- consuming installment and relatively higher cost are the major disadvantages of load cell sensors.
Figure 10. Illustration. Load cell sensor (FHWA 2018).
Figure 11. Illustration. Load cell installation (FHWA 2018). Polymer Piezo Sensor A polymer piezo sensor consists of a copper strand surrounded by a piezoelectric polymer material covered by a brass sheath, as shown in figure 12. These sensors can be ordered in different cable lengths. As vehicles pass over the WIM system, the changes in voltage (electrical charge) caused
22

by exerted pressure are detected by piezo sensors. Thus, the weight due to the passing tire/axle group can be determined--the heavier the vehicle, the larger the charge. The polymer piezo WIM systems are the least expensive, most durable, and easiest to install. Therefore, these sensors are widely used for vehicle classification purposes. However, due to their sensitivity to temperature and pavement stiffness, they are less accurate than other WIM sensor types.
Figure 12. Photo. Polymer piezo sensor (FHWA 2018). Quartz Piezo Sensor Quartz crystal technology has been utilized in the quartz piezo WIM sensor. As vehicles pass over the sensor, vertical forces are applied and distributed through the quartz crystals in the system, producing an electrical charge proportional to the applied vertical forces. These sensors can be installed in either AC or PCC pavements; however, the installation in PCC pavement structure is much more durable. The main advantage of the quartz sensors is that they are less sensitive to temperature changes, thus making them more precise and subsequently more expensive when compared to other piezo-style sensors. The disadvantage of this sensor type is sensitivity to the
23

structural strength of the pavement due to material softening in high temperatures or high soil moisture content. The quartz piezo sensor is approximately 2 inches wide, 2 inches thick, and 1.5 or 2 m long, and it can be varied in length to provide half-lane or full-lane width coverage, as shown in figure 13. Figure 14 illustrates a quartz piezo installation embedded in a pavement cross section.
Figure 13. Illustration. Quartz piezo sensor (FHWA 2018).
Figure 14. Illustration. Quartz piezo installation depiction (FHWA 2018). Strain Gauge Strip Sensor Figure 15 illustrates a strain gauge strip sensor. This type of WIM sensor is based on the strain gauge load cell technology in which the vertical strains are measured as vehicles pass over the system. The induced electronic changes in the strain gauge load cells are converted into dynamic
24

loads. Each strip sensor is approximately 3 inches wide and 3 inches tall, and weighs 45 to 65 lb, depending on the length of the sensor. These sensors can be installed in either AC or PCC pavement. Because of the sensor's design, which is slightly larger, the sensor is less sensitive to the structure of the pavement compared to quartz piezo sensors. The strain gauge strip sensors are less expensive than quartz piezo sensors and less sensitive to temperature changes as compared to polymer piezo sensors.
Figure 15. Illustration. Strain gauge strip sensor (FHWA 2018). WIM Sensors in Georgia In Georgia, the vendor uses two WIM sensor models in the state's current WIM system: quartz and bending plate sensors. Lanes instrumented with quartz sensors provide information on both vehicle weight and class. Although bending plate sensors also record vehicle class and weight data simultaneously, the vendor considers the weight data from quartz sensors to be more accurate and reliable (Chorzepa et al. 2020).
MISSING AND ERRONEOUS DATA Generally, there are two quality issues with WIM data: missing values and erroneous data. Power outages or sensor malfunctions can cause missing values (Wei and Fricker 2003). A variety of other factors may affect WIM data quality, including environmental changes, pavement conditions, lack of calibration, and the type of WIM technology. Moreover, drivers' behavior, such as accelerating, decelerating, and weaving, also impact the data quality depending on the sensor
25

technologies (Wei and Fricker 2003, Stone et al. 2011). In addressing the data quality issues in practice, state departments of transportation (DOTs) have developed standard quality control (QC) checks to ensure the quality of the data before releasing them for planning or design practice. To ensure the quality of WIM data used in the analysis in this project, the research team implemented a customized QC procedure derived from the QC policies currently used by GDOT (Wiegand 2018) and NCDOT (Stone et al. 2011).
QUALITY CONTROL CHECKS
Based on previous studies, only 1525 percent of the collected WIM data are considered good quality data due to lack of skilled staffing, resources, and support software. Thus, both the Traffic Monitoring Guide (FHWA 2016) and AASHTO Guidelines for Traffic Data Programs (AASHTO 2009) emphasize the QC requirements in traffic monitoring programs. The FHWA Long-Term Pavement Performance Program developed mandatory verification QC checks and software on the collected traffic data in the field before merging with the database. Moreover, state-specific traffic data QC rules (e.g., New Mexico, North Carolina, and Georgia) have been developed to ensure accurate and reliable data are being collected for further analysis (Li et al. 2018). In the following subsections, the QC rules of three state DOTs are discussed. Through the QC process, a portion of raw WIM data are eliminated, which indicates the quality of WIM data. Detailed QC process for Georgia WIM data is discussed in chapter 4.
New Mexico DOT The New Mexico Department of Transportation (NMDOT) used Microsoft's Visual Basic for Applications (VBA) language to develop a program for implementing QC rules to their raw WIM data. The first set of rules is to check the time and location of each vehicle. The remaining rules check the consistency of the vehicle class, GVW, number of axles, weights, and their spacings.
26

Table 7 shows the NMDOT multiple rules in the order applied to check the quality of the WIM data (Brogan et al. 2011).

Order 1 2 3 4 5 6 7 8 9
10
11
12
13 14 15 16
17

Table 7. NMDOT QC rule list for WIM data.

Rule

Rule Description

Year is correct and unique.

If year 09, then error.

Month is correct and unique.

If month 01, then error (e.g., for January).

Day is correct.

If day 131, then error.

Hour is correct.

If hour 023, then error.

WIM station ID is correct.

If station code 21020, then error.

Direction is correct. Lane number is correct. Vehicle class is correct.

If direction 1 or 5, then error. If lane number 14, then error. If vehicle class 413, then error.

Number of axles consistent with the number of axle spacings.
Number of axles consistent with the number of axle weights.

If number of axles number of axle spacings + 1, then error.
If number of axles number of axle weights, then error.

GVW consistent with the sum of axle weights.

If sum of axle weights total weight, then error.

Number of axles consistent with the vehicle If number of axles range of axles for that vehicle

class.

class, then error.

Sum of axle spacings consistent with maximum wheelbase.
Axle weights within acceptable range.

If sum of axle spaces > 29.93 m, then error. If 200 kg < axle weight < 20,003.4 kg, then ok.

Axle spacings within acceptable range.

If 0.6 m < axle spacings < 15 m, then ok.

Visual review of the GVW frequency distribution for each class to check consistency with the peaks for loaded and unloaded vehicles.

Visual interpretation of the front steering axle weight frequency distribution for each class to check whether the majority of axles fall within the proper range.

North Carolina DOT The NCDOT WIM QC process is a combination of automated and manually applied procedures: a series of class and weight data checks. The priority QC checks are on weights, as weight measurements are more likely to be erroneous than vehicle class data. Table 8 and table 9 present the class and weight QC checks applied by the NCDOT WIM QC database (Stone et al. 2011).

27

Table 8. NCDOT QC rule list for class data.

Order ID

Description

Criteria

Any field with a null 1 C_NULL
value

Field Value = Null

2

C8

Invalid month

MONTH (112)

3

C10

Invalid hour

HOUR (023)

Total lane volume

4

C1

exceeds max. limit

5

C11

Invalid FIPS1 code

TOTAL_VOL > 3000 STATE_CD 37

STATION_ID Expected station

6

C4

Invalid station ID

identifier

Invalid direction for

7

C6

DRCTN_CD Valid values for station

station

Invalid lane number

TRVL_LN_NBR Valid values for

8

C5

for station

station

9

C7

Invalid year

YEAR Valid year for date range captured

10

C9

Invalid day

DAY Valid date for the MONTH

A full day of data is

11

C3 not available for a day

Manual audit of hours and days

for all lanes

Class volume exceeds

12

C2

CLS_CNT_## = TOTAL_VOL

maximum limit

1 AM total lane

HOUR (1) TOTAL_VOL > HOUR (13)

13

C13 volume exceeds 1 PM

TOTAL_VOL

total lane volume

Static total lane

HOUR (X) TOTAL_VOL = HOUR

14

C14

volume for four

(X+1, +2, +3) TOTAL_VOL

consecutive hours

Review avg. DOW2

A pattern deviates significantly from

15

CP1 volumes by month for

other months

unusual patterns

Review class

A pattern deviates significantly from

16

CP2 distribution by month

for unusual patterns

other months

17

CP3

Review class % distributions for unusual patterns

The summary data exhibits an unusual pattern

1 FIPS is the two-digit Federal Information Processing Standard state code.

2 DOW stands for day of week.

Tools QC Set 3 QC Set 3 QC Set 3 QC Set 4 Forms 2 Forms 2 Forms 2 Forms 2 Forms 2 Forms 2 Forms 2
Forms 3
Forms 3
Forms 3
Plots
Plots
Plots

28

Order 1 2 3 4 5 6 7 8 9 10
11
12
13
14 15 16 17
18
19

Table 9. NCDOT QC rule list for weight data.

ID W_NULL
W12 W10 W16 W14 W6 W8 W7 W9 W11
W13
W1
W2
W3 W4 W5 W17
WP1
WP2

Description
Any field with a null value
Invalid hour
Invalid month Invalid vehicle class
code Invalid FIPS code
Invalid station ID
Invalid direction for station
Invalid lane number for station
Invalid year
Invalid day Hour without any weight records. A full day of data may not be available for all
lanes Axle count inconsistent with number of axle spacings Axle count inconsistent with number of axle
weights GVW inconsistent with sum of axle
weights Axle weight out of acceptable range Axle spacing out of acceptable range Sum of axle spacings exceeds maximum
wheelbase Review average DOW volumes by month for unusual
patterns Review GVW plots by class by month for
unusual patterns

Criteria Field Value = Null
HOUR (023) MONTH (112) VHCL_CLASS (413) STATE_CD 37 STATION_ID Expected station
identifier DRCTN_CD Valid values for station
TRVL_LN_NBR Valid values for station
YEAR Valid year for date range captured
DAY Valid date for the MONTH
Manual audit of hours without weight records
AXLE_COUNT (# of spacings + 1)
AXLE_COUNT # of axle weights
TOTAL_WGHT Sum of axle weights
441 lb (200 kg) < (X)_WGHT < 44,100 lb (20,003.4 kg)
1.97 ft (0.6 m) < (X)_(Y)_SPACING < 49.2 ft (15 m)
Sum of axle spacings > 98.2 ft (29.93 m)
A pattern deviates significantly from other months
A pattern deviates significantly from other months

Tools QC Set 1 QC Set 1 QC Set 1 QC Set 1 Forms 1 Forms 1 Forms 1 Forms 1 Forms 1 Forms 1
Forms 1
QC Set 2
QC Set 2
QC Set 2 QC Set 2 QC Set 2 QC Set 2
Plots
Plots

29

Georgia DOT The GDOT Office of Transportation Data currently has a comprehensive quality control and quality assurance (QA) process in place. Table 10 shows GDOT's quality control rules for WIM sites (Wiegand 2018).

Table 10. GDOT QC rules list for WIM sites.

Quality Control Rule Error Ratio
Minimum Class Hours No Truck Data No Trucks Lane
Ratio of Class 1 to Class 2
Ratio of Class 13 to Class 9 Ratio of Long Class to Short
Class Trucks Last Year Zero Long Class Zero Short Class Minimum Hours
No Data Volume Last Year
Volume Split
Volume Step Volume Step Lane Zero Hours All Day Zero Hours During Day (Continued on next page)

Description
The system will reject the day(s) that have vehicles in class 15 (the error bin) greater than X percent of the total volume. The system will reject data that do not provide a complete 24 hours of truck data.
The system will reject the day(s) if no truck data exist for the day.
The system will reject the data if there is no truck traffic in one lane for the day.
The system will flag any day(s) for which the volume in vehicle class 1 (motorcycles) exceeds the volume in vehicle
class 2 (cars). The system will flag any day(s) for which the volume in vehicle class 13 exceeds the volume in vehicle class 9. The system will flag any day(s) for which the total of the volumes in vehicle classes, 11, 12, and 13 (long class) exceeds the volumes in vehicle classes 8, 9, and 10 (short class). The system will reject any daily truck traffic volumes that are
substantially different from the previous year. The system will reject day(s) for which the long truck classes
have a zero volume. The system will reject day(s) for which the short truck classes
have a zero volume. The system will reject any day that does not have data for
every hour The system will reject a day for which there are no data. The system will reject any daily traffic volumes that are
substantially different from the previous year. The system will flag the entire set of counts if volume in one direction is over X percent of the total volume. This check is
not applied to nondirectional data. The system will reject the day(s) that show a sudden dramatic
change in hourly volumes. The system will flag the day(s) that have a sudden dramatic
change in hourly lane volumes. The system will reject any day that has consecutive zero
volumes for the entire day. The system will reject any day that has consecutive zero
volumes at any time during the day.

Data Type Class Class Class Class
Class
Class
Class
Class Class Class Volume Volume Volume
Volume
Volume Volume Volume Volume

30

Quality Control Rule Zero Hours During the Night Class 9 Average Steer Weight
Class 9 BC Spacing Maximum Axle Count Maximum Wheelbase Minimum Axle Count

Table 10. (Continued)
Description
The system will reject any day for which there are consecutive zero volumes at any time during the night.
The system will reject any day for which the Class 9 average steer weight is outside the parameters.
The system will reject any day for which the Class 9 average BC axle spacing is outside the parameters.
The system will reject any day for which the ratio of vehicles to axles is more than X.
The system will reject any day for which the wheelbase is more than X.
The system will reject any day for which the ratio of vehicles to axles is less than X.

Data Type Volume WIM WIM WIM WIM WIM

31

CHAPTER 4. DATA ACQUISITION PROPERTIES OF GEORGIA WIM SITES For this study, the researchers obtained data from 10 active WIM stations in the state of Georgia. Each station covers both directions of traffic, and WIM data were collected by direction. The locations of these WIM stations are shown in figure 16. Given the potentially distinct directional truck traffic patterns, each direction was treated as a separate WIM site (Li et al. 2017), resulting in a total of 20 WIM sites for this study.
Figure 16. Map. Active WIM stations with available data in Georgia. Only vehicle classes 4 through 13 were considered in this study since the impact of classes 1 through 3 is negligible from a pavement design standpoint. The traffic data were retrieved and
32

compiled for vehicle classes 4 through 13 from the 10 WIM stations (20 WIM sites). Table 11 summarizes the properties of the WIM sites in Georgia.
INACTIVE WIM STATIONS GDOT uses the Traffic Analysis and Data Application (TADA), a web application, to disseminate traffic data collected from the Georgia Traffic Monitoring Program. The application utilizes a dynamic mapping interface that allows users to access data from the map in a variety of reports, graphs, and data formats. Historical data from two inactive WIM stations, 245-0218 and 143-0126, were evaluated. The 245-0218 WIM data (figure 17) are erroneous and incomplete; thus, they were excluded from the ESAL calculations. Figure 17 shows a screenshot of TADA in which the details of one of the inactive WIM stations are characterized.
WIM DATA ANALYSIS Different Python codes were developed to analyze the raw WIM data. First, the vehicle class distribution of directional WIM stations was visualized. Figure 18 and figure 19 show the vehicle class distribution by month for north- and southbound directions of WIM station 185-0227, respectively. The vehicle class distributions for the other nine WIM stations are presented in appendix A. As seen in the figures, the class 9 vehicle is the most dominant truck for all the WIM stations throughout the year. Moreover, figure 20 shows the GVW frequency distribution of WIM station 185-0227. Based on the figure, the first few weight ranges were considered outliers and were removed in the QC process to retrieve the real weight distribution of vehicles.
33

Pavement No.
Type

1

Rigid

2

Rigid

3

Rigid

4

Rigid

5 Flexible

6

Rigid

7

Rigid

8

Rigid

9 Flexible

10 Flexible

Site Name 000000217334 000000510368 000001270312
000001430126 000001850227 000002450218 000000510387 000001750247 000002170218 000002850243

Table 11. Properties of WIM Sites in Georgia.

Site ID 021-w334 051-0368 127-0312
143-0126 185-0227 245-0218 051-0387 175-0247 217-0218 285-0243

Description

Latitude Longitude

I-75 N of I-475 Split Dr. Macon
I-16 East of Dean Forest exit
I-95 btwn SR 27 & Golden Isles Parkway SR
25 Spur M I-20 Alabama state line &
SR 100 Veterans Mem Hwy
I-75/SR 401 @ FLA SL, Lake Park, Lowndes Co
I-20 E of I-520 @ SC state line, Augusta
I-95, 2 mi N of SR-21 (Augusta Rd) @ SC state
line I-16, 1.4 miles East of
SR-338 MP 43 I-20 WEST OF SR 11
BTWN SR 142 I-185 N of SR 18 @ Dennis Smith Rd, Pine
Mtn

32.75959 32.06899 31.23438
33.68077 30.62671 33.52746 32.2002 32.51342 33.61157 32.87801

-83.68055 -81.19281 -81.5093
-85.30221 -83.17308 -82.01906 -81.18769 -83.0697 -83.76155 -84.96221

Functional Class
Interstate (Urban) Interstate (Urban)
Interstate (Urban)
Interstate (Rural)
Interstate (Rural) Interstate (Urban)
Interstate (Urban)
Interstate (Rural) Interstate (Urban)
Interstate (Rural)

Lanes County

1N,

1S.

Bibb

2E, 2W Chatham

4N, Glynn
4S

City Macon Savannah Brunswick

2E, Haralson Tallapoosa
2W

3S, 3N Lowndes Lake Park 2E, 4W Richmond Augusta

3N, 3S Chatham Savannah

2E, 2W Laurens

Dublin

2E, 2W Newton Covington

2N,

Pine

Troup

2S

Mountain

34

Figure 17. Screenshot. Inactive WIM station in TADA.2
Figure 18. Line graph. Vehicle class distribution by month, Site 185-0227 NB.
2 https://gdottrafficdata.drakewell.com/publicmultinodemap.asp
35

Figure 19. Line graph. Vehicle class distribution by month, Site 185-0227 SB.
Figure 20. Line graph. GVW frequency distribution, Site 185-0227. 36

WIM DATA QUALITY CONTROL As discussed in Quality Control Checks in chapter 3, the NCDOT and GDOT QC criteria were applied to raw WIM data to process and generate truck ESAL factors and AASHTOWare Pavement ME Design traffic inputs. WIM DATA AFTER QC CHECKS Figure 21 and figure 22 show the monthly distribution factors of the directional WIM stations after applying the QC criteria. Furthermore, figure 23 illustrates the GVW frequency distribution for site 185-0227 after QC checks. The results for individual WIM stations are presented in appendix B. As shown in the figures in appendix B, portions of yearly WIM data were eliminated after the QC process, which indicates the quality of the data. Based on the QC process results, it is recommended to continuously monitor the WIM data quality and calibrate the WIM sensors if necessary to obtain good quality yearly data in the future.
Figure 21. Line graph. Monthly distribution factors, Site 185-0227 NB. 37

Figure 22. Line graph. Monthly distribution factors, Site 185-0227 SB.
Figure 23. Line graph. GVW frequency distribution, Site 185-0227. DATA ACQUISITION The compiled WIM data include five feature categories consistent with the AASHTOWare Pavement ME Design traffic inputs: (1) vehicle class distribution (VCD) factors, (2) monthly distribution factors (MDFs) for each
38

vehicle class, (3) hourly distribution factors (HDFs) for each hour of the day, (4) normalized axle load spectra (NALS) for single-axle loads across vehicle classes and weight bins, and (5) NALS for tandem-axle loads across vehicle classes and weight bins. In compiling WIM data in AASHTOWare Pavement ME Design traffic input format, the tridem- and quad-axle load spectra were generally excluded from the analysis since pavement designs are less sensitive to tridem and quad axles due to their low impact and representation as compared to single- and tandem-axle load applications (Selezneva et al. 2016). As a result, a total of 564 design-related traffic features were obtained for each WIM site, including 10 VCD features, 120 MDF features, 24 HDF features, 230 NALS single-axle features, and 180 NALS tandem-axle features. These feature categories for each of the 20 WIM sites are available in appendix C.
39

CHAPTER 5. DEVELOPMENT OF GDOT TRUCK ESAL FACTORS
GEORGIA ESAL FACTORS Based on the instructions presented in chapter 2, ESAL factors were calculated using the available WIM data. The results are presented in appendix D. ESAL Factors Variation vs. Structural Number In the case of flexible pavement, ESAL factors were calculated assuming different structural numbers to determine if structural number variation had any effect on the resultant ESAL factors. For this purpose, structural numbers of 4, 5, 6, 7, and 8 were considered, and then ESAL factors were computed separately. The results show that there is no significant difference between ESAL factors when different structural numbers are considered. Figure 24 illustrates different ESAL factors for FHWA vehicle classes for WIM station 185-0227. As shown, the effect of SN variation is negligible. The same result was seen for the other WIM stations. ESAL Factors Variation vs. Slab Thickness The same procedure was repeated for rigid pavement design by considering different slab thicknesses before ESAL calculation. For this purpose, slab thicknesses of 8, 10, and 12 inches were assumed. The results show that there is no considerable difference when choosing different slab thickness for rigid pavement design in Georgia. Figure 25 shows the effect of slab thickness variation on the rigid pavement ESAL factors.
40

Figure 24. Line graph. ESAL factors with different SNs vs. vehicle classes, Site 185-0227.
Figure 25. Line graph. ESAL factors with different slab thicknesses vs. vehicle classes, Site 185-0227. 41

ESAL Factors Variation vs. Vehicle Classes Figure 26 shows the average ESAL factors of site 185-0227, which were calculated assuming various structural numbers for different vehicle classes. The results indicate the ESAL factor varies across vehicle classes while remaining relatively constant across SNs.
Figure 26. Line graph. ESAL factors comparison with different SNs for FHWA vehicle classes, Site 185-0227.
Figure 27 shows similar patterns for rigid pavement design where the ESAL factor varies across vehicle classes but remains nearly constant over different slab thicknesses.
42

Figure 27. Line graph. ESAL factors comparison with different slab thicknesses for FHWA vehicle classes, Site 185-0227.
RELIABILITY INCLUSION IN GEORGIA ESAL FACTORS
Table 12 shows the default ESAL factors that GDOT is currently using. It should be mentioned that these values were derived based on the 1972 AASHTO design guide in which reliability was not explicitly considered. To account for uncertainty, ESAL factors were calculated in this study using the formulas in the 1993 AASHTO design guide.

Table 12. GDOT's default truck ESAL factors.

Vehicle Classification Passenger Cars & Pickup Trucks
Single-unit Trucks Multi-unit Trucks

Flexible Pavement
0.004 0.40 1.50

Rigid Pavement
0.0004 0.50 2.68

For this purpose, the relationship between the 1972 and 1993 design guides was determined and then the new ESAL factors were calculated with specified levels of reliability. In the 1993 design
43

guide, the resilient modulus term has been replaced by the soil support value and regional factor in the 1972 design guide. The remaining part of the equation retains the basic approach from the 1972 interim guide. Therefore, the relationship between the two traffic (ESAL) terms in the 1972 and 1993 design guides is related by equation 9:
= 10 (9)

Where,

18

is the traffic (weighted ESAL factor) from the available WIM data, and


18

is

the

estimated traffic (ESAL) after considering the reliability term.

Table 13 through table 15 show the new ESAL factors for flexible pavement design with different structural numbers by considering various levels of reliability. Similarly, table 16 through table 18 present the ESAL factors for rigid pavement design with different slab thicknesses and reliability levels.

Site 051-0368, which is close to the Savannah port, is one of the high traffic volume WIM stations. However, this WIM site was excluded from the computed averages in Table 13 through Table 18 due to erroneous/missing 2018 and 2019 WIM data.

44

Table 13. Truck ESAL factors with different reliability levels for flexible pavement design, structural number 4.

Reliability Level

Site ID

75%

80%

85%

90%

95%

ESAL Factors for Single-unit (SU) and Multi-unit (MU) Trucks

SU

MU SU

MU SU

MU

SU

MU SU

MU

185-0227

0.4 1.50 0.45 1.70 0.54 2.02 0.67 2.53 0.94 3.54

285-0243

0.36 1.35 0.40 1.53 0.49 1.83 0.61 2.29 0.85 3.21

021-w334

0.29 1.08 0.32 1.23 0.39 1.47 0.49 1.84 0.69 2.58

127-0312

0.34 1.29 0.39 1.46 0.46 1.75 0.58 2.19 0.81 3.06

051-0387

0.42 1.56 0.47 1.77 0.56 2.11 0.70 2.65 0.98 3.70

217-0218

0.47 1.77 0.53 2.00 0.64 2.40 0.80 3.00 1.12 4.20

051-0368*

0.86 3.23 0.97 3.66 1.17 4.39 1.46 5.50 2.05 7.68

143-0126

0.29 1.09 0.33 1.25 0.40 1.49 0.50 1.86 0.70 2.60

175-0247

0.30 1.14 0.34 1.30 0.41 1.55 0.52 1.94 0.72 2.71

Average

0.36 1.35 0.40 1.53 0.49 1.83 0.61 2.29 0.85 3.20

*This site has been excluded from the average.

Note: Site 245-0218 also has been excluded due to erroneous and incomplete data.

Table 14. Truck ESAL factors with different reliability levels for flexible pavement design, structural number 6.

Reliability Level

Site ID

75%

80%

85%

90%

ESAL Factors for Single-unit and Multi-unit Trucks

SU

MU SU

MU SU

MU

SU

MU

185-0227

0.35 1.33 0.40 1.51 0.48 1.81 0.60 2.27

285-0243

0.32 1.21 0.37 1.38 0.44 1.65 0.55 2.07

021-w334

0.25 0.97 0.29 1.09 0.35 1.31 0.44 1.64

127-0312

0.30 1.15 0.35 1.31 0.42 1.57 0.52 1.96

051-0387

0.37 1.41 0.42 1.60 0.51 1.90 0.64 2.39

217-0218

0.42 1.58 0.48 1.79 0.57 2.14 0.72 2.68

051-0368*

0.83 3.13 0.95 3.55 1.13 4.25 1.42 5.32

143-0126

0.26 0.97 0.29 1.09 0.35 1.30 0.44 1.64

175-0247

0.26 1.00 0.31 1.17 0.37 1.40 0.47 1.75

Average

0.32 1.20 0.36 1.37 0.44 1.64 0.55 2.05

*This site has been excluded from the average.

Note: Site 245-0218 also has been excluded due to erroneous and incomplete data.

95%

SU

MU

0.85 3.18

0.77 2.89

0.61 2.30

0.73 2.74

0.89 3.34

1.00 3.75

1.98 7.43

0.61 2.29

0.65 2.45

0.76 2.87

45

Table 15. Truck ESAL factors with different reliability levels for flexible pavement design, structural number 8.

Reliability Level

Site ID

75%

80%

85%

90%

ESAL Factors for Single-unit and Multi-unit Trucks

SU

MU SU

MU SU

MU

SU

MU

185-0227

0.35 1.30 0.39 1.47 0.47 1.76 0.59 2.20

285-0243

0.31 1.17 0.35 1.33 0.42 1.59 0.53 2.00

021-w334

0.25 0.95 0.29 1.08 0.35 1.30 0.43 1.62

127-0312

0.30 1.12 0.34 1.27 0.40 1.52 0.50 1.90

051-0387

0.36 1.37 0.41 1.55 0.49 1.85 0.62 2.32

217-0218

0.41 1.54 0.47 1.75 0.56 2.09 0.70 2.62

051-0368*

0.85 3.19 0.97 3.62 1.15 4.33 1.45 5.43

143-0126

0.25 0.95 0.29 1.07 0.34 1.28 0.43 1.61

175-0247

0.26 0.98 0.30 1.14 0.37 1.37 0.46 1.72

Average

0.31 1.17 0.36 1.33 0.43 1.60 0.53 2.00

*This site has been excluded from the average.

Note: Site 245-0218 also has been excluded due to erroneous and incomplete data.

95%

SU

MU

0.82 3.08

0.74 2.79

0.60 2.27

0.71 2.66

0.87 3.25

0.98 3.66

2.02 7.59

0.60 2.25

0.64 2.40

0.75 2.80

Table 16. Truck ESAL factors with different reliability levels for rigid pavement design, slab thickness of 8.

Reliability Level

Site ID

75%

80%

85%

90%

ESAL Factors for Single-unit and Multi-unit Trucks

SU

MU SU

MU SU

MU

SU

MU

185-0227

0.37 2.00 0.42 2.26 0.50 2.70 0.63 3.40

285-0243

0.32 1.72 0.36 1.94 0.43 2.33 0.54 2.92

021-w334

0.27 1.46 0.30 1.65 0.37 1.97 0.46 2.47

127-0312

0.33 1.78 0.37 2.01 0.45 2.41 0.56 3.02

051-0387

0.40 2.10 0.44 2.38 0.53 2.85 0.66 3.57

217-0218

0.27 1.43 0.30 1.62 0.36 1.94 0.45 2.43

051-0368*

0.73 3.91 0.82 4.43 0.99 5.30 1.24 6.65

143-0126

0.23 1.26 0.26 1.43 0.32 1.71 0.40 2.14

175-0247

0.28 1.52 0.33 1.77 0.39 2.11 0.49 2.65

Average

0.31 1.66 0.35 1.88 0.42 2.25 0.52 2.83

*This site has been excluded from the average.

Note: Site 245-0218 also has been excluded due to erroneous and incomplete data.

95%

SU

MU

0.88 4.74

0.76 4.07

0.64 3.45

0.79 4.23

0.66 3.57

0.63 3.40

1.73 9.29

0.56 3.00

0.70 3.71

0.70 3.77

46

Table 17. Truck ESAL factors with different reliability levels for rigid pavement design, slab thickness of 10.

Reliability Level

Site ID

75%

80%

85%

90%

ESAL Factors for Single-unit and Multi-unit Trucks

SU

MU SU

MU SU

MU

SU

MU

185-0227

0.37 2.00 0.42 2.26 0.50 2.70 0.63 3.40

285-0243

0.32 1.72 0.36 1.95 0.43 2.33 0.55 2.92

021-w334

0.27 1.46 0.31 1.66 0.37 1.99 0.46 2.5

127-0312

0.33 1.78 0.37 2.02 0.45 2.42 0.56 3.03

051-0387

0.39 2.10 0.44 2.38 0.53 2.85 0.66 3.57

217-0218

0.27 1.43 0.30 1.62 0.36 1.94 0.45 2.44

051-0368*

0.75 4.00 0.84 4.53 1.01 5.42 1.26 6.80

143-0126

0.23 1.26 0.27 1.43 0.32 1.71 0.40 2.14

175-0247

0.28 1.52 0.33 1.77 0.39 2.12 0.50 2.66

Average

0.31 1.66 0.35 1.89 0.42 2.26 0.53 2.83

*This site has been excluded from the average.

Note: Site 245-0218 also has been excluded due to erroneous and incomplete data.

95%

SU

MU

0.88 4.74

0.76 4.09

0.65 3.48

0.79 4.23

0.93 5.00

0.63 3.41

1.77 9.50

0.56 3.00

0.70 3.71

0.74 3.96

Table 18. Truck ESAL factors with different reliability levels for rigid pavement design, slab thickness of 12.

Reliability Level

Site ID

75%

80%

85%

90%

ESAL Factors for Single-unit and Multi-unit Trucks

SU

MU SU

MU SU

MU

SU

MU

185-0227

0.38 2.01 0.43 2.28 0.50 2.73 0.64 3.42

285-0243

0.32 1.74 0.37 1.97 0.44 2.36 0.55 2.96

021-w334

0.27 1.46 0.31 1.66 0.37 1.99 0.46 2.5

127-0312

0.33 1.78 0.37 2.02 0.45 2.42 0.56 3.03

051-0387

0.39 2.10 0.44 2.38 0.53 2.85 0.66 3.57

217-0218

0.27 1.45 0.31 1.65 0.37 1.97 0.46 2.47

051-0368*

0.75 4.05 0.85 4.60 1.02 5.50 1.28 6.88

143-0126

0.23 1.27 0.27 1.44 0.32 1.72 0.40 2.16

175-0247

0.28 1.52 0.33 1.77 0.40 2.12 0.50 2.66

Average

0.31 1.67 0.35 1.90 0.42 2.27 0.53 2.85

*This site has been excluded from the average.

Note: Site 245-0218 also has been excluded due to erroneous and incomplete data.

95%

SU

MU

0.89 4.78

0.77 4.13

0.65 3.48

0.79 4.23

0.93 5.00

0.64 3.46

1.80 9.62

0.56 3.01

0.70 3.72

0.74 3.98

The results facilitate the pavement design process in which engineers could select the desired reliability level and the corresponding ESAL factor for a specific road section. Table 19 summarizes the location of the interstates (i.e., rural or urban) based on TADA. Table 20 and table 21 show the ESALs with various reliability levels for flexible pavements for WIM stations located on rural and urban interstate highways, respectively. For flexible pavement design, a structural number of 8 has been selected.

47

Table 22 and table 23 represent the ESALs calculated for rigid pavement design, assuming a slab thickness of 12 inches in which different reliability levels are considered for rural and urban WIM stations, respectively.

Table 19. Type of interstate highway for each Georgia WIM site.

Site ID 185-0227 285-0243 021-w334 127-0312 051-0387 217-0218 051-0368 143-0126 175-0247

Interstate Type Rural Rural Urban Urban Urban Urban Urban Rural Rural

Table 20. Truck ESAL factors with different reliability levels for flexible pavement design in rural WIM stations, structural number 8.

Site ID
185-0227 285-0243 143-0126 175-0247 Average

75%
SU MU 0.35 1.30 0.31 1.17 0.25 0.95 0.26 0.98 0.29 1.10

Reliability Level

80%

85%

90%

ESAL Factors for Single-unit and Multi-unit Trucks

SU

MU SU

MU

SU

MU

0.39 1.47 0.47 1.76 0.59 2.20

0.35 1.33 0.42 1.59 0.53 2.00

0.29 1.07 0.34 1.28 0.43 1.61

0.30 1.14 0.37 1.37 0.46 1.72

0.33 1.25 0.40 1.50 0.50 1.88

95%

SU

MU

0.82 3.08

0.74 2.79

0.60 2.25

0.64 2.40

0.70 2.63

Table 21. Truck ESAL factors with different reliability levels for flexible pavement design in urban WIM stations, structural number 8.

Site ID
021-w334 127-0312 051-0387 217-0218 Average

75%
SU MU 0.25 0.95 0.30 1.12 0.36 1.37 0.41 1.54 0.33 1.25

Reliability Level

80%

85%

90%

ESAL Factors for Single-unit and Multi-unit Trucks

SU

MU SU

MU

SU

MU

0.29 1.08 0.35 1.30 0.43 1.62

0.34 1.27 0.40 1.52 0.50 1.90

0.41 1.55 0.49 1.85 0.62 2.32

0.47 1.75 0.56 2.09 0.70 2.62

0.38 1.41 0.45 1.69 0.56 2.12

95%

SU

MU

0.60 2.27

0.71 2.66

0.87 3.25

0.98 3.66

0.79 2.96

48

Table 22. Truck ESAL factors with different reliability levels for rigid pavement design in rural WIM stations, slab thickness of 12.

Site ID
185-0227 285-0243 143-0126 175-0247 Average

75%
SU MU 0.38 2.01 0.32 1.74 0.23 1.27 0.28 1.52 0.30 1.64

Reliability Level

80%

85%

90%

ESAL Factors for Single-unit and Multi-unit Trucks

SU

MU SU

MU

SU

MU

0.43 2.28 0.50 2.73 0.64 3.42

0.37 1.97 0.44 2.36 0.55 2.96

0.27 1.44 0.32 1.72 0.40 2.16

0.33 1.77 0.40 2.12 0.50 2.66

0.35 1.87 0.42 2.23 0.52 2.80

95%

SU

MU

0.89 4.78

0.77 4.13

0.56 3.01

0.70 3.72

0.73 3.91

Table 23. Truck ESAL factors with different reliability levels for rigid pavement design in urban WIM stations, slab thickness of 12.

Site ID
021-w334 127-0312 051-0387 217-0218 Average

75%
SU MU 0.27 1.46 0.33 1.78 0.39 2.10 0.27 1.45 0.32 1.70

Reliability Level

80%

85%

90%

ESAL Factors for Single-unit and Multi-unit Trucks

SU

MU SU

MU

SU

MU

0.31 1.66 0.37 1.99 0.46 2.5

0.37 2.02 0.45 2.42 0.56 3.03

0.44 2.38 0.53 2.85 0.66 3.57

0.31 1.65 0.37 1.97 0.46 2.47

0.36 1.93 0.43 2.31 0.54 2.89

95%

SU

MU

0.65 3.48

0.79 4.23

0.93 5.00

0.64 3.46

0.75 4.04

RESULTS According to the results, the ESAL factors with 85 percent reliability are close to GDOT's default values for flexible pavement design in either rural or urban interstate highways. For rigid pavement, ESALs calculated based on 90 percent reliability are close to GDOT's default values. Since the variation of ESAL factors is significant, it is recommended to verify with site-specific ESAL factors before selecting ESAL factors and reliability levels for pavement design.
COST ANALYSIS From the viewpoint of pavement design, it is important to know how the pavement thickness and associated cost would vary in response to different reliability levels, especially how they compare

49

to those based on GDOT's current default ESAL factors. In the following analysis, the design inputs for flexible pavement were extracted from WIM stations. Then, GDOT pavement design software was used to calculate the required and proposed structural number. Three different ESAL factors were considered, and the results were compared.
Table 24 and table 25 present the design inputs used in the GDOT pavement design software in which the values for different categories are specific to the type of interstate highways (i.e., rural or urban). Table 26 through table 28 show the pavement design results using GDOT's default ESALs, ESALs from WIM data with 85 percent reliability level, and ESALs from WIM data with 90 percent reliability level, respectively. Finally, the increases in HMA costs are summarized in table 29.

Site ID
185-0227 285-0243 143-0126 175-0247 Average

Table 24. 1972 pavement design inputs for rural WIM stations.

Initial 1-way Traffic
26602.67 23368.71 34293.14 21571.71

24 Hour Truck %
28.86 10.84 27.55 23.74

SU Truck %
3.44 2.41 2.03 2.26

MU Truck %
25.42 8.43 25.51 21.48

No. of Lanes in
Each Direction
3 2 2 2

26459.00 22.75=23 2.53

20.21



LDF %
70 100 100 100 100

Soil Support Value
4 3 2.5 3.5 Default value

Regional Factor
1.4 1.6 1.8 1.4 Default value

Site ID
021-w334 127-0312 051-0387 217-0218 Average

Table 25. 1972 pavement design inputs for urban WIM stations.

Initial 1way
Traffic
21901.00 17223.46 24830.05 55272.64

24 Hour Truck %
12.48 17.89 17.61 13.67

SU Truck %
3.05 2.17 2.02 2.16

MU Truck %
9.42 15.71 15.58 11.5

No. of Lanes in
Each Direction
1 4 3 2

29807.00 15.41=15 2.35

13.05



LDF %
100 60 60 80 100

Soil Support Value
3 4 4 2.5 Default value

Regional Factor
1.6 1.7 1.7 1.6 Default value

50

Table 26. 1972 pavement design results for WIM stations using GDOT's default ESAL factors.

WIM

Proposed Required

Station

SN

SN

Location

Design Policy

Course 3 Thickness
(inch)

Rural

6.38

4.91%

1.00

6.70

Under

designed

8.25

4.11%

1.00

Urban

6.15

6.41

Under

7.5

designed

* Expense for hot mix asphalt (HMA) tonnage per lane mile.

Structural Coefficient
0.44 0.30 0.44 0.30

Structural Value
0.44 2.48 0.44 2.25

HMA Tonnage per Lane
Mile
3,582.0
3,291.0

Expense ($)*
232,947.0
214,060.0

Table 27. 1972 pavement design results for WIM stations using 85% reliability level ESAL factors.

WIM

Proposed Required

Station

SN

SN

Location

Design Policy

4.91%

Rural

6.38

6.70

Under

designed

4.45%

Urban

6.23

6.52

Under

designed

* Expense for HMA tonnage per lane mile.

Course 3 Structural Structural
Thickness Coefficient Value
(inch)

1.00

0.44

0.44

8.25

0.30

2.48

1.00

0.44

0.44

7.75

0.30

2.33

HMA Tonnage per Lane
Mile

Expense ($)*

3,582.0 232,947.0

3,388.0 220,356.0

Table 28. 1972 pavement design results for WIM stations using 90% reliability level ESAL factors.

WIM

Proposed Required

Station

SN

SN

Location

Design Policy

4.38%

Rural

6.60

6.90

Under

designed

4.90%

Urban

6.38

6.70

Under

designed

* Expense for HMA tonnage per lane mile.

Course 3 Structural Structural
Thickness Coefficient Value
(inch)

1.00

0.44

0.44

9.00

0.30

2.70

1.00

0.44

0.44

8.25

0.30

2.48

HMA Tonnage per Lane
Mile

Expense ($)*

3,872.0 251,835.0

3,582.0 232,973.0

Table 29. Average increase in HMA cost.

WIM Station Location Rural Urban

Reliability Level

85%

90%

0%

8.10%

2.94%

8.84%

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CHAPTER 6. DEVELOPMENT OF PAVEMENT ME INPUTS
TRAFFIC INPUTS IN AASHTO MEPDG MOP The AASHTO MEPDG MOP requires various data to design new or rehabilitated pavement structures. Generally, there are four different categories of inputs in AASHTOWare Pavement ME Design software (PMED). These input data are: climate inputs, layer/material property inputs, design features and layer property inputs, and traffic inputs. The required traffic input data which can be extracted from WIM data are: VCDs, MDFs, HDFs, axles per truck class, and axle load distribution factors or NALS. For each of these traffic inputs, depending on the level of design, the PMED requires traffic distributions for each of 10 standard FHWA vehicle classes (i.e., classes 4 through 13). The design levels are defined as follows:
Level 1: Most accurate design level requiring site-specific weight and volume data collected at or near the project site.
Level 2: Intermediate accuracy design level with a modest knowledge of traffic characteristics requiring regional weight data and site-specific volume data.
Level 3: Least accurate design level with knowledge only of statewide default weight and volume data.
In the following sections, the five different traffic input data are defined separately.
Vehicle Class Distribution VCD represents the percentage of each truck class (i.e., 4 through 13) within the annual average daily truck traffic (AADTT) for the base year, which is defined as the first year of the forecast period. The sum of the percent AADTT of all truck classes must equal 100.
52

Monthly Distribution Factor MDF is defined as the seasonal differences in AADTT by allocating a normalized weight factor to each month of the year. As the default, a seasonally independent value of 1 for each of the 12 months is assumed as level 3 data. In this way, months with higher AADTT will receive a weight factor greater than 1, whereas months with lower AADTT will receive a factor less than 1 (ARA, Inc. 2004). The sum of the MDF of all truck classes must equal 12. Hourly Distribution Factor HDF is defined as the percentage of total trucks within each hour using data measured continuously over a 24-hour period. The sum of the percent of daily truck traffic per time increment should add up to 100 percent. Axles per Truck Class This input represents the average number of axles for each truck class (i.e., 4 to 13) for each axle group type (i.e., single, tandem, tridem, and quad). Axle Load Distribution Factors or Normalized Axle Load Spectra The axle load distribution factors represent the percentage of the total axle applications for load intervals in a specific axle group type (i.e., single, tandem, tridem, and quad) and vehicle classes 4 through 13. The load intervals for each axle group type are as below:
Single axles 3,000 lb to 40,000 lb at 1,000-lb intervals. Tandem axles 6,000 lb to 80,000 lb at 2,000-lb intervals. Tridem and quad axles 12,000 lb to 102,000 lb at 3,000-lb intervals.
53

The NALS can only be extracted from WIM data. Thus, the level of input depends on the data source (i.e., site, regional, or national).
To generate AASHTOWare Pavement ME Design traffic inputs, the WIM volume and weight data were reviewed using a quality control procedure. The QC procedure will be discussed comprehensively in the following section. The cleaned data were then processed using computer programming to generate traffic inputs, including VCD, MDF, HDF, axles per truck class, and NALS.
TRUCK TRAFFIC CLASSIFICATION GROUPS
Truck traffic classification (TTC) groups were originally developed based on the LTPP databases and provide the opportunity for engineers to use the national default values (i.e., Level 3 design inputs) when site-specific traffic data are not available. Seventeen TTC groups were defined based on the distributions of vehicle classes in traffic streams (ARA 2004), as shown in figure 28. In the PMED process, one typically obtains traffic composition on a specific roadway section from shortterm traffic counts to identify the closely matched TTC group. Then, the required design traffic inputs associated with the identified TTC group can be obtained from the historical databases (Wang et al. 2015). However, the actual traffic data may not match well with any of the national default TTC groups. Thus, using the closely matched TTC group for design may result in over- or under-design of pavement structure. Nassiri et al. (2014) investigated the influence of site-specific traffic characteristics and the AASHTOWare Pavement ME Design default values on the performance of both flexible and rigid pavements in Alberta. Based on the results, TTC groups were found to be influential on AC pavement performance, especially for rutting. In another study, Li et al. (2015) aimed at developing simplified TTC groups based on cluster analysis of vehicle class
54

distributions in Arkansas. Like many other states in the U.S., Georgia currently uses national default values (Level 3 design inputs) for pavement design.
Figure 28. Line graph. Truck traffic classification groups based on National Cooperative Highway Research Program (NCHRP) Project 1-37A (Ara, Inc. 2004).
The purpose of this study was to develop customized or state-specific TTC groups using existing WIM data in Georgia. The researchers aimed to leverage unsupervised machine learning techniques to develop clusters (groups) of truck traffic by mining the comprehensive WIM data compiled in the AASHTOWare Pavement ME Design traffic input formats by categories, including VCD, MDF, HDF, and NALS. Table 30 represents the 17 TTC groups and their corresponding description and vehicle class distribution.
55

Table 30. TTC group description and corresponding vehicle class distribution default values (percentages) (ARA, Inc. 2004). 56

MACHINE LEARNING TECHNIQUES
Since obtaining high-quality WIM data is an expensive and time-consuming process, not all roads are equipped with WIM sensors. As a result, site-specific traffic data are limited to specific road sections. The issue appears when designing new pavements since designers have no idea about the TTC grouping of the section, subsequently leading to confusion in the selection of PMED input data.
Machine learning methodologies in transportation engineering have been widely used in recent years. One common approach is clustering WIM stations to generate similar traffic-loading spectra as well as traffic data for designing new road sections without site-specific traffic information. Generally, the traffic vehicle class distribution has been utilized as a feature for clustering WIM stations. In this study, VCD, MDF, HDF, and NALS for single- and tandem-axle loads were generated and used as features for clustering analysis. Therefore, the purpose of this study was to find the trucking pattern of the Georgia roads based on PMED traffic input data and generate new TTCs to determine whether, apart from VCD, other input data play a considerable role in clustering WIM sites.
Principal Component Analysis Principal component analysis (PCA) is an unsupervised method that is commonly used for dimension reduction, in which high-dimensional features are projected to a low-dimensional space without losing much information. Principal components (PCs) are created in the order of the amount of the variation and are orthogonal to each other. In other words, PC1 captures the direction of most variance, PC2 is orthogonal to PC1 and captures the direction of second most variation, and so forth. As discussed previously, PCA is applied to each feature category separately. Figure 29 shows the top 10 PCs for each of 5 feature categories defined previously.
57

Figure 29. Bar graph. Determining the optimal number of principal components for the attributes.
As seen in figure 29, the variance captured by each subsequent PC decreases. The number of PCs (i.e., dimensions) to keep is a judgment call that reflects the trade-off between the amount of variance to retain and the complexity (dimensionality) of the resulting feature space. For this analysis, the decision was based on the sudden drop of variance as well as retaining at least 60 percent of variances for each feature category. The ultimately retained PCs are indicated inthe red rectangle in figure 29, and the corresponding percentages of variance captured are summarized
58

in table 31. As a result, a total of 20 PCs (i.e., three PCs each for VCD, MDF, and HDF, respectively; six PCs for NALS-Single Axles; five PCs for NALS-Tandem Axles) were retained, which is a significant reduction from the original 565 features. The 20 PCs were used for the subsequent cluster analysis.

Table 31. Percent of variance explained by feature categories.

Principal Component (PC) Feature Category
PC1 PC2 PC3 PC4 PC5 PC6

VCD

0.41 0.19 0.14

MDF

0.30 0.21 0.09

HDF

0.40 0.27 0.18

NALS-Single Axles 0.22 0.14 0.11 0.09 0.08 0.06

NALS-Tandem Axles 0.18 0.15 0.10 0.09 0.08
Shading denotes "Not Used".

Total
0.74 0.60 0.85 0.70 0.60

Clustering Technique

Cluster analysis aims to find homogeneous subgroups among observations such that the observations within one group will be similar to one another and different from the objects in other groups. A variety of cluster methods have been developed, with K-means being the most popular one that works well with many different data sets. In K-means clustering, the number of clusters, K, needs to be prespecified. The idea behind the K-means method is to find the K clusters so that the within-cluster variation is minimized. With the commonly used Euclidean distance as the proximity measure, the K-means algorithm can be expressed as an optimization problem as in equation 10.

1

minimize

-

,...,

||

,

(10)

59

Where, = the jth feature of observation i
p = the number of features | | = the number of observations in the kth cluster The within-cluster sum-of-squares is also referred to as inertia, which measures how internally coherent the clusters are. Inertia is commonly used to determine the optimal number of clusters (K). A range of K values were experimented within the PC features derived previously. The inertia was then plotted against K in figure 30, showing the inertia reduces as K increases. In extreme cases, when the number of observations equals K, the inertia reduces to zero. In practice, the elbow method is often applied, where K is chosen as the point of the maximum curvature in the inertia plot (indicated by the red arrow in figure 30). Following this approach, K was chosen to be 4 in this study.
60

Figure 30. Line graph. Elbow method for determining K.

For visualization purposes, the 20 PCs derived previously (referred to as the lower-level PCA) were further projected onto a two-dimensional space again using the PCA method, referred to as the higher-level PCA. This allowed the researchers to visually inspect how the 20 WIM sites are clustered on a two-dimensional plane. This nested PCA procedure is illustrated in figure 31.

10 VCD features
PCA 3 VCD PCs PC1 PC2 PC3

120 MDF features
PCA 3 MDF PCs PC1 PC2 PC3

24 HDF features
PCA 3 HDF PCs PC1 PC2 PC3

230 NALS Single Axle Features
PCA 6 Single Axles PCs PC1 PC2 PC3 PC4 PC5 PC6

180 NALS Tandem Axle Features
PCA 5 Tandem Axles PCs PC1 PC2 PC3 PC4 PC5

PCA

PC1

PC2

Figure 31. Illustration. Nested PCA procedure.

Lower Level PCA Feature reduction: 564 -> 20
Higher Level PCA Feature reduction: 20 -> 2

Corresponding to the higher-level PCA, the loading factors of the 20 lower-level PCs were calculated with respect to the two higher-level PC axes and are shown in table 32.

61

Table 32. Loading factors of 20 lower-level PCs
The clusters of 20 WIM sites were plotted in the two-dimensional plane in the higher-level PC space, together with the scaled loading vectors, shown in figure 32.
90 Figure 32. Plot. Clustering result and loading vectors.
62

As shown in figure 32, all clusters are clearly separated in the two-dimensional higher-level PC space. Cluster 0 (red square) consists of 8 WIM sites, which are in the lower left region, while cluster 1 (green triangles) consists of 10 WIM sites, which are located in the upper left region. Clusters 2 and 3 (blue diamond and cyan circle) contain only one WIM site each and both belong to the same WIM station 051-0368. For direct reference, the WIM site IDs, and their corresponding clusters are included in figure 32, as well.
Apparently, clusters 2 and 3 (representing directional traffic at the same WIM station: 051-0368) are further separated from other sites and they are also farther apart from each other in figure 33. This seemingly strange clustering outcome is intuitively interpretable, as the station 051-0368 is located on Interstate 16 near the City of Savannah and serves as the gateway for heavy trucks entering and leaving the Savannah Port. The distinct directional patterns of truck traffic at this station are well expected.
The lines in figure 32 represent loading vectors for the 20 lower-level PCs. They can be used to interpret which features or feature categories have contributed to separating different clusters. For example, the VCD feature category (PC1_VCD) plays the most important role in separating clusters 2 and 3 from clusters 0 and 1. This can be seen from figure 32 by drawing an imaginary line that is perpendicular to the PC1_VCD vector (see the dashed purple line). This is due to the fact that the vehicle class distribution at the station 051-0368 near the Savannah Port is quite different from other sites in Georgia. Besides PC1_VCD, other vectors, especially those crossing the dashed purple line (e.g., PC1-HDF, PC3-MDF, and PC2-tandem-axles, etc.), more or less contributed to separating this special station from other stations. Figure 33 shows the clusters of the 20 WIM sites by their geographical locations.
63

Figure 33. Map. Clusters of WIM sites. PAVEMENT PERFORMANCE ANALYSIS AND RESULTS To compare the design implications of the derived clusters with the default TTC groups, the TTC groups that match the clusters were found based on their similarity. As a result, clusters 0 and 1 are close to TTC group 1; cluster 2 is close to TTC group 4; and cluster 3 is close to TTC group 9. For illustration purposes, the paired clusters and TTC groups are plotted together by the VCD features, as shown in figure 34.
64

Figure 34. Line graph. Traffic pattern comparison of clusters and default TTC groups. Both the cluster-based traffic inputs and the corresponding TTC group-based traffic inputs were entered into the AASHTOWare Pavement ME Design software to simulate pavement performance. Two pavement designs (one for jointed plain concrete pavement [JPCP] and one for flexible pavement) were evaluated. The JPCP design consisted of five layers commonly used in Georgia. The top layer was a 12" PCC layer with the recommended JPCP values. The second layer was a 3" AC layer with Superior Performing Asphalt Pavement (Superpave): 64-22. The third layer was a 12" crushed gravel layer, serving as the nonstabilized base. The fourth and fifth layers were two subgrade A-7-6 sections, with a 12" layer on the top and the bottom layer serving as a semiinfinite layer. The flexible pavement design included a 8" asphalt concrete layer followed by a 12" unbound aggregate base layer on A-2-4 subgrade soils.
The AASHTOWare Pavement ME Design software requires a series of traffic inputs, which can be obtained from the WIM data. The software allows pavement design to be conducted with three
65

levels of inputs depending on the data availability. Level 1 uses site-specific data, which provide the highest level of input accuracy for the pavement design. Level 2 uses regional data, which provide an intermediate level of input accuracy for the pavement design. Level 3 uses national or global averages, which provide the least detailed input values.
As the purpose of this analysis was focused on the performance comparison of different traffic input scenarios, Level 3 inputs were used. Specifically, the performance difference was analyzed between two traffic input scenarios: one with the cluster-based traffic inputs derived in this project and the other with the national traffic inputs based on the default TTC groups. All the material inputs used in the analysis followed the recently developed input library from The GDOT Pavement ME Design User Input Guide (Kim et al. 2020).
The performance curves over a design period of 20 years are plotted in figure 35 and figure 36 for JPCP and flexible pavement designs, respectively. For the JPCP, the default TTC groups resulted in worse performance than the cluster group counterparts. The performance gap was the largest between TTC group 1 and clusters 0 and 1 (with about 50 inch in International Roughness Index (IRI) and over 0.1 inch in faulting at the end of the design period). For the flexible pavement, similar performance trends were observed. The TTC groups generally performed equally well or worse than the cluster groups. The apparent gap in bottom-up cracks occurred between TTC group 1 and clusters 0 and 1, which is about 6 percent at the end of the design period. The difference in permanent deformation between TTC group 9 and cluster 3 was about 0.15 inch at the end of the design period. The findings indicate that using the national default TTC groups that closely match the actual traffic data resulted in over-design of the pavement structure, especially for JPCP, in Georgia. This highlights the importance of developing customized TTC groups using state-specific WIM data.
66

Figure 35. Line graph. JPCP pavement performance comparison of cluster-based traffic inputs and default TTC groups.
67

Figure 36. Line graph. Flexible pavement performance comparison of cluster-based traffic inputs and default TTC groups.
68

CHAPTER 7. CONCLUSIONS AND RECOMMENDATIONS
CONCLUSIONS
In this study, data from 10 WIM stations throughout the state of Georgia were analyzed and truck ESAL factors were updated using QC-checked data. Different structural numbers and slab thicknesses were selected for flexible and rigid pavement designs. According to the results, neither variation of structural number nor slab thickness has a significant effect on the resultant ESAL factors. Also, the reliability concept was implemented and Georgia's ESAL factors were calculated using different reliability levels. The results showed that the ESAL factors with 85 percent reliability are close to GDOT's currently used default values for flexible pavement design in either rural or urban interstate highways. In the case of rigid pavement design, ESALs calculated based on a 90 percent reliability level are close to GDOT's default values.
As a result, WIM data provide the opportunity to develop more accurate truck ESAL factors than GDOT's default ESAL factors since WIM data represent the actual traffic data on roadways. Thus, the data obtained from this study can be utilized by pavement designers and engineers for the design of new pavements as well as pavement maintenance purposes. GDOT should continue to obtain good quality WIM data for longer periods to consider the possible variation in truck ESAL factors over time.
In this study, unsupervised learning algorithms were leveraged to analyze high-dimensional traffic characteristic data collected from the existing WIM stations in Georgia. A practical analytics procedure was developed to first apply stratified principal component analysis to each of the traffic feature categories consistent with the AASHTOWare Pavement ME Design inputs. This results in a significantly reduced feature space formed by the top principal components that capture most of
69

the data variance in each feature category. Then, K-means cluster analysis was conducted in the reduced dimension space. Knowing that most traffic features, especially those within the same category, are highly correlated, the proposed procedure is capable of extracting higher-level sparse features from low-level dense features and effectively clustering WIM sites in the low-dimensional feature space.
The proposed analytics procedure was applied to the WIM data in Georgia. The resulting clusters were verified by their geographical locations as well as their projections in the higher-level twodimensional PC space, in which influential feature categories can be visually inspected. The performances of two exemplar designs (one JPCP and one flexible pavement) were evaluated using AASHTOWare Pavement ME Design software with respect to two traffic input scenarios: one based on the derived clusters and one based on the corresponding national default TTC groups. The results showed that using the national default TTC groups led to over-design of the pavement structure, especially JPCP, in Georgia. The performance gaps between the national default TTC groups and state-specific traffic characteristics highlight the importance of developing customized TTC groups using state-specific WIM data.
RECOMMENDATIONS
It should be noted that the quality of clusters derived largely depends on the quality of the WIM data. Thus, it is important that reliable quality control procedures are implemented to continuously monitor any data errors or anomalies likely arising from internal or external sources. Additionally, WIM sensors should be calibrated on a regular basis to ensure high-quality data are collected in support of pavement design practice. Also, the more data are utilized for clustering, the more precise results are obtained. Therefore, it is recommended to gather data from more WIM stations
70

that have been monitored from continuous years to obtain a wide range of datasets for the analysis. Further, it is highly recommended to re-evaluate AASHTOWare Pavement ME Design traffic inputs and ESAL factors when the WIM data are calibrated by the vendor following the updated GDOT QC process.
71

APPENDIX A: VEHICLE CLASS DISTRIBUTION BY MONTH FOR DIRECTIONAL WIM STATIONS
Figure 37. Line graph. Vehicle class distribution by month, Site 285-0243 NB.
Figure 38. Line graph. Vehicle class distribution by month, Site 285-0243 SB. 72

Figure 39. Line graph. Vehicle class distribution by month, Site 021-w334 NB.
Figure 40. Line graph. Vehicle class distribution by month, Site 021-w334 SB. 73

Figure 41. Line graph. Vehicle class distribution by month, Site 127-0312 NB.
Figure 42. Line graph. Vehicle class distribution by month, Site 127-0312 SB. 74

Figure 43. Line graph. Vehicle class distribution by month, Site 143-0126 EB.
Figure 44. Line graph. Vehicle class distribution by month, Site 143-0126 WB. 75

Figure 45. Line graph. Vehicle class distribution by month, Site 051-0368 EB.
Figure 46. Line graph. Vehicle class distribution by month, Site 051-0368 WB. 76

Figure 47. Line graph. Vehicle class distribution by month, Site 051-0387 NB.
Figure 48. Line graph. Vehicle class distribution by month, Site 051-0387 SB. 77

Figure 49. Line graph. Vehicle class distribution by month, Site 217-0218 EB.
Figure 50. Line graph. Vehicle class distribution by month, Site 217-0218 WB. 78

Figure 51. Line graph. Vehicle class distribution by month, Site 245-0218 EB.
Figure 52. Line graph. Vehicle class distribution by month, Site 245-0218 WB. 79

Figure 53. Line graph. Vehicle class distribution by month, Site 175-0247 EB.
Figure 54. Line graph. Vehicle class distribution by month, Site 175-0247 WB. 80

APPENDIX B: WIM DATA ANALYSIS AFTER QC CHECKS
Figure 55. Line graph. Monthly distribution factors, Site 285-0243 NB. Figure 56. Line graph. Monthly distribution factors, Site 285-0243 SB.
81

Figure 57. Line graph. Monthly distribution factors, Site 021-w334 NB.
Figure 58. Line graph. Monthly distribution factors, Site 021-w334 SB. 82

Figure 59. Line graph. Monthly distribution factors, Site 127-0312 NB.
Figure 60. Line graph. Monthly distribution factors, Site 127-0312 SB. 83

Figure 61. Line graph. Monthly distribution factors, Site 143-0126 EB.
Figure 62. Line graph. Monthly distribution factors, Site 143-0126 WB. 84

Figure 63. Line graph. Monthly distribution factors, Site 051-0368 EB.
Figure 64. Line graph. Monthly distribution factors, Site 051-0368 WB. 85

Figure 65. Line graph. Monthly distribution factors, Site 051-0387 NB.
Figure 66. Line graph. Monthly distribution factors, Site 051-0387 SB. 86

Figure 67. Line graph. Monthly distribution factors, Site 217-0218 EB.
Figure 68. Line graph. Monthly distribution factors, Site 217-0218 WB. 87

Figure 69. Line graph. Monthly distribution factors, Site 245-0218 EB.
Figure 70. Line graph. Monthly distribution factors, Site 245-0218 WB. 88

Figure 71. Line graph. Monthly distribution factors, Site 175-0247 EB.
Figure 72. Line graph. Monthly distribution factors, Site 175-0247 WB. 89

Figure 73. Line graph. GVW frequency distribution, Site 285-0243.
Figure 74. Line graph. GVW frequency distribution, Site 021-w334. 90

Figure 75. Line graph. GVW frequency distribution, Site 127-0312.
Figure 76. Line graph. GVW frequency distribution, Site 143-0126. 91

Figure 77. Line graph. GVW frequency distribution, Site 051-0368.
Figure 78. Line graph. GVW frequency distribution, Site 051-0387. 92

Figure 79. Line graph. GVW frequency distribution, Site 217-0218.
Figure 80. Line graph. GVW frequency distribution, Site 245-0218. 93

Figure 81. Line graph. GVW frequency distribution, Site 175-0247. 94

APPENDIX C: MEPDG TRAFFIC INPUT DATA EXTRACTED FROM WIM DATA AS FEATURE CATEGORIES

VEHICLE CLASS DISTRIBUTIONS

Table 33. Vehicle class distribution, Site 185-0227 NB.

Truck Class 4

5

6

7

8

9

10 11

12

13 Total

AADTT 60.0 247.0 119.0 1.0 451.0 4405.0 19.0 227.0 170.0 0.0 5699.0

VCD

1.05 4.33 2.08 0.01 7.91 77.29 0.33 3.98 2.98 0.00 100.0

Truck Class AADTT VCD

Table 34. Vehicle class distribution, Site 185-0227 SB.

4

5

6

7

8

9

10 11 12 13 Total

37.0 118.0 67.0 1.0 335.0 3382.0 10.0 192.0 124.0 0.00 4266.0

0.86 2.76 1.57 0.02 7.85 79.27 0.23 4.50 2.90 0.00 100.0

Table 35. Vehicle class distribution, Site 285-0243 NB.

Truck Class 4

AADTT 21.0

VCD

1.74

5

6

7

8

9

10 11 12 13 Total

192.0 58.0 0.0 157.0 715.0 6.0 42.0 18.0 0.0 1209

15.88 4.80 0.00 13.0 59.14 0.50 3.47 1.49 0.00 100.0

Truck Class AADTT VCD

Table 36. Vehicle class distribution, Site 285-0243 SB.

4

5

6

7

8

9

10 11

12

18.0 156.0 61.0 0.0 142.0 755.0 4.0 42.0 16.0

1.51 13.07 5.11 0.00 11.89 63.23 0.34 3.52 1.34

13 Total 0.0 1194.0 0.00 100.0

Table 37. Vehicle class distribution, Site 021-w334 NB.

Truck Class 4

5

6

7

8

9

10 11

12

13 Total

AADTT 13.0 97.0 63.0 0.00 96.0 494.0 5.0 7.0 11.0 0.00 786.0

VCD

1.65 12.34 8.01 0.00 12.21 62.84 0.63 0.89 1.40 0.00 100.0

Truck Class AADTT VCD

Table 38. Vehicle class distribution, Site 021-w334 SB.

4

5

6

7

8

9

10 11 12

11.0 86.0 48.0 0.00 73.0 363.0 8.0 3.0 6.0

1.84 14.38 8.02 0.00 12.20 60.70 1.33 0.50 1.00

13 Total 0.00 598.0 0.00 100.0

95

Truck Class AADTT VCD

Table 39. Vehicle class distribution, Site 127-0312 NB.

4

5

6

7

8

9

10 11 12

51.0 344.0 145.0 1.0 550.0 1977.0 29.0 121.0 87.0

1.540 10.405 4.385 0.030 16.636 59.800 0.877 3.660 2.631

13 1.0 0.030

Total 3306.0 100.0

Truck Class AADTT VCD

Table 40. Vehicle class distribution, Site 127-0312 SB.

4

5

6

7

8

9

10 11 12

23.0 190.0 69.0 1.0 266.0 3933.0 14.0 63.0 42.0

0.50 4.12 1.50 0.02 5.78 85.46 0.30 1.36 0.91

13 Total 1.0 4602.0 0.02 100.0

Truck Class AADTT VCD

Table 41. Vehicle class distribution, Site 051-0387 NB.

4

5

6

7

8

9

10 11 12

70.0 301.0 183.0 2.0 570.0 3192.0 44.0 117.0 89.0

1.53 6.59 4.00 0.04 12.47 69.85 0.96 2.56 1.95

13 Total 2.0 4570 0.04 100.0

Truck Class AADTT VCD

Table 42. Vehicle class distribution, Site 051-0387 SB.

4

5

6

7

8

9

10 11 12

68.0 310.0 149.0 1.0 576.0 3230.0 32.0 118.0 92.0

1.49 6.77 3.25 0.02 12.58 70.55 0.70 2.58 2.01

13 Total 2.0 4578.0 0.04 100.0

Truck Class AADTT VCD

Table 43. Vehicle class distribution, Site 217-0218 EB.

4

5

6

7

8

9

10 11 12

127.0 115.0 135.0 2.0 208.0 2130.0 26.0 61.0 31.0

4.47 4.05 4.76 0.07 7.33 75.03 0.92 2.15 1.09

13 Total 4.0 2839.0 0.14 100.0

Truck Class AADTT VCD

Table 44. Vehicle class distribution, Site 217-0218 WB.

4

5

6

7

8

9

10 11 12

5.0 86.0 50.0 0.0 51.0 638.0 6.0 15.0 8.0

0.58 10.01 5.82 0.00 5.94 74.27 0.70 1.75 0.93

13 Total 0.0 859.0 0.00 100.0

Truck Class AADTT VCD

Table 45. Vehicle class distribution, Site 051-0368 EB.

4

5

6

7

8

9

10 11 12

48.0 140.0 64.0 0.0 51.0 337.0 6.0 8.0 4.0

7.29 21.28 9.73 0.00 7.75 51.22 0.91 1.22 0.61

13 Total 0.0 658.0 0.00 100.0

96

Table 46. Vehicle class distribution, Site 051-0368 WB.

Truck Class 4

5

6

7

8

AADTT 9.0 67.0 9.0 0.0 13.0

VCD

6.12 45.58 6.12 0.00 8.84

9

10

11

12

49.0 0.0 0.0 0.0

33.33 0.00 0.00 0.00

13 Total 0.0 147.0 0.00 100.0

Truck Class AADTT VCD

Table 47. Vehicle class distribution, Site 143-0126 EB.

4

5

6

7

8

9

10 11

12

26.0 152.0 67.0 1.0 227.0 1977.0 9.0 122.0 81.0

0.98 5.71 2.52 0.04 8.52 74.24 0.34 4.58 3.04

13 Total 1.0 2663.0 0.04 100.0

Truck Class AADTT VCD

Table 48. Vehicle class distribution, Site 143-0126 WB.

4

5

6

7

8

9

10 11 12 13 Total

33.0 182.0 97.0 1.0 359.0 3294.0 20.0 206.0 138.0 1.0 4331.0

0.76 4.20 2.24 0.02 8.29 76.06 0.46 4.76 3.19 0.02 100.0

Truck Class AADTT VCD

Table 49. Vehicle class distribution, Site 245-0218 EB.

4

5

6

7

8

9

10 11 12

21.0 128.0 80.0 10.0 160.0 1055.0 8.0 35.0 18.0

1.38 8.43 5.27 0.66 10.54 69.50 0.53 2.31 1.19

13 Total 3.0 1518.0 0.20 100.0

Truck Class AADTT VCD

Table 50. Vehicle class distribution, Site 245-0218 WB.

4

5

6

7

8

9

10 11

12

40.0 219.0 126.0 3.0 404.0 1602.0 22.0 49.0 24.0

1.60 8.78 5.05 0.12 16.19 64.21 0.88 1.96 0.96

13 Total 6.0 2495.0 0.24 100.0

Truck Class AADTT VCD

Table 51. Vehicle class distribution, Site 175-0247 EB.

4

5

6

7

8

9

10 11 12

31.0 97.0 84.0 1.0 210.0 1689.0 61.0 31.0 26.0

1.39 4.35 3.76 0.04 9.41 75.67 2.73 1.39 1.16

13 Total 2.0 2232.0 0.09 100.0

Truck Class AADTT VCD

Table 52. Vehicle class distribution, Site 175-0247 WB.

4

5

6

7

8

9

10 11 12

26.0 94.0 84.0 1.0 161.0 1662.0 38.0 27.0 23.0

1.23 4.44 3.97 0.05 7.61 78.51 1.79 1.28 1.09

13 Total 1.0 2117.0 0.05 100.0

97

MONTHLY DISTRIBUTION FACTORS

Table 53. Monthly distribution factors, Site 185-0227 NB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.76 0.80 0.88 0.79 0.77 1.08 0.97 0.95 0.96 0.92

February 0.86 0.85 0.94 0.49 0.85 1.03 1.06 0.96 0.95 1.20

March

1.52 1.10 1.10 0.92 1.34 1.11 1.15 1.01 1.04 1.20

April

1.28 1.06 1.07 1.02 1.40 1.10 0.93 0.98 0.98 1.48

May

1.14 1.06 0.95 1.38 1.13 1.09 0.82 1.00 1.00 1.06

June

1.17 1.17 0.97 1.12 1.09 1.08 0.66 1.01 0.98 0.35

July

1.09 1.04 0.95 1.25 0.98 1.06 0.96 0.97 0.97 1.13

August

0.88 1.04 0.96 1.09 0.98 1.12 1.27 1.10 1.05 0.92

September 0.82 1.13 1.10 0.92 0.91 1.08 0.88 0.98 0.98 0.49

October

0.84 1.00 1.05 1.09 0.93 1.12 1.54 1.11 1.08 1.06

November 0.84 0.91 1.04 1.22 0.82 1.09 0.84 1.02 1.05 0.99

December 0.72 0.78 0.93 0.66 0.71 0.00 0.86 0.92 0.97 1.20

Table 54. Monthly distribution factors, Site 185-0227 SB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.85 0.88 0.98 0.87 1.08 1.09 1.01 0.97 0.97 1.58

February 0.87 0.81 0.89 0.77 0.88 1.01 0.96 0.97 0.95 0.62

March

1.32 1.01 1.01 0.90 1.04 1.07 1.35 1.01 1.01 1.23

April

0.96 0.95 0.92 0.93 0.84 1.08 0.97 0.97 0.99 0.69

May

0.93 0.97 0.95 1.00 0.86 1.08 1.07 0.99 1.01 0.89

June

0.97 1.06 0.90 1.30 0.93 1.10 0.94 1.02 1.02 0.96

July

1.02 1.02 0.89 1.44 0.91 1.06 0.92 0.98 0.99 0.82

August

0.85 1.15 1.03 0.97 0.90 1.13 0.94 1.10 1.06 0.89

September 0.94 1.21 1.07 0.97 0.97 1.08 0.84 0.97 0.96 1.30

October

1.09 1.00 1.11 0.97 1.20 1.15 0.98 1.08 1.04 0.75

November 1.06 0.94 1.05 0.87 1.18 1.09 0.94 1.00 1.03 1.10

December 1.08 0.93 1.14 0.97 1.15 0.00 1.01 0.93 0.97 1.17

98

Table 55. Monthly distribution factors, Site 285-0243 NB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.99 0.74 0.93 0.59 1.25 1.09 0.85 0.97 1.00 0.37

February 1.03 0.73 0.87 1.28 1.27 1.05 0.81 1.01 0.96 0.63

March

0.50 0.39 0.45 1.57 0.63 0.48 0.39 0.45 0.47 0.52

April

0.94 1.09 1.16 0.59 1.09 1.06 1.04 1.03 1.03 1.05

May

1.01 1.22 1.21 0.98 1.03 1.06 1.32 0.99 1.05 1.31

June

1.28 1.14 1.18 0.59 1.04 1.06 1.31 0.99 1.07 1.21

July

1.13 1.13 1.02 1.57 1.06 1.07 1.41 1.10 1.13 1.26

August

1.02 1.11 1.04 1.38 1.01 1.08 1.46 1.20 1.11 1.78

September 1.00 1.05 1.15 0.69 0.96 1.03 1.19 1.07 1.02 1.78

October

1.07 1.15 1.06 0.98 1.00 1.08 0.88 1.13 1.06 0.84

November 1.12 1.15 0.98 1.18 0.88 0.99 0.68 1.04 0.99 0.79

December 0.90 1.09 0.95 0.59 0.79 0.94 0.65 1.01 1.12 0.47

Table 56. Monthly distribution factors, Site 285-0243 SB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

1.46 0.88 0.95 1.23 1.36 1.05 0.98 0.89 0.94 1.07

February 0.60 0.87 0.97 1.56 1.33 1.00 1.17 0.97 1.02 0.90

March

0.35 0.51 0.46 1.73 0.69 0.46 0.59 0.48 0.47 0.63

April

0.99 1.02 1.01 1.07 1.09 1.09 1.17 1.03 0.98 1.16

May

1.09 1.15 1.08 0.49 1.06 1.16 1.04 1.09 1.06 0.99

June

1.55 1.10 1.04 0.16 0.98 1.13 0.95 1.05 1.08 0.72

July

1.20 1.05 1.02 1.23 0.95 1.08 1.32 1.05 1.20 1.16

August

1.17 1.09 1.19 1.23 1.00 1.09 1.13 1.19 1.10 1.25

September 0.96 1.02 1.12 0.90 0.96 1.01 0.81 1.05 1.00 1.07

October

0.93 1.15 1.08 0.74 1.01 1.05 1.13 1.11 1.02 1.07

November 0.99 1.11 1.06 1.07 0.85 0.97 0.86 1.07 1.03 1.25

December 0.71 1.07 1.00 0.58 0.73 0.91 0.85 1.01 1.11 0.72

99

Table 57. Monthly distribution factors, Site 021-w334 NB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January 0.779 0.986 0.952 1.224 0.796 1.048 0.960 0.86 0.91 0.86

February 0.815 0.942 0.980 0.979 0.867 0.997 0.935 0.78 0.88 0.29

March

1.176 1.024 1.010 1.387 1.151 1.041 0.753 0.85 0.90 1.43

April

0.879 1.091 1.166 2.040 1.234 1.093 0.740 1.00 0.99 0.57

May

0.976 1.039 1.036 1.469 1.069 1.082 0.772 1.03 0.79 1.71

June

1.127 0.928 0.965 0.489 1.181 1.139 0.740 1.40 1.05 0.29

July

1.132 0.923 0.989 0.653 1.135 1.100 0.916 1.20 1.10 0.86

August

1.043 1.073 1.037 1.224 1.012 1.104 1.243 1.08 1.09 1.14

September 0.987 1.024 1.068 0.653 0.940 1.056 1.149 0.96 1.05 0.57

October 1.033 1.038 1.056 0.816 0.961 1.204 1.230 0.97 1.10 1.71

November 1.091 0.970 0.946 0.408 0.889 1.132 1.425 0.96 1.09 0.86

December 0.956 0.957 0.788 0.653 0.759 0.000 1.130 0.91 1.05 1.71

Table 58. Monthly distribution factors, Site 021-w334 SB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.71 1.02 0.95 1.16 0.87 1.06 1.16 0.98 0.95 0.89

February 0.76 0.95 0.89 1.23 0.85 1.00 1.02 1.01 0.88 0.22

March

1.04 0.95 0.92 0.87 0.91 1.02 0.91 1.03 0.87 0.67

April

0.79 1.07 1.06 1.23 0.89 1.08 0.84 0.97 1.06 0.89

May

0.89 1.04 1.05 1.01 0.92 1.14 0.78 0.82 0.89 1.11

June

0.92 0.88 0.93 0.87 0.96 1.11 0.75 0.84 1.10 0.44

July

0.97 0.82 0.99 0.87 0.96 1.07 1.04 1.03 1.06 1.11

August

0.97 1.11 1.00 1.60 0.95 1.11 1.16 0.96 1.02 1.56

September 0.97 1.01 1.09 0.94 1.02 1.01 1.00 1.08 1.00 1.11

October

1.29 1.11 1.14 1.01 1.32 1.24 1.41 1.34 1.19 2.22

November 1.36 1.01 0.98 0.58 1.23 1.12 0.99 1.18 1.00 1.56

December 1.28 0.98 0.94 0.58 1.07 0.00 0.90 0.76 0.97 0.22

100

Table 59. Monthly distribution factors, Site 127-0312 NB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.89 0.89 1.00 1.04 0.85 1.19 1.27 1.06 1.07 0.55

February 1.00 0.95 0.98 0.73 0.97 1.12 1.11 1.02 1.05 0.32

March

1.65 1.20 1.17 1.36 1.71 1.18 1.22 1.12 1.14 0.77

April

1.80 1.23 1.20 1.41 1.89 1.23 1.23 1.06 1.14 0.79

May

1.19 1.17 1.04 0.73 1.30 1.22 1.10 1.08 1.10 0.67

June

1.26 1.16 0.97 0.41 1.12 1.13 1.11 1.08 1.08 0.51

July

1.13 1.09 0.96 1.62 1.01 1.13 1.12 1.01 1.09 3.03

August

0.88 1.19 1.02 0.99 0.97 1.21 1.02 1.16 1.17 0.53

September 0.59 0.96 1.00 0.94 0.60 0.88 0.69 0.81 0.77 0.43

October

0.44 0.73 0.91 0.94 0.55 0.84 0.82 0.95 0.81 1.89

November 0.58 0.70 0.97 0.78 0.51 0.81 0.61 0.86 0.85 0.24

December 0.53 0.67 0.73 0.99 0.46 0.00 0.64 0.79 0.73 2.27

Table 60. Monthly distribution factors, Site 127-0312 SB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

1.08 0.98 1.01 1.02 1.34 1.15 0.73 0.79 0.80 0.48

February 1.01 0.94 0.89 0.91 1.00 1.03 0.86 0.96 1.00 0.59

March

1.23 1.11 1.12 0.98 1.16 1.35 1.30 1.38 1.34 0.72

April

1.16 1.04 1.09 1.10 0.96 1.19 1.21 1.10 1.18 0.96

May

0.81 0.92 0.93 0.91 0.78 1.08 1.13 0.93 0.97 0.72

June

0.88 0.98 0.92 0.83 0.83 1.03 1.00 0.94 0.90 0.86

July

0.99 0.97 0.82 0.94 0.75 0.93 0.81 0.88 0.88 0.59

August

0.80 1.09 0.94 1.36 0.83 1.09 1.00 1.10 1.06 3.80

September 0.80 1.00 1.09 0.83 0.81 1.01 1.01 0.88 0.92 0.99

October

0.96 0.93 1.06 0.79 1.13 0.92 0.99 0.90 0.95 1.12

November 1.20 1.05 1.22 1.32 1.40 1.19 1.20 1.23 1.20 0.67

December 1.03 0.93 0.84 0.94 0.94 0.00 0.71 0.90 0.80 0.51

101

Table 61. Monthly distribution factors, Site 051-0387 NB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.80 1.04 1.02 0.88 0.84 1.23 1.00 1.06 1.13 0.79

February 0.97 1.01 0.98 0.71 0.94 1.16 0.98 0.99 1.11 0.86

March

1.29 1.21 1.22 0.90 1.68 1.25 1.13 1.04 1.23 1.03

April

1.41 0.95 1.18 1.45 1.73 0.69 0.83 0.84 0.69 0.70

May

1.02 0.95 1.03 1.67 1.07 0.96 2.74 0.95 0.93 4.07

June

1.14 0.99 0.88 0.91 0.96 0.93 1.25 1.03 0.98 1.83

July

1.03 0.98 0.91 0.91 0.91 0.86 0.71 1.00 0.91 0.49

August

0.89 1.01 0.95 1.12 0.85 0.98 0.72 1.13 1.02 0.56

September 0.84 1.06 0.91 0.85 0.70 0.91 0.62 0.87 0.85 0.42

October

0.85 1.04 1.11 0.96 0.86 1.10 0.85 1.13 1.09 0.65

November 0.90 0.89 0.95 1.05 0.77 0.98 0.61 1.03 1.11 0.28

December 0.87 0.86 0.86 0.60 0.69 0.96 0.56 0.93 0.94 0.33

Table 62. Monthly distribution factors, Site 051-0387 SB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

1.00 1.08 1.05 0.69 1.14 1.13 1.21 1.07 1.06 0.72

February 0.98 1.02 1.95 0.64 0.95 1.07 1.08 1.00 1.05 0.81

March

1.13 1.06 0.98 0.75 0.84 1.17 1.08 1.06 1.15 0.73

April

0.83 0.91 0.94 2.66 0.96 0.82 2.54 0.77 0.78 5.08

May

2.74 0.92 0.76 0.96 0.88 1.06 0.88 1.01 0.97 0.65

June

1.25 1.01 0.72 0.59 0.92 1.07 0.85 1.08 1.07 0.74

July

0.71 1.02 0.77 1.17 0.89 1.05 0.78 1.05 1.09 0.54

August

0.72 1.15 0.82 1.06 0.88 1.13 0.80 1.16 1.15 0.70

September 0.62 1.00 0.87 0.88 0.83 0.92 0.68 0.90 0.91 0.61

October

0.85 1.00 1.12 1.17 1.31 0.88 0.81 1.06 1.03 0.63

November 0.61 0.95 1.07 0.85 1.28 0.86 0.69 0.98 0.93 0.45

December 0.56 0.88 0.94 0.59 1.13 0.84 0.60 0.86 0.80 0.35

102

Table 63. Monthly distribution factors, Site 217-0218 EB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.11 1.05 0.68 0.43 1.35 0.95 0.79 0.87 0.95 0.62

February 0.11 1.01 0.65 0.42 1.54 0.85 0.70 0.85 0.97 0.24

March

0.13 1.00 0.57 0.29 2.32 0.69 0.54 0.87 0.89 0.42

April

1.17 1.02 1.22 4.96 1.00 1.01 1.55 0.93 0.96 8.62

May

1.51 0.73 1.03 0.75 1.01 1.11 1.12 1.09 1.06 0.26

June

1.48 0.76 0.98 0.43 0.98 1.07 1.02 1.07 1.05 0.30

July

1.45 0.71 1.10 0.80 0.97 1.06 1.12 1.03 1.06 0.21

August

1.51 0.79 1.18 0.80 1.01 1.14 1.19 1.16 1.19 0.22

September 1.38 1.10 1.10 0.85 0.47 1.03 1.05 0.99 0.99 0.23

October

1.47 1.19 1.26 0.98 0.44 1.09 1.07 1.13 1.09 0.38

November 1.39 1.06 1.07 0.74 0.38 1.02 0.96 1.04 0.94 0.29

December 0.29 1.58 1.17 0.55 0.51 0.96 0.89 0.97 0.86 0.20

Table 64. Monthly distribution factors, Site 217-0218 WB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

1.21 1.74 1.98 4.62 2.88 2.48 3.91 2.77 2.65 3.46

February 1.24 1.70 1.77 2.18 3.04 2.15 1.96 2.89 2.77 2.22

March

1.45 1.64 1.69 3.06 3.16 1.84 2.35 2.79 2.39 3.18

April

1.06 0.64 0.71 0.11 0.27 0.65 0.35 0.41 0.44 0.39

May

0.91 0.66 0.67 0.23 0.30 0.63 0.51 0.41 0.46 0.48

June

1.01 0.66 0.66 0.08 0.28 0.61 0.44 0.48 0.56 0.46

July

0.90 0.61 0.70 0.11 0.30 0.60 0.38 0.41 0.47 0.18

August

0.76 0.66 0.82 0.15 0.47 0.68 0.48 0.47 0.59 0.18

September 0.88 0.59 0.70 0.19 0.44 0.60 0.44 0.36 0.41 0.32

October

0.90 0.67 0.80 0.54 0.43 0.66 0.48 0.35 0.49 0.55

November 0.83 0.58 0.78 0.42 0.31 0.55 0.40 0.33 0.38 0.21

December 0.86 1.86 0.74 0.31 0.12 0.54 0.28 0.32 0.39 0.37

103

Table 65. Monthly distribution factors, Site 051-0368 EB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.71 1.05 1.27 2.00 0.83 1.01 1.13 0.95 0.85 0.67

February 0.79 0.87 1.06 0.00 1.03 0.70 1.07 0.92 0.90 0.96

March

1.23 0.83 1.20 2.00 0.78 1.03 1.80 1.02 1.10 0.94

April

1.33 1.13 1.31 2.00 0.98 1.18 0.85 1.06 0.97 0.99

May

1.37 0.98 1.58 1.00 0.88 1.18 1.15 1.01 1.04 0.89

June

0.87 0.57 0.65 1.00 0.20 0.73 0.59 1.07 0.98 1.06

July

1.12 0.50 0.63 0.00 0.69 0.50 1.46 0.97 0.99 0.96

August

0.74 1.20 1.18 2.00 1.08 1.18 1.80 1.08 1.04 1.01

September 1.04 1.31 0.89 0.00 1.37 1.01 0.28 0.90 0.86 0.84

October

1.31 1.35 0.87 1.00 1.42 1.39 0.85 1.09 1.10 1.14

November 0.87 1.31 0.63 0.00 1.42 1.24 0.59 1.01 1.07 1.21

December 0.61 0.91 0.72 1.00 1.32 0.86 0.42 0.93 1.10 1.31

Table 66. Monthly distribution factors, Site 051-0368 WB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.64 1.45 2.02 0.00 1.21 1.10 1.20 0.89 0.79 0.61

February 0.75 0.87 1.01 0.00 0.70 0.85 1.56 0.79 0.70 0.50

March

1.27 0.72 1.45 0.00 0.77 0.73 1.44 0.99 0.95 1.25

April

1.25 0.87 1.16 0.00 0.57 1.22 1.44 1.24 0.82 0.74

May

1.30 0.72 1.01 0.00 0.26 0.79 1.32 1.13 1.30 0.70

June

0.91 1.01 1.01 0.00 1.15 1.16 0.72 1.01 1.04 0.92

July

1.07 0.58 0.72 6.00 0.77 0.61 1.56 1.29 1.14 0.61

August

0.77 1.45 0.58 0.00 0.89 0.67 1.56 1.25 1.11 1.11

September 1.06 1.16 0.87 0.00 1.02 1.46 0.36 0.97 0.79 1.53

October

1.37 1.01 0.43 6.00 1.40 1.46 0.48 1.12 1.01 1.18

November 0.97 1.30 1.01 0.00 1.66 0.55 0.00 0.67 1.30 1.38

December 0.63 0.87 0.72 0.00 1.60 1.40 0.36 0.64 1.04 1.46

104

Table 67. Monthly distribution factors, Site 143-0126 EB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

1.22 1.44 1.58 1.42 1.41 1.70 1.71 1.66 1.61 1.35

February 1.13 1.21 1.27 1.22 1.18 1.28 1.23 1.25 1.29 1.71

March

1.68 1.66 1.68 0.58 1.61 1.84 2.18 1.81 1.91 0.93

April

2.00 1.73 1.70 0.90 1.75 1.79 1.93 1.79 1.74 1.14

May

1.74 1.73 1.63 0.90 1.69 1.66 1.61 1.78 1.78 1.00

June

1.65 1.75 1.55 0.80 1.61 1.82 1.60 1.77 1.75 1.85

July

1.35 1.35 1.29 1.38 1.35 1.19 1.04 1.24 1.23 1.21

August

0.57 0.60 0.69 1.25 0.85 0.41 0.36 0.38 0.34 0.78

September 0.29 0.28 0.33 1.35 0.36 0.14 0.14 0.13 0.15 0.71

October

0.33 0.20 0.19 1.99 0.16 0.13 0.14 0.11 0.14 1.21

November 0.03 0.05 0.08 0.19 0.04 0.04 0.05 0.06 0.05 0.11

December 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Table 68. Monthly distribution factors, Site 143-0126 WB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.90 1.00 1.11 0.98 0.93 1.18 1.22 1.11 1.12 0.93

February 0.99 1.01 1.03 1.14 0.95 1.13 1.16 1.08 1.07 0.90

March

1.23 1.11 1.13 1.23 1.11 1.09 1.18 1.10 1.12 1.37

April

1.37 1.15 1.15 1.44 1.24 1.06 1.14 1.08 1.03 1.10

May

1.16 1.15 1.13 0.93 1.21 1.05 1.03 1.08 1.05 1.47

June

1.15 1.23 1.11 1.14 1.21 1.07 1.12 1.07 1.03 0.90

July

1.08 1.17 1.10 0.59 1.15 1.04 0.96 1.04 1.05 0.87

August

1.03 1.20 1.17 0.85 1.23 1.11 0.96 1.15 1.15 1.43

September 0.91 0.99 0.98 1.14 0.98 1.05 0.99 0.99 0.99 1.10

October

1.12 0.93 0.99 1.27 0.93 0.99 0.97 1.07 1.11 1.07

November 0.85 0.83 0.90 1.06 0.84 0.98 0.98 0.99 1.01 0.80

December 0.21 0.22 0.21 0.21 0.21 0.24 0.28 0.25 0.27 0.07

105

Table 69. Monthly distribution factors, Site 245-0218 EB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.87 0.96 0.95 0.80 0.83 1.03 0.20 0.78 0.81 0.38

February 0.81 0.90 0.86 0.82 0.79 0.95 0.18 0.75 0.79 0.20

March

1.20 1.01 0.98 1.39 1.05 1.02 0.17 0.82 0.92 0.30

April

1.14 0.99 0.96 1.14 1.05 1.02 0.73 0.96 0.96 0.22

May

1.06 1.05 1.04 1.17 1.16 1.07 1.33 1.10 1.11 0.13

June

1.02 1.00 1.03 1.18 1.06 1.03 1.22 1.14 1.17 0.20

July

0.92 1.01 1.14 0.88 1.15 1.02 1.28 1.05 1.12 0.11

August

0.94 1.04 1.10 1.70 1.11 1.03 1.27 1.19 1.23 0.10

September 1.09 1.01 1.13 0.80 1.06 0.97 1.33 1.00 1.01 0.06

October

1.03 1.07 1.10 0.95 1.02 1.01 1.24 1.20 1.09 0.09

November 1.05 0.95 0.89 0.57 0.90 0.91 1.76 1.03 0.91 7.33

December 0.86 0.99 0.83 0.61 0.82 0.94 1.29 0.99 0.88 2.87

Table 70. Monthly distribution factors, Site 245-0218 WB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.92 0.99 1.16 0.49 1.04 0.48 0.27 0.29 0.28 0.10

February 0.94 1.00 1.18 0.71 0.95 0.95 0.42 0.72 0.82 0.08

March

1.34 1.09 1.26 1.48 0.98 1.40 0.67 1.17 1.31 0.06

April

1.27 1.09 1.22 0.87 1.08 1.19 0.58 1.17 1.20 0.20

May

0.95 1.08 1.14 1.02 1.30 0.84 0.97 1.13 1.10 1.04

June

1.08 1.14 1.10 0.92 1.29 0.92 1.20 1.11 1.09 1.66

July

0.99 1.06 1.02 1.00 1.28 0.65 1.98 0.88 0.94 3.91

August

1.01 1.15 1.05 1.14 1.22 0.90 1.32 1.12 1.17 0.95

September 1.19 0.89 0.78 0.87 0.93 0.85 1.31 1.03 0.93 1.03

October

0.83 0.87 0.74 1.19 0.73 1.29 1.91 1.15 1.14 2.17

November 0.81 0.83 0.70 0.95 0.63 1.31 0.88 1.13 1.05 0.76

December 0.67 0.82 0.66 1.36 0.56 1.22 0.49 1.10 0.97 0.05

106

Table 71. Monthly distribution factors, Site 175-0247 EB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.46 1.05 1.02 0.66 0.95 1.12 0.98 1.00 1.02 0.96

February 0.57 1.03 1.00 0.45 1.01 1.06 0.85 1.00 0.96 0.65

March

0.50 0.78 0.65 0.53 0.81 0.75 0.63 0.72 0.82 0.43

April

0.58 1.20 1.45 2.29 0.91 0.96 1.65 0.79 0.95 3.51

May

0.66 1.18 1.10 0.71 1.61 0.97 1.12 0.98 1.02 1.41

June

0.62 1.11 0.92 0.37 1.26 1.01 0.94 1.08 1.07 0.68

July

0.52 1.08 1.01 0.55 1.13 1.04 0.94 1.07 1.00 0.53

August

0.94 1.15 1.01 1.05 0.95 1.09 0.93 1.21 1.10 0.51

September 2.13 0.91 0.97 3.08 0.72 0.92 0.81 0.94 0.93 0.76

October

2.14 0.78 0.96 0.79 1.04 1.11 1.09 1.16 1.02 1.26

November 2.00 0.75 0.91 0.68 0.86 0.99 1.00 1.10 1.05 0.59

December 0.88 0.99 1.01 0.84 0.76 0.99 1.06 0.96 1.06 0.72

Table 72. Monthly distribution factors, Site 175-0247 WB.

Truck Classification

Month

4

5

6

7

8

9

10

11

12

13

January

0.60 0.90 0.94 0.76 0.70 1.36 1.30 1.13 1.29 1.70

February 0.74 0.90 0.86 0.87 0.77 1.27 1.15 1.10 1.19 1.15

March

0.66 0.68 0.65 0.68 0.62 0.89 0.81 0.79 0.95 0.79

April

0.76 1.36 1.30 0.79 0.62 1.16 1.24 1.08 1.20 1.70

May

0.95 1.29 1.13 1.35 1.23 1.06 1.17 1.13 1.20 0.89

June

0.91 1.15 1.13 1.19 1.26 0.92 0.98 1.07 1.02 0.89

July

0.66 0.95 1.02 0.89 1.26 0.70 0.75 0.87 0.68 0.85

August

0.55 0.85 0.94 1.11 1.28 0.57 0.57 0.72 0.53 0.66

September 0.96 0.68 0.62 1.79 1.10 0.49 0.49 0.59 0.45 0.32

October

2.21 1.12 1.18 0.95 1.42 1.25 1.18 1.32 1.14 1.00

November 2.10 1.00 1.15 0.76 1.03 1.16 1.11 1.16 1.19 0.96

December 0.90 1.12 1.08 0.87 0.73 1.18 1.24 1.05 1.16 1.09

107

HOURLY DISTRIBUTION FACTORS

Table 73. Hourly distribution factors, Site 185-0227 NB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.795 1.466 1.297 1.209 1.284 1.615 2.385 3.376 4.178 5.254 6.301 7.167

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 7.528 7.337 7.274 6.799 6.506 6.255 5.293 4.399 3.593 2.972 2.545 2.158

Table 74. Hourly distribution factors, Site 185-0227 SB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.676 1.497 1.533 1.582 1.705 1.876 2.310 3.102 4.061 5.197 5.872 6.065

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.445 7.185 7.524 7.3005 6.780 6.409 5.527 4.556 3.682 3.232 2.778 2.094

108

Table 75. Hourly distribution factors, Site 285-0243 NB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 0.790 0.567 0.538 0.637 1.111 2.146 2.515 3.277 4.605 5.484 5.881 6.372

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.946 7.414 7.649 7.784 7.980 7.705 6.287 4.707 3.475 2.709 2.077 1.330

Table 76. Hourly distribution factors, Site 285-0243 SB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.322 0.933 0.836 0.672 0.711 1.277 2.510 3.666 4.506 4.930 5.592 6.254

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.416 6.563 6.985 7.335 7.986 7.570 6.272 5.254 4.455 3.497 2.576 1.872

Table 77. Hourly distribution factors, Site 021-w334 NB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 0.980 0.671 0.636 0.771 1.089 1.932 4.788 6.964 6.989 5.658 5.749 5.650

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.068 6.298 6.347 6.470 6.526 6.458 6.109 4.256 3.422 2.577 2.086 1.494

109

Table 78. Hourly distribution factors, Site 021-w334 SB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.079 0.776 0.731 0.659 0.904 1.732 2.592 4.431 4.401 4.633 4.985 5.539

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 5.940 6.057 6.483 7.789 8.659 10.397 6.589 5.076 3.864 2.925 2.090 1.659

Table 79. Hourly distribution factors, Site 127-0312 NB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.349 1.095 0.958 0.927 1.260 1.916 3.269 4.530 4.552 5.448 6.738 7.181

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 7.407 7.260 7.190 7.013 6.588 6.120 5.121 4.196 3.369 2.692 2.132 1.676

Table 80. Hourly distribution factors, Site 127-0312 SB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.116 0.885 0.793 0.855 1.009 1.561 2.175 3.356 4.336 5.657 6.755 7.343

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 7.368 7.666 7.859 7.806 7.744 6.776 5.322 4.277 3.287 2.594 1.986 1.462

110

Table 81. Hourly distribution factors, Site 051-0387 NB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.324 0.986 0.914 0.921 1.190 2.023 3.786 4.869 5.189 5.444 6.148 6.782

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.951 7.112 7.245 7.163 6.877 6.041 5.203 4.026 3.189 2.701 2.159 1.749

Table 82. Hourly distribution factors, Site 051-0387 SB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.218 0.973 0.909 0.993 1.319 1.795 2.649 3.653 4.521 5.364 6.352 6.777

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.985 7.054 6.965 6.989 7.416 7.612 5.810 4.412 3.516 2.804 2.237 1.666

Table 83. Hourly distribution factors, Site 217-0218 EB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.08 0.81 0.72 0.81 1.38 3.38 4.42 4.72 4.69 5.00 5.55 5.98

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.15 6.42 6.64 6.92 7.28 7.36 5.75 4.60 3.68 2.99 2.13 1.57

111

Table 84. Hourly distribution factors, Site 217-0218 WB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 0.66 0.38 0.30 0.36 0.91 2.71 3.59 4.62 4.21 4.44 5.10 5.90

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.45 6.79 8.12 8.15 8.67 8.54 6.49 4.85 3.56 2.38 1.71 1.09

Table 85. Hourly distribution factors, Site 051-0368 EB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 0.95 0.57 0.48 0.49 1.02 3.05 6.62 7.78 7.14 6.18 5.86 5.63

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 5.65 5.86 6.25 6.08 6.30 6.47 5.33 3.80 2.81 2.31 1.91 1.45

Table 86. Hourly distribution factors, Site 051-0368 WB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 0.68 0.43 0.32 0.45 0.60 1.47 2.87 4.91 4.82 4.34 4.97 5.61

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.13 6.34 7.29 9.35 9.31 8.60 7.09 5.17 3.60 2.61 1.80 1.25

112

Table 87. Hourly distribution factors, Site 143-0126 EB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.71 1.32 1.19 1.19 1.46 1.98 2.38 2.72 3.18 4.07 5.39 6.32

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.57 6.66 6.97 6.98 7.10 7.12 6.67 5.76 4.70 3.61 2.71 2.24

Table 88. Hourly distribution factors, Site 143-0126 WB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.81 1.50 1.30 1.23 1.48 2.15 2.95 3.58 3.94 4.76 5.88 6.53

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.51 6.41 6.87 6.85 6.80 6.02 5.26 5.04 4.22 3.67 2.86 2.39

Table 89. Hourly distribution factors, Site 245-0218 EB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.13 0.78 0.69 0.73 0.90 1.84 3.36 4.56 4.75 4.47 4.89 5.42

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.14 6.53 6.91 7.27 7.82 8.47 6.43 5.24 4.42 3.32 2.30 1.67

113

Table 90. Hourly distribution factors, Site 245-0218 WB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 0.87 0.63 0.54 0.61 0.84 1.68 3.63 6.93 6.20 5.24 5.43 5.69

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 6.24 6.29 6.36 7.05 7.32 7.76 6.45 5.01 3.42 2.61 1.90 1.30

Table 91. Hourly distribution factors, Site 175-0247 EB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 1.35 1.02 0.92 1.12 1.32 1.87 2.95 4.46 4.51 4.91 5.61 6.38

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 7.16 7.11 7.05 7.17 7.18 6.58 5.58 4.62 3.91 2.92 2.46 1.86

114

Table 92. Hourly distribution factors, Site 175-0247 WB.

Time Period 12:00 a.m. 1:00 a.m. 1:00 a.m. 2:00 a.m. 2:00 a.m. 3:00 a.m. 3:00 a.m. 4:00 a.m. 4:00 a.m. 5:00 a.m. 5:00 a.m. 6:00 a.m. 6:00 a.m. 7:00 a.m. 7:00 a.m. 8:00 a.m. 8:00 a.m. 9:00 a.m. 9:00 a.m. 10:00 a.m. 10:00 a.m. 11:00 a.m. 11:00 a.m. 12:00 p.m.

Distribution, percent 0.92 0.69 0.77 0.99 1.49 2.33 3.07 3.81 4.31 5.08 6.08 6.92

Time Period 12:00 p.m. 1:00 p.m. 1:00 p.m. 2:00 p.m. 2:00 p.m. 3:00 p.m. 3:00 p.m. 4:00 p.m. 4:00 p.m. 5:00 p.m. 5:00 p.m. 6:00 p.m. 6:00 p.m. 7:00 p.m. 7:00 p.m. 8:00 p.m. 8:00 p.m. 9:00 p.m. 9:00 p.m. 10:00 p.m. 10:00 p.m. 11:00 p.m. 11:00 p.m. 12:00 a.m.

Distribution, percent 7.53 7.86 7.49 7.51 7.32 6.77 5.51 4.49 3.45 2.60 1.78 1.24

115

NORMALIZED AXLE LOAD SPECTRA SINGLE AXLES

Mean Axle Load,
lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 93. Single-axle load distribution factors, Site 185-0227 NB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

5.57 11.60 6.20 7.01 8.06 9.84 10.12 8.91 7.35 6.37 5.47 3.85 2.55 2.23 1.60 1.23 0.87 0.51 0.26 0.17 0.06 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

12.53 13.75 9.64 9.86 10.52 9.35 7.65 6.47 5.48 3.77 2.92 2.38 1.73 1.29 0.88 0.66 0.52 0.31 0.17 0.09 0.04 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.40 3.32 4.97 5.92 6.39 7.45 12.40 26.84 15.76 4.97 3.48 2.53 1.72 1.39 0.77 0.34 0.15 0.09 0.05 0.03 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.10 1.10 2.21 2.76 2.21 5.52 7.73 7.18 6.35 4.14 7.18 9.67 12.43 14.92 5.80 3.04 2.21 1.10 0.28 1.66 0.83 0.28 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

16.92 21.70 13.86 8.87 6.71 6.08 7.21 5.82 3.45 2.16 1.73 1.37 1.07 0.90 0.69 0.57 0.42 0.27 0.13 0.07 0.03 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.19 3.57 6.34 6.64 6.16 6.20 13.12 32.60 17.90 2.19 0.68 0.59 0.78 0.69 0.25 0.06 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.50 5.44 11.63 9.84 8.27 14.08 14.89 15.50 9.18 4.00 2.23 0.96 0.57 0.63 0.13 0.09 0.01 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.71 4.72 11.92 13.72 6.44 10.89 15.78 8.59 5.66 4.55 4.07 3.31 2.63 1.91 1.31 0.87 0.52 0.26 0.10 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.99 5.75 12.09 16.96 12.02 9.08 7.87 7.73 7.62 7.02 5.40 3.03 1.42 0.65 0.25 0.10 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13
5.62 5.00 3.75 5.00 6.25 7.50 12.50 16.88 8.75 6.88 15.00 3.12 1.88 0.00 0.62 0.62 0.00 0.62 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

116

Mean Axle Load,
lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 94. Single-axle load distribution factors, Site 185-0227 SB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

2.42 11.96 4.43 5.68 6.22 8.06 8.73 8.96 6.72 6.59 7.31 6.49 4.75 3.50 2.68 2.08 1.74 0.81 0.37 0.23 0.08 0.05 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

4.68 6.79 4.98 6.90 12.48 12.40 11.14 9.43 7.60 5.92 4.88 3.63 2.95 2.26 1.50 1.03 0.67 0.37 0.15 0.10 0.06 0.03 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.21 0.30 0.61 0.53 1.04 3.34 9.54 26.29 31.13 7.57 4.96 4.22 3.11 2.76 2.26 1.10 0.63 0.32 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.12 0.00 0.28 0.56 1.40 1.96 3.07 1.12 4.75 4.47 7.26 8.10 11.73 16.20 16.76 11.73 5.31 1.40 1.68 0.84 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

14.05 21.66 13.71 7.80 6.00 4.42 6.56 7.19 5.12 2.28 2.23 2.12 1.71 1.43 1.06 0.86 0.81 0.51 0.24 0.12 0.05 0.02 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.19 0.51 1.04 1.11 1.28 2.10 6.44 25.34 45.77 9.71 1.27 1.10 1.55 1.68 0.65 0.18 0.05 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.38 0.63 1.36 4.96 4.93 6.18 14.05 24.29 23.61 10.02 4.60 1.93 1.69 0.87 0.38 0.08 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.05 0.16 0.52 1.13 2.03 4.30 12.82 19.16 11.54 9.62 9.08 7.84 6.60 5.42 4.16 2.88 1.67 0.69 0.23 0.07 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.03 0.30 1.02 2.79 4.85 6.40 9.64 13.46 16.72 17.46 13.71 7.19 3.29 1.75 0.81 0.37 0.15 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13
8.13 12.44 3.35 7.18 9.09 7.18 14.35 12.92 11.48 5.26 5.26 0.96 1.44 0.48 0.00 0.48 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

117

Mean Axle Load, lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 95. Single-axle load distribution factors, Site 285-0243 NB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.93

4.25

0.10

0.00 12.28 0.97

0.00

0.68

0.25

4.88

9.15

0.28

0.00 17.09 2.09

0.26

2.27

0.75

6.53

8.95

0.86

1.85 16.55 2.79

1.43

4.86

3.21

5.56 10.04 2.39

0.00 10.94 3.47

3.39 10.81 6.31

6.38 10.12 4.87

0.00

6.27

4.58

4.95 11.38 6.63

6.14 11.48 4.62

3.70

5.77

4.02 10.68 10.86 9.01

6.02

9.77

7.87

3.70

6.53

9.62 19.92 12.33 11.86

6.92

7.63 14.59 1.85

4.89 19.16 20.18 9.10 14.97

7.74

6.36 15.18 7.41

5.63 20.31 13.80 10.16 16.26

6.12

5.56 10.66 0.00

4.04 10.08 9.38

7.86 11.56

6.79

4.18 14.91 3.70

2.25 10.92 5.60

4.63

7.23

6.91

3.08 10.26 9.26

1.61

7.60

4.43

3.73

5.07

4.93

1.96

3.91

9.26

1.20

2.42

3.91

3.12

3.27

4.68

1.55

2.12 14.81 1.02

1.21

1.17

2.33

1.92

5.30

1.25

1.62

7.41

0.79

0.52

0.39

1.77

1.06

4.48

1.08

1.28

9.26

0.66

0.16

0.26

1.36

0.45

3.19

0.88

1.17

1.85

0.60

0.05

0.26

1.01

0.14

1.81

0.64

0.80

5.56

0.48

0.02

0.00

0.61

0.02

1.44

0.50

0.63

1.85

0.35

0.00

0.00

0.43

0.02

1.14

0.41

0.48

7.41

0.25

0.00

0.00

0.25

0.00

0.78

0.31

0.44

7.41

0.21

0.00

0.00

0.20

0.00

0.42

0.35

0.38

3.70

0.18

0.00

0.00

0.11

0.00

0.24

0.22

0.31

0.00

0.11

0.00

0.00

0.06

0.00

0.22

0.13

0.18

0.00

0.09

0.00

0.00

0.05

0.00

0.04

0.07

0.06

0.00

0.07

0.00

0.00

0.02

0.00

0.09

0.04

0.02

0.00

0.05

0.00

0.00

0.01

0.00

0.09

0.02

0.02

0.00

0.04

0.00

0.00

0.01

0.00

0.04

0.01

0.00

0.00

0.01

0.00

0.00

0.01

0.00

0.01

0.01

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
0.00 13.21 9.43 13.21 2.83 5.66 16.04 5.66 17.92 10.38 2.83 1.89 0.00 0.00 0.94 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

118

Mean Axle Load, lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 96. Single-axle load distribution factors, Site 285-0243 SB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

3.38 5.34 2.81 4.17 4.87 7.41 8.81 9.88 9.05 8.73 9.94 10.88 6.30 3.17 2.36 1.42 0.74 0.340 0.10 0.08 0.04 0.02 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

3.71 5.60 8.08 11.56 14.23 12.69 10.65 9.21 7.04 4.93 3.66 2.56 1.98 1.47 1.01 0.74 0.41 0.21 0.13 0.05 0.03 0.02 0.02 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.10 0.28 0.68 2.34 5.78 11.47 18.41 22.56 16.07 6.98 3.77 2.69 2.43 2.34 1.60 1.19 0.82 0.30 0.14 0.03 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.41 2.82 1.41 1.41 0.00 2.82 1.41 1.41 5.63 7.04 8.45 8.45 7.04 21.13 11.27 9.86 7.04 1.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

14.78 20.05 11.89 7.58 7.53 8.89 8.79 5.95 3.69 2.49 2.23 1.81 1.23 0.94 0.72 0.53 0.47 0.19 0.11 0.06 0.03 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.38 2.48 2.18 2.10 4.86 11.51 19.88 24.53 19.05 6.16 1.70 1.54 1.37 0.83 0.29 0.09 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.33 0.33 0.50 1.66 7.13 15.26 22.06 26.37 13.93 7.46 2.65 1.66 0.17 0.33 0.00 0.17 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.17 0.78 3.14 3.63 4.83 9.05 13.19 13.12 9.83 9.12 8.48 7.14 5.85 4.67 3.42 2.11 1.01 0.36 0.07 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.25 0.75 3.21 6.31 6.63 9.01 11.86 14.97 16.26 11.56 7.23 5.07 3.27 1.92 1.06 0.45 0.14 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13
0.00 4.88 2.44 7.32 4.88 4.88 9.76 26.83 4.88 31.71 0.00 2.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

119

Mean Axle Load,
lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 97. Single-axle load distribution factors, Site 021-w334 NB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

4.75 9.80 10.91 10.33 8.99 8.32 7.41 6.72 6.23 5.26 4.42 3.57 2.97 2.43 2.01 1.49 1.17 0.87 0.70 0.61 0.28 0.17 0.15 0.10 0.11 0.03 0.01 0.02 0.01 0.0 0.0 0.0 0.01 0.0 0.0 0.0 0.0 0.0 0.0

6.13 9.12 11.72 11.63 10.99 9.91 8.17 6.82 5.68 4.43 3.41 2.58 1.83 1.58 1.21 0.99 0.83 0.65 0.56 0.52 0.39 0.27 0.18 0.11 0.11 0.03 0.04 0.02 0.02 0.02 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00

0.67 1.65 3.34 5.81 8.67 10.68 11.55 11.40 11.71 9.33 7.56 5.79 3.67 2.21 1.46 1.13 1.12 0.72 0.62 0.38 0.25 0.13 0.07 0.03 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 1.38 0.00 3.45 2.07 6.90 4.14 3.45 8.28 10.34 11.03 8.97 6.90 7.59 3.45 3.45 4.83 2.76 2.76 0.69 1.38 4.83 0.69 0.69 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

15.08 16.23 12.96 9.88 8.50 7.46 6.36 5.31 4.45 3.46 2.53 1.62 1.29 1.00 0.80 0.60 0.53 0.40 0.32 0.33 0.26 0.17 0.14 0.09 0.07 0.05 0.03 0.03 0.02 0.01 0.01 0.01 0.01 0.01 0.00 0.01 0.00 0.00 0.00

2.24 2.49 3.73 6.20 9.62 12.81 13.49 13.03 12.11 9.32 6.65 4.25 2.35 0.97 0.39 0.18 0.09 0.04 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.49 1.96 3.27 8.45 12.37 15.53 13.02 12.37 9.16 7.14 5.23 4.14 2.34 2.56 1.09 0.60 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

11.99 14.31 15.33 12.16 10.44 9.06 6.98 5.21 3.89 2.92 2.21 1.32 1.27 0.86 0.65 0.43 0.36 0.21 0.16 0.12 0.06 0.05 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

7.96 10.74 12.94 10.90 8.54 7.92 7.95 6.74 6.56 5.53 4.12 3.23 2.63 1.73 1.03 0.75 0.39 0.17 0.09 0.04 0.02 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13
0.00 5.26 10.53 10.53 0.00 5.26 31.58 5.26 5.26 5.26 5.26 10.53 5.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

120

Mean Axle Load, lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 98. Single-axle load distribution factors, Site 021-w334 SB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

6.23 14.39 11.74 10.81 9.04 9.58 10.43 6.66 5.79 5.12 3.53 2.33 1.58 1.18 0.82 0.36 0.19 0.10 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

6.55 9.97 13.49 15.40 13.12 11.05 8.80 6.36 4.16 3.06 2.30 1.68 1.43 1.06 0.64 0.37 0.27 0.14 0.07 0.05 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.26 0.69 1.77 3.54 8.51 16.22 21.24 18.09 9.40 5.46 5.10 3.94 2.48 1.55 0.82 0.52 0.29 0.08 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.62 1.85 0.00 1.85 3.09 4.32 9.88 15.43 8.02 9.88 13.58 19.75 6.17 4.32 0.62 0.00 0.00 0.62 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

16.25 18.64 12.85 9.49 8.71 9.71 7.25 4.42 2.99 2.42 1.90 1.60 1.26 0.95 0.65 0.42 0.18 0.10 0.07 0.05 0.04 0.03 0.03 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.23 2.35 2.44 4.42 9.48 18.66 24.21 20.97 9.27 2.41 1.43 0.97 0.57 0.30 0.15 0.06 0.04 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.28 0.77 1.95 6.72 17.43 26.55 22.48 14.16 5.53 2.47 1.04 0.42 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.44 7.63 8.22 8.66 15.58 19.75 12.45 7.57 5.43 3.93 2.58 2.20 1.79 1.02 0.47 0.18 0.04 0.02 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3.36 10.10 19.29 16.14 12.29 15.59 12.61 6.36 2.28 1.15 0.33 0.24 0.10 0.09 0.01 0.03 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13
8.62 8.62 8.62 8.62 20.69 15.52 10.34 12.07 1.72 3.45 0.00 1.72 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

121

Mean Axle Load,
lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 99. Single-axle load distribution factors, Site 127-0312 NB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

2.93 14.39 4.05 3.03 5.32 8.53 10.86 11.41 8.74 7.53 6.57 4.45 2.76 2.07 1.59 1.42 1.18 0.75 0.43 0.39 0.25 0.14 0.17 0.16 0.16 0.11 0.09 0.08 0.06 0.05 0.05 0.03 0.05 0.03 0.01 0.01 0.00 0.00 0.00

6.62 10.20 10.72 10.54 11.94 10.31 8.25 7.16 6.10 4.79 3.86 2.82 2.05 1.48 1.03 0.74 0.53 0.33 0.20 0.11 0.07 0.04 0.03 0.02 0.01 0.01 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.27 0.73 1.35 2.88 4.87 8.18 14.93 24.42 22.71 7.23 3.91 2.89 1.97 1.44 0.92 0.54 0.32 0.22 0.11 0.04 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.90 0.90 1.35 1.35 0.00 3.60 4.95 6.76 10.81 8.11 9.91 12.16 8.56 7.66 8.56 7.21 4.05 2.25 0.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

14.91 20.41 16.75 9.68 7.43 6.24 6.17 4.90 3.32 2.18 1.81 1.44 1.17 0.97 0.78 0.60 0.46 0.30 0.17 0.11 0.07 0.05 0.03 0.03 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.95 2.33 3.83 3.57 5.03 7.52 13.05 25.71 26.76 6.19 1.32 0.97 1.08 1.07 0.48 0.11 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.55 1.84 2.68 3.29 6.42 10.28 16.26 23.02 20.65 7.94 3.28 2.15 0.98 0.44 0.15 0.03 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.36 1.54 5.91 12.07 6.51 8.12 14.72 14.54 8.74 6.47 5.60 4.58 3.71 2.78 1.85 1.20 0.73 0.35 0.15 0.04 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.42 1.92 6.59 14.35 11.21 8.13 9.16 10.13 10.45 9.36 8.26 5.11 2.65 1.39 0.60 0.19 0.06 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13
2.44 3.88 5.03 4.17 4.45 8.76 8.05 22.56 16.95 11.49 8.05 2.73 0.72 0.14 0.43 0.00 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

122

Mean Axle Load, lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 100. Single-axle load distribution factors, Site 127-0312 SB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

7.58 16.88 14.31 8.13 7.11 6.75 5.43 5.20 5.34 4.91 4.15 3.17 2.77 2.45 1.65 1.36 0.77 0.57 0.39 0.27 0.21 0.17 0.08 0.03 0.03 0.06 0.02 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00

6.88 11.73 14.36 12.81 10.89 9.69 7.44 5.67 4.65 3.59 2.88 2.19 1.67 1.40 1.02 0.77 0.56 0.40 0.29 0.21 0.18 0.11 0.10 0.07 0.07 0.05 0.04 0.03 0.03 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00

0.46 1.11 2.32 4.61 9.99 16.26 17.64 13.49 9.13 6.57 5.11 3.73 2.83 2.18 1.50 1.02 0.60 0.45 0.29 0.20 0.13 0.10 0.09 0.06 0.03 0.01 0.03 0.02 0.01 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

6.16 2.90 1.09 1.45 1.45 3.99 5.80 3.99 7.25 8.33 9.42 9.78 7.97 10.51 3.99 6.52 2.90 1.09 1.81 1.09 0.72 0.72 0.00 0.36 0.00 0.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.36 0.00

16.44 19.73 13.95 9.77 8.01 7.04 5.63 4.34 3.39 2.66 2.14 1.70 1.32 1.08 0.81 0.59 0.48 0.29 0.18 0.14 0.08 0.07 0.05 0.03 0.03 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.89 1.51 1.72 3.21 8.58 17.42 22.36 18.90 11.35 5.61 3.30 2.11 1.40 0.89 0.45 0.20 0.08 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.49 1.65 1.60 4.26 11.70 19.20 20.92 17.62 10.01 6.07 2.59 1.65 1.20 0.49 0.19 0.09 0.11 0.11 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.18 0.69 1.74 3.80 7.84 12.95 14.28 11.97 10.11 8.45 7.28 5.99 4.63 3.77 2.40 1.72 1.01 0.59 0.31 0.15 0.09 0.02 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.36 1.55 4.13 7.05 10.46 14.02 15.61 14.78 11.91 8.09 5.20 3.08 1.79 1.02 0.47 0.23 0.11 0.06 0.03 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13
7.95 12.77 10.12 6.99 11.57 15.18 12.53 10.60 2.89 4.34 1.93 0.24 1.20 0.72 0.72 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

123

Mean Axle Load, lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 101. Single-axle load distribution factors, Site 051-0387 NB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

3.65

4.27

0.11

0.33 15.16 0.81

0.60

0.32

0.26

7.63

5.66

0.28

0.50 18.85 1.54

1.13

2.75

1.87

9.62

7.92

0.74

2.00 15.96 1.99

1.71

3.91

4.52

7.85

9.26

1.88

2.00 10.31 2.32

5.57

8.20

8.01

7.09 10.95 5.10

3.33

7.41

5.90 15.80 11.10 10.69

7.48 10.79 8.78

3.66

5.58 13.49 16.79 9.33 11.43

6.72

9.28

8.15

4.49

4.85 10.04 8.54 11.45 12.16

6.68

8.25 12.25 4.16

4.38 13.25 10.55 12.18 11.85

6.86

7.14 15.16 6.32

3.77 18.83 12.10 9.66 11.46

6.60

5.95 12.14 8.82

2.97 12.20 9.20

8.02

9.95

6.26

4.95 10.04 9.32

2.36

8.34

7.16

6.34

7.91

4.67

3.79

7.43

9.32

1.82

5.55

4.64

4.89

4.84

3.85

2.77

4.76

8.82

1.35

3.10

2.88

3.72

2.69

3.43

2.34

3.63

5.66

1.08

1.85

1.53

2.81

1.28

2.87

1.73

2.57

3.66

0.91

0.61

0.91

1.99

0.55

2.29

1.44

1.81

4.99

0.71

0.14

0.49

1.41

0.26

1.71

1.09

1.48

3.49

0.63

0.04

0.23

0.90

0.13

1.26

0.75

1.11

4.49

0.50

0.01

0.12

0.51

0.07

1.00

0.50

0.82

5.16

0.41

0.00

0.04

0.28

0.03

0.89

0.40

0.68

3.33

0.34

0.00

0.02

0.12

0.02

0.65

0.25

0.36

2.33

0.28

0.00

0.00

0.06

0.01

0.40

0.16

0.32

0.83

0.19

0.00

0.00

0.02

0.00

0.23

0.10

0.16

1.16

0.12

0.00

0.00

0.01

0.00

0.11

0.05

0.11

1.00

0.05

0.00

0.00

0.00

0.00

0.04

0.03

0.06

0.33

0.02

0.00

0.00

0.00

0.00

0.00

0.02

0.02

0.50

0.01

0.00

0.00

0.00

0.00

0.01

0.01

0.04

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
5.94 8.90 6.39 9.93 23.63 15.64 5.71 4.79 6.74 5.82 4.22 1.03 0.46 0.68 0.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

124

Mean Axle Load,
lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 102. Single-axle load distribution factors, Site 051-0387 SB.
Vehicle Classification

4

5

6

7

8

9

10 11 12 13

1.78 11.06 8.88 6.40 5.40 7.32 8.50 8.53 7.31 6.12 5.83 5.13 4.44 3.62 2.85 2.11 1.60 1.04 0.70 0.45 0.32 0.17 0.15 0.08 0.04 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3.83 6.52 6.44 8.37 12.67 12.25 10.19 8.75 7.31 5.67 4.62 3.63 2.85 2.29 1.55 1.05 0.73 0.43 0.30 0.17 0.10 0.07 0.03 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.38 0.88 1.38 1.15 1.99 4.48 10.34 18.32 19.87 12.19 7.99 5.78 4.25 3.00 2.24 1.79 1.43 1.09 0.68 0.41 0.18 0.07 0.04 0.02 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.09 0.55 0.82 0.82 0.82 2.46 4.92 3.83 5.19 9.02 7.65 7.38 10.11 9.56 8.20 7.92 6.83 4.92 2.73 2.19 1.91 0.55 0.27 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.09

15.72 21.65 15.34 8.39 6.16 5.30 6.03 5.50 3.98 2.67 2.03 1.61 1.27 1.08 0.86 0.67 0.54 0.38 0.27 0.20 0.13 0.08 0.06 0.03 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.39 1.06 1.81 1.75 1.82 3.09 8.49 23.02 31.60 14.26 4.94 2.90 2.47 1.63 0.57 0.15 0.04 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.38 1.90 2.12 1.08 1.59 5.14 13.10 23.11 23.26 13.20 6.77 3.76 2.25 1.28 0.59 0.18 0.14 0.08 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.11 0.34 0.79 2.31 2.82 4.14 11.24 14.82 11.43 9.98 9.94 8.89 7.70 6.07 4.31 2.61 1.47 0.63 0.27 0.10 0.03 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.11

0.19 0.67 1.92 6.62 8.11 7.04 8.00 10.65 13.51 14.96 13.64 8.07 3.93 1.64 0.64 0.26 0.10 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.66 2.73 3.99 8.18 8.86 14.51 13.05 15.48 12.27 8.57 4.19 1.85 0.78 1.66 1.56 0.19 0.19 0.10 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

125

Mean Axle Load,
lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 103. Single-axle load distribution factors, Site 217-0218 EB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

13

3.21 4.19 5.05 7.27 10.12 10.48 9.67 9.37 8.56 6.74 6.05 4.92 3.72 3.03 2.18 1.63 1.19 0.82 0.66 0.42 0.27 0.15 0.11 0.05 0.07 0.03 0.02 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

10.43 14.76 13.99 12.45 9.95 8.57 6.52 4.62 3.86 3.14 2.34 1.70 1.43 1.34 1.29 0.89 0.61 0.47 0.41 0.35 0.36 0.21 0.17 0.09 0.03 0.01 0.01 0.01 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

4.40 7.25 6.83 5.72 5.23 3.98 6.97 10.14 12.02 6.96 7.09 7.29 5.21 2.89 2.13 1.47 1.10 0.86 0.70 0.66 0.53 0.32 0.16 0.06 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3.67 7.33 8.31 17.11 3.42 1.96 1.47 1.47 3.91 2.44 4.65 4.65 5.38 3.91 6.60 8.80 6.36 1.71 2.20 0.98 1.22 1.96 0.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

18.32 19.79 15.53 11.04 5.96 4.46 5.18 4.43 3.47 2.94 2.60 1.77 1.32 0.93 0.64 0.45 0.36 0.26 0.20 0.15 0.10 0.06 0.04 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

23.83 8.74 4.89 4.55 5.72 2.05 4.63 10.58 15.03 5.66 3.74 5.13 3.86 1.20 0.27 0.08 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

26.91 10.72 3.73 4.85 3.69 2.88 6.82 10.07 10.49 6.26 4.64 4.62 3.09 0.66 0.34 0.16 0.05 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

14.51 9.96 8.01 7.62 3.41 3.59 8.00 9.39 6.26 6.16 6.24 5.39 4.37 3.29 2.09 1.03 0.38 0.15 0.08 0.05 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13.46 13.92 6.11 5.38 6.05 6.17 5.71 6.78 9.60 9.32 7.79 4.78 2.77 1.48 0.52 0.13 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

5.54 6.64 2.82 4.23 5.49 4.44 2.77 2.51 4.44 10.61 10.92 16.20 12.28 5.54 1.52 0.84 0.99 0.31 0.73 0.16 0.05 0.10 0.05 0.47 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31 0.00 0.00 0.00 0.00 0.00 0.00

126

Mean Axle Load,
lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 104. Single-axle load distribution factors, Site 217-0218 WB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

3.21 4.19 5.05 7.27 10.12 10.48 9.67 9.37 8.56 6.74 6.05 4.92 3.72 3.03 2.18 1.63 1.19 0.82 0.66 0.42 0.27 0.15 0.11 0.05 0.07 0.03 0.02 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

10.43 14.76 13.99 12.45 9.95 8.57 6.52 4.62 3.86 3.14 2.34 1.70 1.43 1.34 1.29 0.89 0.61 0.47 0.41 0.35 0.36 0.21 0.17 0.09 0.03 0.01 0.01 0.01 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

4.40 7.25 6.83 5.72 5.23 3.98 6.97 10.14 12.02 6.96 7.09 7.29 5.21 2.89 2.13 1.47 1.10 0.86 0.70 0.66 0.53 0.32 0.16 0.06 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3.67 7.33 8.31 17.11 3.42 1.96 1.47 1.47 3.91 2.44 4.65 4.65 5.38 3.91 6.60 8.80 6.36 1.71 2.20 0.98 1.22 1.96 0.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

18.32 19.79 15.53 11.04 5.96 4.46 5.18 4.43 3.47 2.94 2.60 1.77 1.32 0.93 0.64 0.45 0.36 0.26 0.20 0.15 0.10 0.06 0.04 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.50 2.88 5.68 8.89 9.33 11.89 12.30 12.91 9.70 8.73 5.28 3.94 2.44 2.03 1.01 0.89 0.28 0.20 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

26.91 10.72 3.73 4.85 3.69 2.88 6.82 10.07 10.49 6.26 4.64 4.62 3.09 0.66 0.34 0.16 0.05 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

14.51 9.96 8.01 7.62 3.41 3.59 8.00 9.39 6.26 6.16 6.24 5.39 4.37 3.29 2.09 1.03 0.38 0.15 0.08 0.05 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13.46 13.92 6.11 5.38 6.05 6.17 5.71 6.78 9.60 9.32 7.79 4.78 2.77 1.48 0.52 0.13 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13
5.54 6.64 2.82 4.23 5.49 4.44 2.77 2.51 4.44 10.61 10.92 16.20 12.28 5.54 1.52 0.84 0.99 0.31 0.73 0.16 0.05 0.10 0.05 0.47 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.31 0.00 0.00 0.00 0.00 0.00 0.00

127

Mean Axle Load,
lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 105. Single-axle load distribution factors, Site 051-0368 EB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

13

0.33 1.40 3.66 7.72 16.77 13.64 8.06 7.50 7.39 7.06 8.69 5.70 5.08 3.00 1.42 0.90 0.70 0.33 0.26 0.13 0.09 0.04 0.04 0.03 0.03 0.01 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.00

1.62 3.88 6.50 9.30 15.43 13.00 8.84 6.50 7.94 4.42 5.60 4.87 3.25 1.99 1.53 2.17 1.26 0.54 0.54 0.27 0.09 0.27 0.00 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.72 1.43 4.65 9.30 16.82 18.43 15.38 10.73 7.69 5.37 2.50 1.25 1.25 1.25 0.54 0.54 0.36 0.54 0.36 0.54 0.00 0.00 0.18 0.00 0.18 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

7.14 7.14 0.00 0.00 14.29 0.00 0.00 7.14 14.29 0.00 0.00 7.14 0.00 7.14 0.00 7.14 7.14 14.29 7.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

24.22 6.95 8.74 2.91 7.17 7.40 8.97 8.74 5.61 2.02 3.14 2.47 2.02 2.02 2.02 2.02 0.67 0.67 0.90 0.45 0.00 0.22 0.00 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.22 0.22 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.63 4.83 5.15 2.10 1.89 5.88 13.66 25.63 22.58 7.35 2.21 1.05 1.47 1.58 0.95 0.84 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.24 0.95 1.19 1.90 8.57 17.38 25.95 21.67 9.76 5.00 3.57 2.62 0.95 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.60 4.48 8.36 5.07 5.37 12.24 18.21 8.66 8.96 5.97 6.87 3.88 3.28 3.88 2.69 1.19 0.30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 5.13 7.69 25.64 15.38 7.69 5.13 5.13 5.13 10.26 5.13 0.00 5.13 2.56 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 50.00 25.00 25.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

128

Mean Axle Load,
lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 106. Single-axle load distribution factors, Site 051-0368 WB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

13

0.00 0.28 0.39 0.76 2.05 5.62 9.25 10.72 9.80 7.21 6.25 6.00 5.37 4.89 5.28 5.49 4.54 4.20 2.79 2.37 2.19 1.20 0.67 0.37 0.26 0.07 0.21 0.18 0.16 0.09 0.18 0.19 0.19 0.23 0.26 0.14 0.07 0.05 0.00

0.61 0.00 8.54 20.73 32.93 26.22 4.88 3.05 1.83 0.61 0.61 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 2.41 2.41 21.69 28.92 26.51 14.46 3.61 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 50.00 50.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

44.07 31.23 13.44 4.15 1.78 1.58 0.79 2.17 0.40 0.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.84 2.11 2.11 0.42 2.11 10.55 26.58 32.91 18.14 3.80 0.00 0.00 0.00 0.00 0.00 0.00 0.42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 2.00 0.00 2.00 8.00 20.00 31.00 29.00 7.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

9.09 0.00 0.00 0.00 9.09 18.18 36.36 18.18 9.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 11.11 77.78 11.11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

70.27 2.70 18.92 0.00 0.00 0.00 2.70 5.41 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

129

Mean Axle Load,
lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 107. Single-axle load distribution factors, Site 143-0126 EB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

3.72

5.03

0.87

7.67

8.94

1.50

0.35

0.30

0.62

5.81

8.15

1.41

11.08 12.04

2.88

0.41

1.31

2.73

5.13

10.15

1.94

7.67

11.63

5.68

0.97

4.12

8.44

4.59

11.70

2.86

5.11

9.04

8.89

1.19

3.71

8.86

4.74

11.19

5.86

5.97

9.07

9.33

4.55

5.37

7.53

5.74

9.89

11.10

6.82

10.36 11.89 11.62 10.82

9.25

6.86

8.44

16.55

6.53

9.21

12.30 22.67 14.43 11.25

6.77

6.95

17.66

4.26

6.89

12.91 23.33 12.52 13.28

6.45

5.55

11.56

3.41

5.40

9.70

14.54 10.05 13.66

6.90

3.95

6.35

4.55

3.89

8.73

8.89

8.07

10.54

7.79

3.30

4.29

5.11

2.94

5.28

4.40

7.26

6.69

7.60

2.82

3.57

5.68

2.24

3.94

2.61

6.10

3.38

5.61

2.45

3.00

4.83

1.88

2.44

1.35

5.10

1.93

4.45

2.10

2.51

5.97

1.56

2.03

1.00

4.11

0.98

3.67

1.77

2.15

5.11

1.28

1.01

0.66

2.92

0.50

3.10

1.56

1.75

2.84

1.03

0.89

0.41

1.83

0.21

2.65

1.27

1.51

2.84

0.77

0.28

0.35

1.09

0.11

2.10

0.99

1.33

1.14

0.56

0.20

0.19

0.51

0.04

1.42

0.76

1.00

1.42

0.39

0.12

0.22

0.22

0.01

1.28

0.54

0.81

0.57

0.27

0.00

0.19

0.10

0.00

0.99

0.41

0.64

0.28

0.19

0.00

0.13

0.03

0.00

0.82

0.31

0.46

0.28

0.13

0.00

0.00

0.02

0.00

0.71

0.22

0.30

0.57

0.09

0.00

0.00

0.00

0.00

0.40

0.16

0.21

0.00

0.05

0.00

0.00

0.00

0.00

0.24

0.11

0.12

0.28

0.04

0.00

0.00

0.00

0.00

0.10

0.06

0.06

0.00

0.03

0.00

0.00

0.00

0.00

0.13

0.05

0.04

0.00

0.02

0.00

0.00

0.00

0.00

0.06

0.04

0.04

0.00

0.01

0.00

0.00

0.00

0.00

0.04

0.02

0.01

0.00

0.01

0.00

0.00

0.00

0.00

0.03

0.01

0.01

0.00

0.01

0.00

0.00

0.00

0.00

0.02

0.01

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.03

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
13.18 16.40 11.90 11.58 7.40 7.07 8.68 5.14 8.04 7.07 0.64 0.96 0.32 0.32 0.64 0.32 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.32 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

130

Mean Axle Load,
lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 108. Single-axle load distribution factors, Site 143-0126 WB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

13

0.31 1.23 3.72 5.55 6.66 9.83 11.84 16.03 17.74 10.96 5.20 2.01 1.12 0.90 0.92 0.77 0.74 0.72 0.88 0.67 0.66 0.53 0.37 0.17 0.10 0.10 0.08 0.05 0.05 0.02 0.03 0.01 0.00 0.01 0.01 0.00 0.00 0.00 0.00

1.74 4.46 7.64 12.72 17.73 18.36 13.84 9.11 5.35 2.59 1.38 0.85 0.67 0.60 0.47 0.45 0.34 0.32 0.26 0.22 0.21 0.17 0.12 0.09 0.07 0.07 0.05 0.03 0.03 0.02 0.02 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00

0.16 0.56 1.80 4.07 5.45 9.56 18.10 25.14 16.13 8.17 3.25 1.51 1.03 1.02 0.95 0.76 0.66 0.51 0.41 0.31 0.20 0.12 0.05 0.03 0.03 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.34 1.34 2.34 3.34 2.34 3.34 8.70 15.72 21.40 17.73 12.04 3.34 3.01 0.67 0.67 0.33 1.00 0.00 0.00 0.33 0.00 0.33 0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

7.81 12.49 16.90 16.52 11.78 9.32 9.37 7.40 4.20 1.47 0.64 0.42 0.33 0.27 0.24 0.18 0.15 0.10 0.10 0.07 0.05 0.04 0.03 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.50 2.88 5.68 8.89 9.33 11.89 12.30 12.91 9.70 8.73 5.28 3.94 2.44 2.03 1.01 0.89 0.28 0.20 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.10 0.23 1.44 3.81 5.35 8.48 18.03 26.98 19.44 9.95 3.65 1.04 0.39 0.36 0.29 0.19 0.14 0.01 0.07 0.03 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.63 1.80 4.30 8.24 13.35 17.72 20.85 19.11 9.77 2.65 0.59 0.27 0.23 0.17 0.12 0.08 0.06 0.03 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.98 2.89 7.11 13.07 22.19 26.87 16.62 6.67 2.05 0.61 0.36 0.28 0.18 0.08 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

8.27 8.01 12.92 10.34 4.91 4.91 17.31 19.12 10.08 3.36 0.26 0.26 0.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

131

Mean Axle Load,
lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 109. Single-axle load distribution factors, Site 245-0218 EB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

1.49

8.61

0.97

3.73

12.28

2.20

5.12

12.94

4.48

5.92

12.15

6.11

7.76

10.59

7.07

8.35

8.94

8.69

8.66

7.18

9.03

10.31

5.73

8.82

10.29

4.88

8.19

8.73

3.91

7.50

7.60

3.08

6.39

5.73

2.28

6.04

4.86

1.85

4.86

4.11

1.34

4.18

2.50

1.03

3.49

1.77

0.78

2.79

1.13

0.62

2.33

0.73

0.48

1.80

0.38

0.37

1.48

0.28

0.24

1.11

0.17

0.19

0.83

0.09

0.14

0.61

0.09

0.12

0.44

0.05

0.08

0.24

0.02

0.04

0.17

0.02

0.04

0.10

0.00

0.04

0.05

0.02

0.03

0.02

0.00

0.01

0.00

0.00

0.01

0.01

0.02

0.01

0.00

0.00

0.01

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.53

17.24

2.16

1.50

3.90

6.46

0.81

20.20

3.66

2.88

5.81

8.20

1.98

16.18

6.56

5.68

8.05

9.75

3.66

10.54

8.13

8.89

8.78

10.93

4.72

7.80

9.08

9.33

9.73

11.40

5.47

5.97

10.91 11.89 10.77 10.80

5.28

4.84

12.05 12.30 10.16

9.40

6.20

3.92

12.22 12.91

9.04

8.69

6.84

3.20

11.37

9.70

7.78

6.90

6.53

2.37

8.67

8.73

6.38

5.48

6.98

1.87

5.90

5.28

5.19

4.58

7.40

1.47

3.61

3.94

4.26

2.97

7.12

1.23

2.32

2.44

3.15

1.76

6.00

0.92

1.56

2.03

2.57

1.19

6.23

0.64

0.94

1.01

1.65

0.66

5.95

0.49

0.52

0.89

1.07

0.38

4.80

0.31

0.24

0.28

0.70

0.24

4.08

0.21

0.06

0.20

0.48

0.11

2.82

0.17

0.01

0.12

0.23

0.04

2.71

0.13

0.01

0.00

0.16

0.04

1.54

0.08

0.00

0.00

0.06

0.01

0.87

0.06

0.00

0.00

0.03

0.01

0.67

0.04

0.00

0.00

0.03

0.01

0.36

0.02

0.00

0.00

0.01

0.00

0.11

0.02

0.00

0.00

0.00

0.00

0.14

0.02

0.00

0.00

0.00

0.00

0.06

0.01

0.00

0.00

0.00

0.00

0.06

0.01

0.00

0.00

0.00

0.00

0.06

0.01

0.00

0.00

0.00

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
5.73 7.48 9.97 6.74 8.36 10.18 8.49 8.42 8.83 6.27 4.38 5.53 2.36 1.55 0.81 0.81 1.89 0.34 0.88 0.07 0.40 0.54 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

132

Mean Axle Load, lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 110. Single-axle load distribution factors, Site 245-0218 WB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

1.49

3.13

0.09

0.29

8.39

2.16

0.24

0.18

0.39

3.73

5.70

0.92

0.19 13.28 3.66

1.98

0.53

0.60

5.12

5.51

1.34

0.38 13.32 6.56

2.07

0.69

1.22

5.92

6.42

0.94

1.72

9.23

8.13

1.56

1.30

3.06

7.76

8.54

1.61

1.24

8.61

9.08

2.34

5.07 10.50

8.35

7.68

4.58

1.82

8.76 10.91 6.80 10.37 14.48

8.66

6.38

9.12

1.82

8.16 12.05 17.97 13.35 10.19

10.31 6.52 12.28 3.92

6.59 12.22 27.74 11.51 10.05

10.29 6.34

9.62

6.32

5.05 11.37 18.87 9.31 12.06

8.73

5.28

5.83

8.61

3.71

8.67

8.87

9.03 11.96

7.60

4.39

4.76

8.42

3.07

5.90

4.22

9.12

9.93

5.73

3.72

3.96 12.25 2.40

3.61

2.43

8.14

7.05

4.86

3.26

3.40 11.20 1.91

2.32

1.56

7.05

4.54

4.11

2.97

3.28

9.38

1.51

1.56

0.93

5.61

2.39

2.50

2.59

2.87 10.62 1.09

0.94

0.90

4.00

1.04

1.77

2.30

2.91

7.46

0.89

0.52

0.60

2.45

0.39

1.13

2.13

2.93

7.08

0.73

0.24

0.36

1.32

0.11

0.73

1.92

2.68

2.87

0.60

0.06

0.24

0.57

0.02

0.38

1.70

2.72

1.91

0.52

0.01

0.18

0.22

0.01

0.28

1.58

2.74

1.15

0.44

0.01

0.06

0.11

0.01

0.17

1.41

2.70

0.29

0.36

0.00

0.03

0.04

0.00

0.09

1.25

2.58

0.57

0.29

0.00

0.06

0.01

0.00

0.09

1.20

2.65

0.29

0.23

0.00

0.00

0.00

0.00

0.05

1.11

2.36

0.19

0.18

0.00

0.00

0.00

0.00

0.02

1.07

2.31

0.00

0.14

0.00

0.00

0.00

0.00

0.02

0.95

2.10

0.00

0.11

0.00

0.00

0.00

0.00

0.00

0.88

1.79

0.00

0.07

0.00

0.00

0.00

0.00

0.02

0.79

1.52

0.00

0.06

0.00

0.00

0.00

0.00

0.00

0.65

1.07

0.00

0.05

0.00

0.00

0.00

0.00

0.00

0.54

0.87

0.00

0.05

0.00

0.00

0.00

0.00

0.02

0.49

0.61

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.38

0.43

0.00

0.03

0.00

0.00

0.00

0.00

0.02

0.31

0.21

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.24

0.15

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.21

0.06

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.18

0.02

0.00

0.02

0.00

0.00

0.00

0.00

0.02

0.15

0.00

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.14

0.00

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
0.38 1.10 1.63 3.73 11.26 16.75 24.56 21.29 11.74 5.11 1.28 0.41 0.20 0.18 0.26 0.05 0.03 0.03 0.00 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

133

Mean Axle Load, lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 111. Single-axle load distribution factors, Site 175-0247 EB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

1.63 4.57 7.70 9.77 10.11 10.00 8.61 8.24 7.51 6.50 6.14 4.72 3.59 3.04 2.16 1.68 1.14 0.91 0.64 0.43 0.30 0.20 0.09 0.06 0.09 0.07 0.03 0.05 0.01 0.02 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00

6.62 8.26 9.63 11.84 12.23 10.93 8.76 7.31 5.95 4.43 3.54 2.85 1.96 1.59 1.10 0.85 0.63 0.43 0.35 0.22 0.18 0.12 0.08 0.06 0.03 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

5.44 2.33 2.74 3.40 5.36 11.92 19.39 18.44 12.13 8.49 4.02 1.98 1.15 1.03 0.74 0.42 0.30 0.26 0.20 0.09 0.06 0.07 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

28.63 1.76 0.00 1.76 3.52 2.20 3.52 3.96 5.29 3.52 5.29 6.61 5.73 6.17 5.73 3.96 3.96 3.08 0.88 1.76 2.20 0.00 0.00 0.00 0.00 0.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

12.46 14.92 11.85 7.39 6.33 7.65 8.28 7.13 5.70 3.49 2.38 1.82 1.52 1.35 1.15 1.08 0.96 0.84 0.76 0.72 0.59 0.46 0.36 0.28 0.19 0.13 0.08 0.05 0.03 0.01 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.85 3.47 3.78 2.98 4.85 12.41 21.45 21.49 13.34 7.61 3.73 1.35 0.72 0.47 0.26 0.13 0.06 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.75 1.37 2.45 2.85 6.51 16.70 25.31 20.71 11.47 7.19 3.07 0.85 0.36 0.23 0.10 0.04 0.02 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.12 2.91 5.56 6.99 7.38 9.67 11.82 10.69 9.71 7.78 6.58 5.40 4.23 3.31 2.27 1.41 0.95 0.60 0.32 0.19 0.07 0.03 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3.48 7.56 15.28 17.21 11.16 7.12 7.20 7.90 7.27 5.91 4.11 2.59 1.44 0.91 0.43 0.26 0.09 0.04 0.02 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

13
3.03 5.47 6.82 6.40 18.01 20.45 13.55 8.08 6.23 4.38 3.87 1.35 1.60 0.76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

134

Mean Axle Load, lb
3000 4000 5000 6000 7000 8000 9000 10,000 11,000 12,000 13,000 14,000 15,000 16,000 17,000 18,000 19,000 20,000 21,000 22,000 23,000 24,000 25,000 26,000 27,000 28,000 29,000 30,000 31,000 32,000 33,000 34,000 35,000 36,000 37,000 38,000 39,000 40,000 41,000

Table 112. Single-axle load distribution factors, Site 175-0247 WB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

1.40

5.33

2.23

3.70 11.38 1.57

2.10

1.55

2.28

2.91

8.87

2.55

3.17 12.51 2.21

2.88

3.54

5.70

4.53 12.60 2.84

3.70 13.22 3.44

4.65

5.80

7.73

5.60 13.09 3.74

2.65 11.49 5.28

8.74

7.44

9.51

6.95 12.09

6.95

4.23

10.10

9.15

14.12 9.76 11.53

8.74 10.72 10.15 2.12

8.13 13.37 17.90 11.29 13.93

9.68

9.09 10.01 3.70

6.17 12.61 15.24 9.31 12.10

9.27

6.91

8.87

4.76

5.28 10.80 10.48 8.27

9.85

8.02

5.20

9.42

4.76

4.81 10.07 7.52

8.65

8.18

6.70

4.08

9.42

7.94

4.31

9.26

4.94

8.18

6.66

6.09

3.21

9.82

5.82

3.65

8.15

4.00

7.53

5.30

5.61

2.25

8.47

6.35

2.65

6.30

3.18

5.58

3.10

5.05

1.74

6.44

5.82

1.66

4.29

2.19

4.01

2.16

3.84

1.37

4.41

5.29

1.02

2.38

1.26

2.93

1.11

3.33

0.96

2.00

6.88

0.72

0.85

0.53

2.07

0.47

2.71

0.65

0.92

7.41

0.56

0.20

0.16

1.42

0.23

2.23

0.45

0.48

4.23

0.46

0.05

0.07

0.93

0.08

1.74

0.31

0.38

4.23

0.40

0.01

0.01

0.61

0.02

1.29

0.32

0.21

5.29

0.37

0.00

0.03

0.45

0.03

1.11

0.20

0.19

2.12

0.25

0.00

0.00

0.27

0.01

0.71

0.12

0.16

1.59

0.23

0.00

0.00

0.15

0.00

0.64

0.13

0.09

1.59

0.16

0.00

0.00

0.09

0.00

0.63

0.08

0.09

0.53

0.12

0.00

0.00

0.08

0.00

0.38

0.06

0.07

1.06

0.09

0.00

0.00

0.05

0.00

0.26

0.06

0.04

0.00

0.07

0.00

0.00

0.02

0.00

0.23

0.03

0.02

0.53

0.06

0.00

0.00

0.01

0.00

0.12

0.02

0.02

0.53

0.03

0.00

0.00

0.00

0.00

0.06

0.03

0.01

0.00

0.02

0.00

0.00

0.00

0.00

0.07

0.00

0.00

0.00

0.02

0.00

0.00

0.00

0.00

0.06

0.01

0.00

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.03

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.02

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
4.84 5.65 8.06 3.23 8.87 16.13 12.90 6.45 5.65 4.84 7.26 4.03 5.65 3.23 1.61 1.61 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

135

NORMALIZED AXLE LOAD SPECTRA TANDEM AXLES

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 113. Tandem-axle load distribution factors, Site 185-0227 NB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.38

0.00 15.63 1.16 14.14 5.23

1.43 40.00 2.59

0.96

0.00 39.11 2.60 14.13 9.38

2.13

0.00

6.25

1.47

0.00

10.29

3.90

17.84 14.58

5.54

0.00 11.70

2.33

0.00

3.76

6.21 15.96 15.80 8.87

0.00 21.33

4.17

0.00

3.60

8.38 11.26 9.94 12.93 0.00 10.33

7.17

0.00

3.54

9.97 10.02 5.94 14.06 0.00 13.26

9.88

0.00

2.92

6.50

6.97

4.84 10.72 0.00 18.45

10.69 0.00

2.77

5.78

3.59

4.23

9.91 40.00 10.31

9.49

0.00

3.22

7.95

2.72

3.82 11.13 20.00 4.15

8.78

0.00

4.14

8.82

1.61

3.51

8.28

0.00

1.36

9.82

0.00

2.71

9.54

0.81

3.56

5.32

0.00

0.24

11.90 0.00

1.81

9.54

0.41

4.59

3.72

0.00

0.02

11.63 0.00

1.67

5.49

0.22

6.78

2.78

0.00

0.00

7.08

0.00

1.27

4.77

0.15

5.86

1.68

0.00

0.00

3.00

0.00

0.89

4.48

0.08

1.63

0.72

0.00

0.00

0.91

0.00

0.81

3.03

0.04

0.23

0.40

0.00

0.00

0.25

0.00

0.71

1.30

0.02

0.05

0.27

0.00

0.00

0.08

0.00

0.59

0.58

0.02

0.01

0.08

0.00

0.00

0.01

0.00

0.24

0.00

0.01

0.00

0.03

0.00

0.00

0.00

0.00

0.17

0.00

0.01

0.00

0.00

0.00

0.00

0.01

0.00

0.05

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
4.95 17.12 12.61 8.56 7.66 5.41 7.21 2.70 3.60 2.25 4.95 1.80 1.35 1.80 4.05 7.21 4.95 0.45 0.90 0.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

136

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 114. Tandem-axle load distribution factors, Site 185-0227 SB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.06

0.00 11.57 1.04

6.82

0.61

0.62 37.50 0.00

2.12

0.00 45.75 0.60

6.85

1.65

0.83

0.00

0.14

2.53

0.00

4.01

1.64 13.72 3.76

3.30 12.50 0.66

0.70

0.00

2.45

1.19 14.73 4.95

5.65

0.00

2.65

0.37

0.00

3.58

1.79 13.60 5.59

7.65

0.00

8.45

0.84

0.00

2.76

3.42 13.33 6.93 11.17 12.50 25.33

1.89

0.00

2.52

7.44 10.98 7.71 11.29 12.50 34.38

2.18

0.00

2.97

8.18

7.19

7.11

8.52 12.50 17.78

6.02

0.00

4.20

8.33

4.26

7.05

9.16

0.00

7.38

8.89

0.00

5.33 14.29 2.80

7.11 10.68 0.00

2.60

10.15 0.00

3.46 10.57 2.37

7.50 10.86 0.00

0.56

16.16 0.00

2.45

8.93

1.89

9.27

8.27

0.00

0.06

23.21 0.00

2.36

9.38

0.86 13.72 6.05 12.50 0.00

16.20 0.00

1.81

9.82

0.30 12.83 3.61

0.00

0.00

6.45

0.00

1.56

6.99

0.15

3.31

1.27

0.00

0.00

1.38

0.00

1.35

4.46

0.09

0.70

0.65

0.00

0.00

0.72

0.00

1.05

1.19

0.02

0.16

0.37

0.00

0.00

0.14

0.00

0.56

0.15

0.02

0.03

0.06

0.00

0.00

0.00

0.00

0.17

0.30

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.07

0.15

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.03

0.15

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
17.57 12.55 6.69 5.44 10.88 12.13 9.21 5.86 5.02 3.77 1.26 1.67 1.26 2.09 2.51 0.84 1.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

137

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 115. Tandem-axle load distribution factors, Site 285-0243 NB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.32

0.00 10.42 0.00 10.78 3.73

0.77

0.00

4.71

0.72

0.00 24.37 0.00 13.26 7.31

1.16

0.00

8.70

1.29

0.00

15.75

2.78

13.24 11.11

3.10

0.82 13.63

0.61

0.00

5.12

2.78 17.28 13.05 5.03

4.10 22.03

0.61

0.00

5.47

4.63 14.57 11.45 6.58

7.38 19.79

0.83

0.00

5.95

2.78 10.93 8.27 11.04 18.85 9.84

2.34

0.00

4.90

5.56

6.49

6.52 14.13 13.11 7.77

2.80

0.00

3.86

4.63

3.70

5.57 11.33 15.57 4.88

2.95

0.00

3.43

9.26

2.67

5.28 12.00 13.93 4.01

5.50

0.00

2.84

7.41

1.99

4.15

9.58

7.38

2.64

8.52

0.00

2.44

8.33

1.21

3.99

8.71

5.74

1.12

9.09

0.00

2.69

3.70

1.04

5.01

8.03

3.28

0.42

11.39 0.00

2.55

8.33

0.90

6.78

4.65

2.46

0.27

10.32 0.00

2.26

7.41

0.50

5.24

2.42

4.10

0.15

7.58

0.00

1.67

8.33

0.40

1.79

1.06

2.46

0.02

7.91

0.00

1.46

3.70

0.37

0.52

0.10

0.00

0.00

8.38

0.00

1.19

2.78

0.36

0.16

0.10

0.82

0.00

8.34

0.00

0.80

5.56

0.13

0.04

0.00

0.00

0.00

5.68

0.00

0.79

2.78

0.09

0.01

0.10

0.00

0.00

3.77

0.00

0.55

2.78

0.04

0.00

0.00

0.00

0.00

0.93

0.00

0.48

3.70

0.03

0.00

0.10

0.00

0.00

0.11

0.00

0.39

0.93

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.18

0.93

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.17

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.12

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.10

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.93

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
20.95 1.90 3.81 12.38 4.76 8.57 7.62 6.67 4.76 5.71 8.57 4.76 0.00 0.00 0.00 0.95 1.90 3.81 1.90 0.95 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

138

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 116. Tandem-axle load distribution factors, Site 285-0243 SB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.30

0.00 27.03 0.00

8.28

4.16

1.81

0.00

0.41

0.90

0.00 19.77 4.50 12.02 9.15

4.00 14.29 1.27

0.36

0.00

7.99

3.60 16.77 12.94 6.32

0.00

4.22

0.24

0.00

5.37

4.50 19.39 11.12 6.58

0.00 10.21

0.15

0.00

4.44

3.60 15.11 8.19 10.58 0.00 16.25

0.87

0.00

4.25

3.60 10.57 6.76 12.77 0.00 25.72

3.19

0.00

4.12

8.11

6.72

5.98

9.03 28.57 19.79

6.13

0.00

4.37

5.41

4.19

5.73

8.26

0.00 12.16

8.54

0.00

4.46 11.71 2.61

6.05

9.81 28.57 6.66

13.17 0.00

3.68

2.70

1.52

5.33

8.39

0.00

2.36

18.85 0.00

3.10

8.11

0.92

6.00

8.52

0.00

0.76

17.95 0.00

2.66

9.91

0.62

7.29

7.23 14.29 0.16

13.65 0.00

2.34

9.01

0.43

6.50

3.48

0.00

0.03

6.76

0.00

1.91

5.41

0.29

3.41

2.06 14.29 0.00

3.13

0.00

1.31

6.31

0.17

1.01

0.77

0.00

0.00

2.68

0.00

1.11

2.70

0.10

0.30

0.26

0.00

0.00

1.59

0.00

0.81

4.50

0.11

0.07

0.13

0.00

0.00

0.93

0.00

0.42

5.41

0.08

0.01

0.00

0.00

0.00

0.48

0.00

0.32

0.00

0.01

0.00

0.00

0.00

0.00

0.06

0.00

0.21

0.90

0.05

0.00

0.00

0.00

0.00

0.03

0.00

0.11

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.07

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.04

0.00

0.00

0.00

0.00

0.00

0.00

0.03

0.00

0.05

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.04

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
24.49 2.04 6.12 12.24 4.08 10.20 8.16 4.08 10.20 6.12 0.00 0.00 2.04 2.04 4.08 2.04 0.00 2.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

139

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 117. Tandem-axle load distribution factors, Site 021-w334 NB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

2.45

0.00

22.01

1.43

18.88 15.30

6.85 10.00 10.26

1.04

0.00

11.75

0.72

20.00 16.61

7.37 50.00 13.80

2.08

0.00

10.09

0.36

16.78 12.72

9.21

0.00 14.53

2.59

0.00

8.47

1.43 12.81 7.95 11.57 0.00 14.64

4.45

0.00

6.94

2.51

9.17

4.98 11.57 0.00 14.02

5.04

0.00

5.49

1.79

6.34

4.22 10.25 0.00 11.87

6.67

0.00

4.60

8.96

4.29

4.13

9.84 10.00 9.96

8.82

0.00

4.34

5.02

3.36

4.39

8.98 10.00 7.02

10.08 0.00

3.73

8.24

2.42

4.67

7.20

0.00

2.67

10.60 0.00

3.14

7.89

1.72

4.91

5.70 10.00 1.03

9.79

0.00

2.45 11.47 1.29

4.93

3.80

0.00

0.14

10.45 0.00

2.34

8.96

0.77

4.61

2.94

0.00

0.05

6.82

0.00

2.05

8.96

0.46

4.27

2.36

0.00

0.00

7.86

0.00

1.92

8.60

0.41

3.06

1.04

0.00

0.00

4.60

0.00

1.65

8.60

0.37

1.82

0.81

0.00

0.00

3.04

0.00

1.50

3.58

0.24

0.88

0.29

0.00

0.00

1.33

0.00

1.20

2.87

0.13

0.33

0.06 10.00 0.00

1.41

0.00

1.19

5.38

0.13

0.14

0.06

0.00

0.00

0.52

0.00

0.91

2.51

0.15

0.05

0.06

0.00

0.00

0.22

0.00

0.91

0.72

0.10

0.02

0.06

0.00

0.00

0.00

0.00

0.82

0.00

0.05

0.01

0.00

0.00

0.00

0.07

0.00

0.66

0.00

0.05

0.01

0.00

0.00

0.00

0.07

0.00

0.51

0.00

0.05

0.00

0.00

0.00

0.00

0.00

0.00

0.43

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.41

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.27

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.13

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.09

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.04

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
14.89 4.26 0.00 0.00 2.13 6.38 2.13 0.00 2.13 4.26 4.26 2.13 10.64 6.38 2.13 4.26 4.26 4.26 8.51 6.38 4.26 0.00 4.26 0.00 0.00 2.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

140

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 118. Tandem-axle load distribution factors, Site 021-w334 SB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

4.01

0.00 32.48 0.32 14.40 7.34

0.88 10.00 1.34

1.95

0.00

6.79

0.65 18.18 10.84 1.98

2.86 13.04

0.82

0.00

8.43

1.29 21.36 10.30 3.43

4.29 18.02

3.91

0.00

7.14

1.29 18.94 7.13

4.31 10.00 33.98

2.98

0.00

7.16

1.29 11.29 5.94

7.24

8.57 24.65

5.66

0.00

6.06

3.56

6.73

5.74

9.15

7.14

5.56

6.07

0.00

4.55

6.47

3.58

5.89 15.08 8.57

2.38

12.24 0.00

4.17

4.53

1.84

6.46 18.23 10.00 0.94

19.75 0.00

3.66 11.33 1.15

7.54 14.20 10.00 0.09

19.55 0.00

3.10 13.92 0.77

8.66

9.08

2.86

0.00

13.17 0.00

2.41 22.33 0.32

8.95

7.10 17.14 0.00

5.45

0.00

1.95 17.80 0.27

7.60

4.45

4.29

0.00

2.47

0.00

1.69

9.39

0.14

4.54

2.86

1.43

0.00

1.34

0.00

1.50

2.91

0.19

1.97

1.24

1.43

0.00

0.62

0.00

1.60

1.62

0.16

0.71

0.57

0.00

0.00

0.00

0.00

1.71

0.32

0.17

0.25

0.14

0.00

0.00

0.00

0.00

1.74

0.32

0.19

0.09

0.04

0.00

0.00

0.00

0.00

1.42

0.65

0.22

0.03

0.04

0.00

0.00

0.00

0.00

0.99

0.00

0.06

0.02

0.00

1.43

0.00

0.00

0.00

0.70

0.00

0.02

0.01

0.00

0.00

0.00

0.00

0.00

0.26

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.18

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.12

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.06

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.05

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
20.34 10.17 16.95 15.25 10.17 1.69 5.08 1.69 0.00 1.69 1.69 0.00 6.78 5.08 1.69 1.69 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

141

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 119. Tandem-axle load distribution factors, Site 127-0312 NB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.10

0.00 10.79 2.22 10.50 2.27

0.73 10.00 0.15

0.26

0.00 29.16 3.21

9.50

5.91

3.49 50.00 1.33

0.44

0.00 11.90 3.46 14.44 12.86 11.33 0.00

9.83

0.56

0.00

9.83

3.46 16.91 16.47 13.20 0.00 14.98

0.66

0.00

6.74

4.94 15.75 9.51 11.75 0.00

9.08

1.10

0.00

4.28

4.44 12.46 5.67 11.71 0.00 17.40

1.73

0.00

3.33

8.15

7.76

4.75 11.45 10.00 23.55

3.20

0.00

3.21

9.88

4.65

4.43

7.96 10.00 14.60

7.01

0.00

3.54 10.62 2.88

4.48

6.04

0.00

6.64

12.73 0.00

4.10 10.86 2.10

4.63

5.87 10.00 1.90

18.01 0.00

3.29

8.89

1.32

5.11

5.32

0.00

0.47

20.40 0.00

2.22

6.91

0.67

6.11

4.43

0.00

0.05

17.62 0.00

2.32

7.65

0.40

7.82

3.21

0.00

0.02

10.08 0.00

2.00

5.43

0.27

7.17

2.02

0.00

0.00

4.32

0.00

1.19

4.20

0.14

2.36

1.05

0.00

0.00

1.28

0.00

0.74

2.47

0.09

0.40

0.27

0.00

0.00

0.33

0.00

0.47

0.99

0.07

0.07

0.13 10.00 0.00

0.09

0.00

0.35

0.74

0.04

0.01

0.04

0.00

0.00

0.04

0.00

0.22

0.74

0.03

0.00

0.00

0.00

0.00

0.01

0.00

0.12

0.49

0.01

0.00

0.00

0.00

0.00

0.03

0.00

0.07

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.03

0.25

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
5.25 15.75 29.75 9.80 8.40 6.30 4.67 5.95 3.38 1.87 2.92 1.05 1.40 1.75 1.17 0.23 0.23 0.00 0.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

142

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 120. Tandem-axle load distribution factors, Site 127-0312 SB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.53

0.00 30.63 3.10 12.07 2.44

0.44

0.00 14.10 3.34 13.26 5.11

0.34

0.00

7.49

2.39 15.86 7.25

0.28

0.00

5.90

4.30 14.98 8.22

0.22

0.00

4.62

3.82 11.78 8.34

0.84

0.00

4.13

8.59

8.74

8.38

2.75

0.00

4.33

9.31

6.11

8.01

5.50

0.00

4.75 10.02 4.30

7.82

10.03 0.00

4.57 11.93 3.27

8.11

12.41 0.00

4.03 10.50 2.59

8.75

15.66 0.00

3.54

7.16

2.09

9.02

14.31 0.00

2.91

6.92

1.73

7.79

12.47 0.00

2.34

5.73

1.20

5.60

7.66

0.00

1.66

3.58

0.78

3.12

6.09

0.00

1.33

3.34

0.50

1.35

3.38

0.00

0.99

1.91

0.32

0.47

2.66

0.00

0.72

0.72

0.16

0.14

1.72

0.00

0.54

1.19

0.14

0.04

1.00

0.00

0.38

0.72

0.05

0.01

0.53

0.00

0.31

0.48

0.01

0.00

0.41

0.00

0.25

0.00

0.00

0.00

0.34

0.00

0.13

0.48

0.02

0.00

0.09

0.00

0.15

0.00

0.01

0.00

0.28

0.00

0.09

0.00

0.00

0.00

0.06

0.00

0.05

0.00

0.00

0.00

0.00

0.00

0.03

0.24

0.00

0.00

0.00

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.24

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

2.59 10.00 0.19

6.03

2.86

1.91

6.71

4.29

5.79

7.85 10.00 13.63

8.61

8.57

23.95

9.18

7.14

23.90

8.72

8.57

15.81

8.70 10.00 8.63

8.81 10.00 4.05

8.72

2.86

1.50

7.96 17.14 0.47

6.18

4.29

0.15

5.28

1.43

0.03

2.85

1.43

0.01

1.01

0.00

0.00

0.57

0.00

0.00

0.18

0.00

0.00

0.04

0.00

0.00

0.00

1.43

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
4.72 9.45 17.86 20.12 16.22 10.47 5.54 1.85 2.26 2.26 1.85 1.64 1.23 1.85 1.03 0.62 0.41 0.41 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

143

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 121. Tandem-axle load distribution factors, Site 051-0387 NB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.38

0.00 11.11 0.53

8.49

2.00

1.96

0.87

0.16

1.16

0.00 16.29 2.30

9.91

5.16

4.54

0.87

1.90

1.63

0.00 10.06 2.30 12.16 8.24

8.23

2.17

5.39

1.10

0.00

7.02

3.81 14.55 9.67

9.80

4.78 13.09

1.01

0.00

5.44

5.04 14.16 9.51

9.39 10.87 14.62

1.70

0.00

5.36

8.58 12.45 8.51

9.73 14.78 17.91

2.62

0.00

5.01

7.79

9.14

7.70 10.20 16.52 19.68

4.05

0.00

4.86

8.50

6.41

7.39 10.87 7.83 14.91

5.43

0.00

4.64

9.47

4.25

8.58 12.30 6.96

8.52

6.77

0.00

4.43

9.20

2.88

7.79

8.45

5.65

2.87

9.16

0.00

4.07

8.32

1.95

6.05

5.63

7.39

0.75

12.21 0.00

4.20

5.49

1.27

5.76

3.94

7.83

0.17

12.49 0.00

4.28

6.19

0.84

6.39

2.48

6.96

0.02

11.75 0.00

3.34

5.22

0.57

4.83

1.35

4.35

0.00

10.97 0.00

2.76

4.69

0.38

1.77

0.53

2.17

0.00

8.93

0.00

2.47

4.34

0.21

0.49

0.27

0.00

0.00

5.36

0.00

1.72

2.92

0.18

0.13

0.12

0.00

0.00

1.98

0.00

1.08

2.04

0.08

0.03

0.12

0.00

0.00

0.80

0.00

0.63

1.06

0.05

0.01

0.05

0.00

0.00

0.38

0.00

0.45

1.24

0.04

0.00

0.02

0.00

0.00

0.07

0.00

0.31

0.44

0.02

0.00

0.00

0.00

0.00

0.04

0.00

0.18

0.09

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.13

0.27

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.08

0.09

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.04

0.09

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.38

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
4.22 10.61 21.22 13.06 8.03 5.03 4.35 7.48 15.78 8.03 0.95 0.54 0.14 0.14 0.27 0.14 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

144

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 122. Tandem-axle load distribution factors, Site 051-0387 SB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.03

0.00

5.67

1.22

9.19

0.90

0.93

2.41

0.19

0.81

0.00 27.16 2.43

8.70

2.67

2.55

4.82

0.51

1.63

0.00 10.59 2.13 11.67 5.54

8.75

7.23

3.50

0.61

0.00

5.69

3.95 16.95 7.95

8.78

2.41 10.32

0.26

0.00

4.71

3.50 15.71 8.41 10.01 4.82

6.50

0.46

0.00

5.17

4.41 12.15 8.10

9.28

8.43 14.17

0.66

0.00

4.79

7.90

8.70

8.08 10.70 14.46 26.10

1.64

0.00

5.03

6.69

5.93

7.26

8.84 12.05 23.85

3.69

0.00

5.66 10.33 3.91

6.68

7.97 10.84 10.87

7.96

0.00

5.61 10.03 2.52

6.47

8.05

7.23

3.16

13.24 0.00

4.77

8.81

1.64

6.98

8.11

8.43

0.66

17.00 0.00

3.77

8.51

1.01

8.68

7.68

7.23

0.15

17.38 0.00

3.01

6.69

0.61 11.03 4.38

4.82

0.02

12.98 0.00

2.40

7.14

0.46

8.02

2.20

1.20

0.00

8.97

0.00

1.95

4.56

0.27

2.55

0.87

0.00

0.00

5.26

0.00

1.47

5.32

0.22

0.53

0.43

0.00

0.00

3.59

0.00

0.99

3.65

0.13

0.11

0.27

0.00

0.00

1.83

0.00

0.65

0.91

0.10

0.02

0.09

0.00

0.00

1.23

0.00

0.44

0.76

0.04

0.00

0.09

2.41

0.00

0.58

0.00

0.22

0.76

0.04

0.00

0.01

0.00

0.00

0.16

0.00

0.13

0.15

0.02

0.00

0.00

1.20

0.00

0.01

0.00

0.07

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.15

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
3.44 7.58 8.76 11.91 16.93 18.80 10.24 5.51 3.44 4.43 0.79 3.15 0.89 0.20 0.30 1.18 0.69 1.08 0.49 0.20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

145

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 123. Tandem-axle load distribution factors, Site 217-0218 EB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

7.46

0.00 11.67 7.95 15.74 9.74

9.01 17.65 24.09

9.18

0.00 23.15 11.49 10.59 9.93 10.71 5.88

4.21

13.67 0.00 15.07 11.05 11.78 16.41 17.74 5.88

5.10

5.93

0.00

8.61

9.43 13.32 9.69 11.88 23.53 5.55

3.19

0.00

7.09

2.65 13.58 7.13

8.88

0.00

5.82

2.76

0.00

4.17

3.68 11.23 6.19

5.71

0.00 12.98

3.89

0.00

3.10

3.39

7.18

5.63

5.62

5.88 19.21

4.47

0.00

3.18

4.42

5.49

5.71

4.93

5.88 12.60

4.56

0.00

2.67

3.83

3.96

4.37

4.67

5.88

7.26

4.58

0.00

2.00

5.01

2.73

3.74

5.89

0.00

2.66

6.28

0.00

2.35

4.86

1.48

4.14

5.54

5.88

0.37

7.67

0.00

2.58

6.92

1.04

6.07

4.47

0.00

0.10

6.52

0.00

2.15

4.86

0.60

7.11

3.30

5.88

0.03

4.90

0.00

1.77

6.19

0.34

2.99

1.06 11.76 0.00

3.82

0.00

1.14

5.45

0.23

0.85

0.41

0.00

0.00

3.65

0.00

1.20

2.80

0.15

0.24

0.11

5.88

0.00

3.28

0.00

1.52

2.06

0.20

0.06

0.04

0.00

0.00

2.13

0.00

1.69

0.88

0.29

0.01

0.06

0.00

0.00

1.07

0.00

1.16

1.47

0.05

0.00

0.00

0.00

0.01

0.48

0.00

0.67

0.59

0.00

0.00

0.00

0.00

0.00

0.22

0.00

0.39

0.74

0.01

0.00

0.00

0.00

0.00

0.10

0.00

0.54

0.15

0.01

0.00

0.00

0.00

0.00

0.12

0.00

0.75

0.00

0.00

0.00

0.00

0.00

0.00

0.06

0.00

0.77

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.00

0.41

0.15

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.14

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.04

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
17.48 12.25 14.26 10.39 10.24 9.60 6.81 4.94 4.73 2.58 1.65 1.36 1.79 0.43 1.00 0.07 0.07 0.14 0.07 0.00 0.07 0.00 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

146

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 124. Tandem-axle load distribution factors, Site 217-0218 WB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

7.46

0.00 11.67 7.95 15.74 9.74

9.01 17.65 24.09

9.18

0.00 23.15 11.49 10.59 9.93 10.71 5.88

4.21

13.67 0.00 15.07 11.05 11.78 16.41 17.74 5.88

5.10

5.93

0.00

8.61

9.43 13.32 9.69 11.88 23.53 5.55

3.19

0.00

7.09

2.65 13.58 7.13

8.88

0.00

5.82

2.76

0.00

4.17

3.68 11.23 6.19

5.71

0.00 12.98

3.89

0.00

3.10

3.39

7.18

5.63

5.62

5.88 19.21

4.47

0.00

3.18

4.42

5.49

5.71

4.93

5.88 12.60

4.56

0.00

2.67

3.83

3.96

4.37

4.67

5.88

7.26

4.58

0.00

2.00

5.01

2.73

3.74

5.89

0.00

2.66

6.28

0.00

2.35

4.86

1.48

4.14

5.54

5.88

0.37

7.67

0.00

2.58

6.92

1.04

6.07

4.47

0.00

0.10

6.52

0.00

2.15

4.86

0.60

7.11

3.30

5.88

0.03

4.90

0.00

1.77

6.19

0.34

2.99

1.06 11.76 0.00

3.82

0.00

1.14

5.45

0.23

0.85

0.41

0.00

0.00

3.65

0.00

1.20

2.80

0.15

0.24

0.11

5.88

0.00

3.28

0.00

1.52

2.06

0.20

0.06

0.04

0.00

0.00

2.13

0.00

1.69

0.88

0.29

0.01

0.06

0.00

0.00

1.07

0.00

1.16

1.47

0.05

0.00

0.00

0.00

0.01

0.48

0.00

0.67

0.59

0.00

0.00

0.00

0.00

0.00

0.22

0.00

0.39

0.74

0.01

0.00

0.00

0.00

0.00

0.10

0.00

0.54

0.15

0.01

0.00

0.00

0.00

0.00

0.12

0.00

0.75

0.00

0.00

0.00

0.00

0.00

0.00

0.06

0.00

0.77

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.00

0.41

0.15

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.14

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.04

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
17.48 12.25 14.26 10.39 10.24 9.60 6.81 4.94 4.73 2.58 1.65 1.36 1.79 0.43 1.00 0.07 0.07 0.14 0.07 0.00 0.07 0.00 0.00 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

147

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 125. Tandem-axle load distribution factors, Site 051-0368 EB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.38

0.00

1.16

0.00

1.63

0.00

1.10

0.00

1.01

0.00

1.70

0.00

2.62

0.00

4.05

0.00

5.43

0.00

6.77

0.00

9.16

0.00

12.21 0.00

12.49 0.00

11.75 0.00

10.97 0.00

8.93

0.00

5.36

0.00

1.98

0.00

0.80

0.00

0.38

0.00

0.07

0.00

0.04

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.38

0.00

0.00

0.00

1.97

0.00

7.95

3.46

0.00

0.87

0.00

9.68

0.00

2.27

7.77

0.24

0.87

0.00

2.15

4.55 11.36 9.71

0.24

2.17

7.69

7.35

0.00

14.77 12.93

0.71

4.78 15.38

8.78

0.00

17.05 13.05

3.10 10.87 15.38

3.05

4.55

15.91

4.19

10.95 14.78 15.38

4.66

9.09

12.50

3.28

29.52 16.52 23.08

7.35

4.55

9.09

3.22 28.33 7.83

0.00

3.58 13.64 2.27

4.49 10.48 6.96

7.69

5.73

9.09

4.55

3.70

5.00

5.65

7.69

5.56

4.55

0.00

4.86

3.57

7.39

7.69

8.78

4.55

1.14

5.59

2.14

7.83

0.00

6.81

4.55

0.00

8.99

3.57

6.96

0.00

7.89 13.64 1.14

7.41

1.43

4.35

0.00

3.94 13.64 0.00

4.01

0.24

2.17

0.00

2.15

4.55

0.00

2.49

0.48

0.00

0.00

2.87

0.00

0.00

0.49

0.00

0.00

0.00

1.79

0.00

0.00

0.18

0.00

0.00

0.00

1.61

4.55

0.00

0.12

0.00

0.00

0.00

1.25

0.00

0.00

0.06

0.00

0.00

0.00

1.08

4.55

0.00

0.00

0.00

0.00

0.00

0.90

0.00

0.00

0.00

0.00

0.00

0.00

0.54

0.00

0.00

0.00

0.00

0.00

0.00

0.36

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.18

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
0.00 5.88 41.18 35.29 0.00 5.88 0.00 5.88 0.00 0.00 0.00 0.00 5.88 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

148

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 126. Tandem-axle load distribution factors, Site 051-0368 WB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.03

0.00

1.20

0.00

7.69

0.46

0.00

2.41

0.00

0.81

0.00 15.66 0.00

0.00

7.41

0.00

4.82

0.00

1.63

0.00

19.28

0.00

23.08 20.14

0.00

7.23 66.67

0.61

0.00

31.33

0.00

30.77 15.51

3.00

2.41 33.33

0.26

0.00 12.05 0.00

7.69

9.95 15.00 4.82

0.00

0.46

0.00

4.82

0.00

7.69 15.74 9.00

8.43

0.00

0.66

0.00

9.64 25.00 7.69 16.67 23.00 14.46 0.00

1.64

0.00

2.41 50.00 15.38 7.87 27.00 12.05 0.00

3.69

0.00

2.41

0.00

0.00

3.94 11.00 10.84 0.00

7.96

0.00

1.20 25.00 0.00

2.08

6.00

7.23

0.00

13.24 0.00

0.00

0.00

0.00

0.23

4.00

8.43

0.00

17.00 0.00

0.00

0.00

0.00

0.00

2.00

7.23

0.00

17.38 0.00

0.00

0.00

0.00

0.00

0.00

4.82

0.00

12.98 0.00

0.00

0.00

0.00

0.00

0.00

1.20

0.00

8.97

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

5.26

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

3.59

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

1.83

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

1.23

0.00

0.00

0.00

0.00

0.00

0.00

2.41

0.00

0.58

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.16

0.00

0.00

0.00

0.00

0.00

0.00

1.20

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
4.55 0.00 59.09 4.55 0.00 31.82 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

149

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 127. Tandem-axle load distribution factors, Site 143-0126 EB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.55

0.00 28.32 9.74

7.19

9.74

0.73

7.41

0.32

0.45

0.00 12.98 9.74 12.25 9.93

3.30

7.41

3.28

0.50

0.00

5.04

4.67 16.81 16.41 6.32 11.11 9.28

0.46

0.00

6.45

7.30 14.32 9.69

6.10

7.41

7.90

0.45

0.00

6.24

5.07 11.47 7.13

8.58 11.11 18.46

0.52

0.00

4.76

6.90

8.28

6.19

9.41 29.63 28.52

0.50

0.00

4.87

4.87

6.13

5.63

8.29 18.52 18.36

1.28

0.00

4.46

5.07

5.11

5.71

7.75

0.00

8.17

3.35

0.00

4.76

5.48

4.51

4.37

8.23

0.00

3.59

7.60

0.00

4.28

8.11

3.30

3.74 10.58 3.70

1.48

13.52 0.00

3.50

5.27

2.88

4.14 11.31 0.00

0.45

18.08 0.00

2.92

8.92

2.42

6.07

8.58

0.00

0.15

15.37 0.00

2.92

4.67

1.94

7.11

5.69

0.00

0.03

8.15

0.00

2.28

3.65

1.26

2.99

3.11

0.00

0.00

4.57

0.00

1.81

3.85

0.89

0.85

1.14

0.00

0.00

3.50

0.00

1.29

1.62

0.56

0.24

0.44

3.70

0.00

2.94

0.00

0.90

1.22

0.34

0.06

0.16

0.00

0.00

3.26

0.00

0.66

0.41

0.18

0.01

0.16

0.00

0.00

3.55

0.00

0.46

0.61

0.07

0.00

0.10

0.00

0.00

3.46

0.00

0.47

0.20

0.03

0.00

0.00

0.00

0.00

2.85

0.00

0.20

0.61

0.03

0.00

0.00

0.00

0.00

2.19

0.00

0.19

0.41

0.01

0.00

0.00

0.00

0.00

1.43

0.00

0.12

0.61

0.00

0.00

0.00

0.00

0.00

0.82

0.00

0.06

0.20

0.00

0.00

0.00

0.00

0.00

0.52

0.00

0.03

0.61

0.00

0.00

0.00

0.00

0.00

0.11

0.00

0.02

0.20

0.00

0.00

0.00

0.00

0.00

0.02

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
11.11 14.73 11.84 15.46 21.50 7.49 3.62 1.93 2.17 0.48 1.69 1.69 0.48 0.97 1.45 1.21 0.97 0.72 0.24 0.00 0.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

150

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 128. Tandem-axle load distribution factors, Site 143-0126 WB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.27

0.00 21.21 0.71 10.32 9.74

0.39 50.00 0.24

1.65

0.00 26.38 1.65 14.99 9.93

1.65

0.00

1.48

1.72

0.00 11.30 3.07 19.21 16.41 4.19

0.00

7.81

1.32

0.00

6.92

7.08 19.96 9.69

7.48

0.00 30.13

4.14

0.00

7.13

7.08 13.81 7.13 12.54 50.00 39.87

8.88

0.00

7.07 12.97 9.37

6.19 17.72 0.00 16.75

20.16 0.00

6.60 20.28 4.93

5.63 19.29 0.00

2.45

26.30 0.00

4.98 17.69 2.28

5.71 14.46 0.00

0.59

13.74 0.00

2.81 10.14 1.33

4.37

9.78

0.00

0.41

3.73

0.00

1.60 11.56 1.02

3.74

6.25

0.00

0.19

1.13

0.00

0.81

1.42

0.75

4.14

3.10

0.00

0.05

0.81

0.00

0.58

2.36

0.60

6.07

1.62

0.00

0.02

1.01

0.00

0.47

0.94

0.48

7.11

0.77

0.00

0.01

1.02

0.00

0.38

0.47

0.39

2.99

0.43

0.00

0.00

1.22

0.00

0.32

0.47

0.22

0.85

0.20

0.00

0.00

1.23

0.00

0.32

0.24

0.17

0.24

0.03

0.00

0.00

1.01

0.00

0.22

0.00

0.07

0.06

0.05

0.00

0.00

1.23

0.00

0.21

0.24

0.04

0.01

0.03

0.00

0.00

1.39

0.00

0.18

0.47

0.02

0.00

0.03

0.00

0.00

1.34

0.00

0.15

0.47

0.02

0.00

0.00

0.00

0.00

1.57

0.00

0.12

0.47

0.01

0.00

0.00

0.00

0.00

1.67

0.00

0.08

0.00

0.01

0.00

0.00

0.00

0.00

1.42

0.00

0.05

0.24

0.01

0.00

0.00

0.00

0.00

1.13

0.00

0.05

0.00

0.00

0.00

0.00

0.00

0.00

0.82

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.07

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
9.36 7.28 13.51 18.71 21.83 8.52 4.57 4.99 2.49 4.99 1.87 1.25 0.00 0.42 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

151

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 129. Tandem-axle load distribution factors, Site 245-0218 EB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.16

0.00 11.32 0.65 21.64 8.74

7.36 18.75 5.75

0.32

0.00

9.18

1.09 17.94 9.60

9.23

6.25 15.82

0.96

0.00

7.46

2.22 15.02 9.11

9.86

6.25 19.71

2.11

0.00

5.72

3.03 12.52 8.19 10.11 0.00 17.12

4.57

0.00

6.16

3.77

9.40

7.22

8.38

6.25 14.84

7.70

0.00

6.62

4.36

6.70

6.47

8.84

6.25 11.72

11.03 0.00

5.45

5.11

4.43

6.28

8.29

6.25

7.48

14.32 0.00

4.32

5.19

3.46

6.66

7.62

0.00

4.70

15.73 0.00

4.23

6.80

2.40

7.69

7.58

0.00

1.89

12.98 0.00

4.01

7.28

1.95

8.17

6.60

0.00

0.59

9.94

0.00

4.21

7.75

1.29

7.46

5.54

0.00

0.25

6.43

0.00

4.25

7.63

0.93

5.95

4.57

0.00

0.03

4.28

0.00

3.56

7.60

0.65

4.22

2.88

0.00

0.03

3.04

0.00

3.29

6.92

0.42

2.41

1.48

0.00

0.02

2.05

0.00

3.14

5.93

0.31

1.12

1.06 25.00 0.02

1.73

0.00

2.70

5.40

0.31

0.46

0.34

0.00

0.00

1.15

0.00

2.69

4.61

0.16

0.16

0.17

0.00

0.02

0.93

0.00

2.31

3.81

0.17

0.06

0.04

0.00

0.00

0.29

0.00

1.81

3.08

0.13

0.02

0.04

0.00

0.00

0.16

0.00

1.78

2.79

0.06

0.01

0.00

0.00

0.00

0.03

0.00

1.49

2.31

0.05

0.00

0.00

0.00

0.00

0.03

0.00

1.25

1.45

0.03

0.00

0.00

0.00

0.00

0.03

0.00

1.09

0.77

0.01

0.00

0.00

0.00

0.00

0.03

0.00

0.77

0.29

0.01

0.00

0.00 25.00 0.00

0.00

0.00

0.57

0.18

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.34

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.17

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.07

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
15.73 10.93 11.28 8.65 10.23 7.95 5.94 5.59 5.68 4.37 4.02 2.62 2.45 1.31 2.01 0.44 0.35 0.17 0.26 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

152

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 130. Tandem-axle load distribution factors, Site 245-0218 WB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

0.00

0.00

1.02

0.17

5.62

8.74

0.45

0.00

5.75

0.11

0.00

4.08

0.28

6.30

9.60

0.60

0.00 15.82

0.92

0.00 14.14 0.56

6.82

9.11

0.88

3.85 19.71

1.99

0.00

7.19

1.07 12.05 8.19

3.39

0.00 17.12

1.14

0.00

4.32

1.41 13.60 7.22

5.32

0.00 14.84

1.12

0.00

5.41

2.31 11.45 6.47

7.41

3.85 11.72

1.95

0.00

5.90

3.99 10.01 6.28 11.15 1.92

7.48

2.40

0.00

5.43

5.68

7.79

6.66 11.67 7.69

4.70

3.88

0.00

5.63

8.77

6.03

7.69 11.85 3.85

1.89

7.40

0.00

5.43 10.69 4.42

8.17 11.55 1.92

0.59

12.94 0.00

5.54 11.75 3.73

7.46 11.91 17.31 0.25

14.74 0.00

4.81 12.71 3.30

5.95 11.46 5.77

0.03

14.01 0.00

4.64 10.57 2.69

4.22

7.41 19.23 0.03

9.87

0.00

4.19 10.57 2.07

2.41

3.78 21.15 0.02

5.56

0.00

3.68

7.03

1.59

1.12

0.70

9.62

0.02

2.88

0.00

3.39

4.72

1.03

0.46

0.21

1.92

0.00

2.08

0.00

2.89

2.64

0.61

0.16

0.15

0.00

0.02

1.67

0.00

2.65

2.08

0.43

0.06

0.06

1.92

0.00

1.67

0.00

2.41

1.35

0.19

0.02

0.03

0.00

0.00

2.15

0.00

2.13

0.84

0.14

0.01

0.03

0.00

0.00

2.30

0.00

1.63

0.39

0.08

0.00

0.00

0.00

0.00

2.66

0.00

1.16

0.34

0.05

0.00

0.00

0.00

0.00

2.23

0.00

0.87

0.06

0.01

0.00

0.00

0.00

0.00

1.84

0.00

0.65

0.00

0.00

0.00

0.00

0.00

0.00

1.59

0.00

0.40

0.00

0.00

0.00

0.00

0.00

0.00

0.71

0.00

0.27

0.00

0.00

0.00

0.00

0.00

0.00

0.15

0.00

0.10

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.00

0.04

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
0.57 2.08 3.13 4.55 4.97 4.12 4.17 6.02 8.43 10.09 14.40 17.67 13.07 5.50 1.14 0.05 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

153

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 131. Tandem-axle load distribution factors, Site 175-0247 EB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

7.25

0.00 46.04 17.47 7.40

6.86 11.07 6.10

3.62

5.84

0.00

21.67

3.49

11.26 14.29 26.23 8.13 12.03

1.83

0.00

5.42

3.49 14.60 16.43 23.79 8.13 21.34

2.85

0.00

4.00

4.57 11.21 12.15 12.60 10.16 15.20

2.29

0.00

3.43

5.38

8.84

7.10

5.70 12.60 13.39

2.60

0.00

2.87

4.57

8.00

5.08

3.33 12.20 14.18

3.13

0.00

2.88

4.30

6.78

4.36

2.91

8.94 10.82

4.12

0.00

2.31

6.99

5.25

3.92

2.73

8.54

5.65

5.67

0.00

2.10

5.65

4.04

3.85

2.78

5.28

2.71

10.13 0.00

1.86

6.99

2.97

4.33

2.81

2.85

0.87

12.84 0.00

1.41

6.45

2.95

5.29

2.16

5.69

0.15

13.05 0.00

1.20

6.18

2.95

5.72

1.80

2.44

0.02

11.79 0.00

0.98

5.38

3.23

4.87

1.19

0.81

0.00

6.72

0.00

0.94

6.45

2.99

3.20

0.52

1.63

0.00

4.26

0.00

0.66

3.23

2.37

1.61

0.25

2.03

0.02

2.50

0.00

0.50

3.49

1.91

0.67

0.06

2.03

0.00

1.58

0.00

0.42

3.49

1.32

0.22

0.03

1.22

0.00

0.63

0.00

0.37

0.54

0.72

0.06

0.01

0.41

0.00

0.21

0.00

0.26

0.54

0.45

0.01

0.03

0.41

0.00

0.32

0.00

0.19

0.81

0.26

0.00

0.00

0.00

0.00

0.14

0.00

0.16

0.00

0.19

0.00

0.00

0.41

0.00

0.11

0.00

0.12

0.00

0.10

0.00

0.00

0.00

0.00

0.07

0.00

0.09

0.27

0.06

0.00

0.00

0.00

0.00

0.04

0.00

0.07

0.27

0.05

0.00

0.00

0.00

0.00

0.00

0.00

0.04

0.00

0.04

0.00

0.00

0.00

0.00

0.04

0.00

0.02

0.00

0.03

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
13.22 10.73 18.97 15.33 14.56 7.47 5.36 3.83 3.64 2.11 0.96 0.57 0.57 0.96 0.57 0.19 0.57 0.38 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

154

Mean Axle Load, lb
6000 8000 10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 30,000 32,000 34,000 36,000 38,000 40,000 42,000 44,000 46,000 48,000 50,000 52,000 54,000 56,000 58,000 60,000 62,000 64,000 66,000 68,000 70,000 72,000 74,000 76,000 78,000 80,000

Table 132. Tandem-axle load distribution factors, Site 175-0247 WB.
Vehicle Classification

4

5

6

7

8

9

10

11

12

1.30

0.00 19.26 2.97

8.46

3.56

1.46

0.00

1.80

2.56

0.00 14.90 2.31 13.80 6.86

3.19 25.00 7.11

3.01

0.00 16.28 1.65 16.49 8.63

4.45 25.00 12.27

4.84

0.00 14.87 3.96 13.91 9.23

4.93

0.00 17.23

4.31

0.00

6.76

3.30 12.36 9.29

6.34

0.00 17.17

4.60

0.00

3.80

5.28 10.17 8.97

8.60

0.00 15.56

3.82

0.00

3.66

9.24

7.36

8.40 10.64 0.00 11.20

6.55

0.00

3.12

6.27

5.28

8.12 11.88 25.00 8.37

6.99

0.00

2.82

6.93

4.09

8.34 14.07 0.00

5.33

8.17

0.00

2.38

9.90

2.59

7.98 13.00 0.00

2.71

6.55

0.00

2.17

5.61

1.82

6.83 10.20 0.00

0.93

4.39

0.00

1.56

6.27

1.31

5.35

6.36

0.00

0.25

4.72

0.00

1.41

4.95

0.88

4.01

3.14 25.00 0.06

5.08

0.00

1.12

8.58

0.70

2.58

1.20

0.00

0.00

5.04

0.00

1.06

4.95

0.27

1.22

0.39

0.00

0.00

5.21

0.00

0.98

5.94

0.23

0.46

0.15

0.00

0.00

4.92

0.00

0.75

4.62

0.09

0.12

0.01

0.00

0.00

4.76

0.00

0.66

3.30

0.05

0.04

0.00

0.00

0.00

4.27

0.00

0.61

1.65

0.07

0.01

0.00

0.00

0.00

3.50

0.00

0.53

1.65

0.01

0.00

0.00

0.00

0.00

2.40

0.00

0.46

0.33

0.02

0.00

0.00

0.00

0.00

1.75

0.00

0.31

0.33

0.02

0.00

0.00

0.00

0.00

0.69

0.00

0.18

0.00

0.01

0.00

0.00

0.00

0.00

0.41

0.00

0.16

0.00

0.00

0.00

0.00

0.00

0.00

0.12

0.00

0.09

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.05

0.00

0.00

0.00

0.00

0.00

0.00

0.04

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

13
15.42 16.36 12.62 17.76 9.81 6.54 6.07 2.80 3.27 1.40 3.74 1.87 0.93 0.47 0.00 0.47 0.00 0.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

155

APPENDIX D: ESAL FACTOR CALCULATION RESULTS

ESAL FACTORS FOR FLEXIBLE PAVEMENTS Table 133 through table 142 represent the truck ESAL factors calculated for flexible pavements for each vehicle classification. Different structural numbers were considered. Then, the ESAL factors are the weighted averages based on their respective vehicle counts.

Table 133. Truck ESAL factors for flexible pavement, Site 185-0227.

4

5

6

7

8

9

10

11

12

13

SN

Vehicle Classes

Single Unit Trucks

Multi-Unit Trucks

Avg

4

0.60 0.26 0.34 1.11 0.30 0.80 0.54 1.20 0.69 0.35

ESAL Factor

23,540 85,094 42,454 351 185,019 1,818,047 6,085 103,794 67,587 149

No. of Vehicles

151,439

2,180,681

Weighted Avg ESAL

0.34

0.77

Factors

Avg

6

0.56 0.24 0.31 1.06 0.27 0.72 0.46 1.09 0.58 0.29

ESAL Factor

23,540 85,094 42,454 351 185,019 1,818,047 6,085 103,794 67,587 149

No. of Vehicles

151,439

2,180,681

Weighted Avg ESAL

0.31

0.69

Factors

Avg

8

0.55 0.23 0.30 1.05 0.26 0.70 0.44 1.07 0.55 0.28

ESAL Factor

23,540 85,094 42,454 351 185,019 1,818,047 6,085 103,794 67,587 149

No. of Vehicles

151,439

2,180,681

Weighted Avg ESAL

0.30

0.67

Factors

156

Table 134. Truck ESAL factors for flexible pavement, Site 285-0243.

4

5

6

7

8

9

10

11

12

13

SN

Vehicle Classes

Single Unit Trucks

Multi-Unit Trucks

Avg

4

0.80 0.29 0.43 1.38 0.28 0.71 0.54 1.14 0.60 0.72

ESAL Factor

4,901 34,046 12,619 76 30,522 160,744 1,265 8,930 3,602

64

No. of Vehicles

51,642

205,127

Weighted Avg ESAL Factors

0.37

0.66

Avg

6

0.75 0.27 0.40 1.32 0.26 0.63 0.46 1.04 0.50 0.67

ESAL Factor

4,901 34,046 12,619 76 30,522 160,744 1,265 8,930 3,602

64

No. of Vehicles

51,642

205,127

Weighted Avg ESAL Factors

0.35

0.59

Avg

8

0.74 0.26 0.40 1.31 0.25 0.61 0.44 1.02 0.48 0.66

ESAL Factor

4,901 34,046 12,619 76 30,522 160,744 1,265 8,930 3,602

64

No. of Vehicles

51,642

205,127

Weighted Avg ESAL Factors

0.34

0.57

Table 135. Truck ESAL factors for flexible pavement, Site 021-w334.

4

5

6

7

8

9

10

11

12

13

SN

Vehicle Classes

Single Unit Trucks

Multi-Unit Trucks

Avg

4

ESAL Factor

0.42 0.30 0.48 1.19 0.32 0.53 0.51 0.44 0.41 0.98

8,514 66,385 40,334 308 61,258 267,445 4,805 3,691 6,094

91

No. of Vehicles

115,541

343,384

Weighted Avg ESAL Factors

0.38

0.49

Avg

6

ESAL Factor

0.39 0.29 0.47 1.14 0.30 0.46 0.43 0.39 0.35 1.01

8,514 66,385 40,334 308 61,258 267,445 4,805 3,691 6,094

91

No. of Vehicles

115,541

343,384

Weighted Avg ESAL Factors

0.36

0.43

Avg

8

ESAL Factor

0.39 0.29 0.48 1.14 0.31 0.45 0.41 0.38 0.34 1.06

8,514 66,385 40,334 308 61,258 267,445 4,805 3,691 6,094

91

No. of Vehicles

115,541

343,384

Weighted Avg ESAL Factors

0.37

0.42

157

Table 136. Truck ESAL factors for flexible pavement, Site 127-0312.

4

5

6

7

8

9

10

11

12

13

SN

Vehicle Classes

Single Unit Trucks

Multi-Unit Trucks

Avg

4

0.71 0.31 0.37 1.27 0.27 0.69 0.43 1.09 0.64 0.30

ESAL Factor

No. of Vehicles

26,950 193,885 78,191 299,565

539 295,471 1,820,138 15,550 67,565 47,459 1032 2,247,215

Weighted Avg ESAL Factors

0.36

0.65

Avg

6

0.68 0.29 0.34 1.16 0.25 0.62 0.36 0.99 0.54 0.25

ESAL Factor

No. of Vehicles

26,950 193,885 78,191 299,565

539 295,471 1,820,138 15,550 67,565 47,459 1032 2,247,215

Weighted Avg ESAL Factors

0.34

0.58

Avg

8

0.69 0.30 0.33 1.23 0.25 0.60 0.35 0.97 0.51 0.25

ESAL Factor

No. of Vehicles

26,950 193,885 78,191 299,565

539 295,471 1,820,138 15,550 67,565 47,459 1032 2,247,215

Weighted Avg ESAL Factors

0.34

0.56

Table 137. Truck ESAL factors for flexible pavement, Site 051-0387.

4

5

6

7

8

9

10

11

12

13

SN

Vehicle Classes

Single Unit Trucks

Multi-Unit Trucks

Avg

4

ESAL Factor

0.80 0.41 0.64 1.28 0.35 0.83 0.49 1.40 0.82 0.39

No. of Vehicles

38,199 161,528 87,551 288,246

968 301,067 1,572,630 20,217 63,243 47,557 1243 2,005,957

Weighted Avg ESAL

0.53

0.77

Factors

Avg

6

ESAL Factor

0.78 0.38 0.61 1.27 0.33 0.73 0.42 1.28 0.69 0.34

No. of Vehicles

38,199 161,528 87,551 288,246

968 301,067 1,572,630 20,217 63,243 47,557 1243 2,005,957

Weighted Avg ESAL

Factors

0.50

0.69

Avg

8

ESAL Factor

0.78 0.38 0.61 1.29 0.35 0.71 0.40 1.26 0.66 0.33

No. of Vehicles

38,199 161,528 87,551 288,246

968 301,067 1,572,630 20,217 63,243 47,557 1243 2,005,957

Weighted Avg ESAL

0.50

0.67

Factors

158

Table 138. Truck ESAL factors for flexible pavement, Site 217-0218.

4

5

6

7

8

9

10

11

12

13

SN

Vehicle Classes

Single Unit Trucks

Multi-Unit Trucks

Avg

4

0.62 0.49 0.70 1.74 0.35 0.92 0.67 1.38 0.83 0.59

ESAL Factor

34,629 35,568 35,529 244 76,596 500,655 4,500 15,144 7,825 108

No. of Vehicles

105,970

604,828

Weighted Avg ESAL Factors

0.61

0.86

Avg

6

0.59 0.47 0.69 1.72 0.33 0.82 0.57 1.24 0.70 0.53

ESAL Factor

34,629 35,568 35,529 244 76,596 500,655 4,500 15,144 7,825 108

No. of Vehicles

105,970

604,828

Weighted Avg ESAL Factors

0.59

0.76

Avg

8

0.59 0.47 0.70 1.74 0.33 0.79 0.55 1.21 0.66 0.51

ESAL Factor

34,629 35,568 35,529 244 76,596 500,655 4,500 15,144 7,825 108

No. of Vehicles

105,970

604,828

Weighted Avg ESAL Factors

0.59

0.74

Table 139. Truck ESAL factors for flexible pavement, Site 051-0368.

4

5

6

7

8

9

10

11

12

13

SN

Vehicle Classes

Single Unit Trucks

Multi-Unit Trucks

Avg

4

0.88 0.47 1.00 1.23 0.33 0.74 0.54 1.24 0.61 0.28

ESAL Factor

40,569 1,317 1,334

32

No. of Vehicles

43,252

628 1,879 708 14,675 5,698 1266 24,854

Weighted Avg ESAL

0.87

0.96

Factors

Avg

6

0.88 0.45 1.00 1.20 0.32 0.66 0.46 1.15 0.51 0.24

ESAL Factor

40,569 1,317 1,334

32

No. of Vehicles

43,252

628 1,879 708 14,675 5,698 1266 24,854

Weighted Avg ESAL

0.87

0.88

Factors

Avg

8

0.92 0.47 1.03 1.20 0.34 0.64 0.44 1.13 0.49 0.23

ESAL Factor

40,569 1,317 1,334

32

No. of Vehicles

43,252

628 1,879 708 14,675 5,698 1266 24,854

Weighted Avg ESAL

0.91

0.86

Factors

159

Table 140. Truck ESAL factors for flexible pavement, Site 143-0126.

4

5

6

7

8

9

10

11

12

13

SN

Vehicle Classes

Single Unit Trucks

Multi-Unit Trucks

Avg

4

1.09 0.38 0.41 0.61 0.33 0.57 0.45 0.76 0.43 0.29

ESAL Factor

21,327 120,989 59,655 641 211,269 2,232,380 10,619 118,998 79,642 687

No. of Vehicles

202,612

2,653,595

Weighted Avg ESAL Factors

0.46

0.55

Avg

6

ESAL Factor

1.11 0.37 0.39 0.58 0.30 0.50 0.38 0.67 0.35 0.25

21,327 120,989 59,655 641 211,269 2,232,380 10,619 118,998 79,642 687

No. of Vehicles

202,612

2,653,595

Weighted Avg ESAL Factors

0.45

0.48

Avg

8

ESAL Factor

1.16 0.38 0.39 0.57 0.30 0.48 0.36 0.65 0.33 0.25

21,327 120,989 59,655 641 211,269 2,232,380 10,619 118,998 79,642 687

No. of Vehicles

202,612

2,653,595

Weighted Avg ESAL Factors

0.47

0.47

Table 141. Truck ESAL factors for flexible pavement, Site 245-0218.

4

5

6

7

8

9

10

11

12

13

SN

Vehicle Classes

Single Unit Trucks

Multi-Unit Trucks

Avg

4

ESAL Factor

1.97 1.60 1.80 1.51 0.66 0.92 0.57 1.23 0.67 0.55

15,172 126,207 75,039 No. of Vehicles
221,093

4,675 205,152 1,659,438 10,939 34,714 17,233 1,931,143

3667

Weighted Avg ESAL

Factors

1.69

0.89

Avg

6

ESAL Factor

2.06 1.70 1.93 1.48 0.66 0.82 0.50 1.13 0.57 0.48

15,172 126,207 75,039 No. of Vehicles
221,093

4,675 205,152 1,659,438 10,939 34,714 17,233 1,931,143

3667

Weighted Avg ESAL

1.80

0.80

Factors

Avg

8

ESAL Factor

2.33 1.97 2.11 1.49 0.72 0.79 0.48 1.11 0.55 0.46

15,172 126,207 75,039 No. of Vehicles
221,093

4,675 205,152 1,659,438 10,939 34,714 17,233 1,931,143

3667

Weighted Avg ESAL

2.04

0.79

Factors

160

Table 142. Truck ESAL factors for flexible pavement, Site 175-0247.

4

5

6

7

8

9

10

11

12

13

SN

Vehicle Classes

Single Unit Trucks

Multi-Unit Trucks

Avg

4

0.82 0.31 0.37 1.03 0.58 0.57 0.34 1.11 0.50 0.28

ESAL Factor

17,288 42,236 42,620 432 84,259 836,594 23,643 13,879 11,720 637

No. of Vehicles

102,576

970,732

Weighted Avg ESAL

0.42

0.57

Factors

Avg

6

0.82 0.29 0.35 1.02 0.57 0.50 0.28 1.03 0.42 0.24

ESAL Factor

17,288 42,236 42,620 432 84,259 836,594 23,643 13,879 11,720 637

No. of Vehicles

102,576

970,732

Weighted Avg ESAL

0.41

0.51

Factors

Avg

8

0.84 0.29 0.35 1.04 0.59 0.49 0.27 1.01 0.41 0.23

ESAL Factor

17,288 42,236 42,620 432 84,259 836,594 23,643 13,879 11,720 637

No. of Vehicles

102,576

970,732

Weighted Avg ESAL

0.41

0.50

Factors

ESAL FACTORS FOR RIGID PAVEMENTS Table 143 through table 151 represent the truck ESAL factors calculated for rigid pavements for each vehicle classification with different slab thicknesses. Similar to the flexible pavement design, ESAL factors are the weighted averages based on their respective vehicle counts.

161

Table 143. Truck ESAL factors for rigid pavement, Site 185-0227.

Slab

4

5

6

7

8

9

10

11

12

13

Thickness Vehicle Classes

(in.)

Single Unit Trucks

Multi-Unit Trucks

Avg

8

0.68 0.25 0.43 1.56 0.27 1.14 0.82 1.07 0.64 0.87

ESAL Factor

No. of Vehicles

35,254 132,858 67,553 236,377

712 284,159 2,270,726 10,530 152,352 107,221 339 2,825,327

Weighted Avg ESAL Factors

0.37

1.03

Avg

10

0.68 0.25 0.43 1.60 0.27 1.15 0.82 1.05 0.62 0.90

ESAL Factor

No. of Vehicles

35,254 132,858 67,553 236,377

712 284,159 2,270,726 10,530 152,352 107,221 339 2,825,327

Weighted Avg ESAL Factors

0.37

1.03

Avg

12

0.68 0.25 0.44 1.61 0.27 1.15 0.82 1.05 0.61 0.91

ESAL Factor

No. of Vehicles

35,254 132,858 67,553 236,377

712 284,159 2,270,726 10,530 152,352 107,221 339 2,825,327

Weighted Avg ESAL Factors

0.37

1.04

Table 144. Truck ESAL factors for rigid pavement, Site 285-0243.

Slab

4

5

6

7

8

9

10

11

12

13

Thickness Vehicle Classes

(in.)

Single Unit Trucks

Multi-Unit Trucks

Avg

8

ESAL Factor

1.40 0.35 0.65 2.32 0.34 0.89 0.79 1.08 0.56 1.13

10,179 78,515 28,097 147 70,805 326,789 2,268 19,404 7,813 116

No. of Vehicles

116,938

427,195

Weighted Avg ESAL Factors

0.52

0.80

Avg

10

ESAL Factor

1.45 0.35 0.67 2.43 0.35 0.89 0.79 1.07 0.55 1.16

10,179 78,515 28,097 147 70,805 326,789 2,268 19,404 7,813 116

No. of Vehicles

116,938

427,195

Weighted Avg ESAL Factors

0.53

0.80

Avg

12

ESAL Factor

1.47 0.36 0.68 2.48 0.35 0.89 0.79 1.07 0.54 1.18

10,179 78,515 28,097 147 70,805 326,789 2,268 19,404 7,813 116

No. of Vehicles

116,938

427,195

Weighted Avg ESAL Factors

0.53

0.81

162

Table 145. Truck ESAL factors for rigid pavement, Site 021-w334.

Slab

4

5

6

7

8

9

10

11

12

13

Thickness Vehicle Classes

(in.)

Single Unit Trucks

Multi-Unit Trucks

Avg

8

0.48 0.29 0.67 2.03 0.32 0.74 0.85 0.40 0.39 1.70

ESAL Factor

8,514 66,385 40,334 308 61,258 267,445 4,805 3,691 6,094

91

No. of Vehicles

115,541

343,384

Weighted Avg ESAL Factors

0.44

0.66

Avg

10

0.48 0.29 0.70 2.13 0.33 0.74 0.85 0.39 0.38 1.84

ESAL Factor

8,514 66,385 40,334 308 61,258 267,445 4,805 3,691 6,094

91

No. of Vehicles

115,541

343,384

Weighted Avg ESAL Factors

0.46

0.66

Avg

12

0.48 0.30 0.72 2.17 0.33 0.74 0.85 0.39 0.38 1.92

ESAL Factor

8,514 66,385 40,334 308 61,258 267,445 4,805 3,691 6,094

91

No. of Vehicles

115,541

343,384

Weighted Avg ESAL Factors

0.46

0.66

Table 146. Truck ESAL factors for rigid pavement, Site 127-0312.

Slab

4

5

6

7

8

9

10

11

12

13

Thickness Vehicle Classes

(in.)

Single Unit Trucks

Multi-Unit Trucks

Avg

8

ESAL Factor

0.91 0.30 0.47 1.75 0.27 1.00 0.63 1.01 0.62 0.44

No. of Vehicles

26,950 193,885 78,191 299,565

539 295,471 1,820,138 15,550 67,565 47,459 2,247,215

1032

Weighted Avg ESAL Factors

0.40

0.90

Avg

10

ESAL Factor

0.93 0.30 0.48 1.79 0.27 1.01 0.63 0.99 0.60 0.44

No. of Vehicles

26,950 193,885 78,191 299,565

539 295,471 1,820,138 15,550 67,565 47,459 2,247,215

1032

Weighted Avg ESAL Factors

0.41

0.90

Avg

12

ESAL Factor

0.94 0.30 0.49 1.83 0.27 1.01 0.63 0.99 0.60 0.45

No. of Vehicles

26,950 193,885 78,191 299,565

539 295,471 1,820,138 15,550 67,565 47,459 2,247,215

1032

Weighted Avg ESAL Factors

0.41

0.90

163

Table 147. Truck ESAL factors for rigid pavement, Site 051-0387.

Slab

4

5

6

7

8

9

10

11

12

13

Thickness Vehicle Classes

(in.)

Single Unit Trucks

Multi-Unit Trucks

Avg

8

ESAL Factor

1.00 0.39 0.83 1.93 0.35 1.17 0.70 1.30 0.79 0.60

No. of Vehicles

38,199 161,528 87,551 288,246

968 301,067 1,572,630 20,217 63,243 47,557 2,005,957

1243

Weighted Avg ESAL Factors

0.61

1.04

Avg

10

ESAL Factor

1.03 0.39 0.85 2.02 0.36 1.18 0.69 1.29 0.77 0.60

No. of Vehicles

38,199 161,528 87,551 288,246

968 301,067 1,572,630 20,217 63,243 47,557 2,005,957

1243

Weighted Avg ESAL Factors

0.62

1.04

Avg

12

ESAL Factor

1.04 0.39 0.86 2.05 0.36 1.18 0.69 1.28 0.76 0.60

No. of Vehicles

38,199 161,528 87,551 288,246

968 301,067 1,572,630 20,217 63,243 47,557 2,005,957

1243

Weighted Avg ESAL Factors

0.62

1.04

Table 148. Truck ESAL factors for rigid pavement, Site 217-0218.

Slab

4

5

6

7

8

9

10

11

12

13

Thickness Vehicle Classes

(in.)

Single Unit Trucks

Multi-Unit Trucks

Avg

8

ESAL Factor

0.68 0.38 0.74 1.40 0.32 0.73 0.51 0.78 0.47 0.57

No. of Vehicles

50,352 63,669 66,576 181,309

712 123,357 1,061,965 11,434 31,962 16,067 1,246,363

1578

Weighted Avg ESAL Factors

0.60

0.68

Avg

10

ESAL Factor

0.70 0.38 0.78 1.46 0.32 0.73 0.51 0.76 0.46 0.57

No. of Vehicles

50,352 63,669 66,576 181,309

712 123,357 1,061,965 11,434 31,962 16,067 1,246,363

1578

Weighted Avg ESAL Factors

0.62

0.68

Avg

12

ESAL Factor

0.70 0.39 0.80 1.49 0.32 0.73 0.51 0.76 0.46 0.57

No. of Vehicles

50,352 63,669 66,576 181,309

712 123,357 1,061,965 11,434 31,962 16,067 1,246,363

1578

Weighted Avg ESAL Factors

0.63

0.69

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Table 149. Truck ESAL factors for rigid pavement, Site 051-0368.

Slab

4

5

6

7

8

9

10

11

12

13

Thickness Vehicle Classes

(in.)

Single Unit Trucks

Multi-Unit Trucks

Avg

8

ESAL Factor

1.01 0.46 1.47 2.11 0.35 1.04 0.74 1.16 0.59 0.41

40,569 1,317 1,334

32

No. of Vehicles

43,252

628 1,879 708 14,675 5,698 1266 24,854

Weighted Avg ESAL Factors

1.01

0.95

Avg

10

ESAL Factor

1.05 0.47 1.55 2.23 0.36 1.05 0.73 1.15 0.58 0.41

40,569 1,317 1,334

32

No. of Vehicles

43,252

628 1,879 708 14,675 5,698 1266 24,854

Weighted Avg ESAL Factors

1.05

0.94

Avg

12

ESAL Factor

1.07 0.48 1.61 2.28 0.37 1.05 0.73 1.15 0.58 0.41

40,569 1,317 1,334

32

No. of Vehicles

43,252

628 1,879 708 14,675 5,698 1266 24,854

Weighted Avg ESAL Factors

1.07

0.94

Table 150. Truck ESAL factors for rigid pavement, Site 143-0126.

Slab

4

5

6

7

8

9

10

11

12

13

Thickness Vehicle Classes

(in.)

Single Unit Trucks

Multi-Unit Trucks

Avg

8

ESAL Factor

1.52 0.38 0.50 0.97 0.34 0.70 0.66 0.68 0.41 0.48

21,327 120,989 59,655 641 211,269 3,874 10,619 118,998 79,642 687

No. of Vehicles

202,612

425,089

Weighted Avg ESAL Factors

0.54

0.46

Avg

10

ESAL Factor

1.62 0.38 0.52 1.00 0.34 0.70 0.66 0.67 0.40 0.48

21,327 120,989 59,655 641 211,269 3,874 10,619 118,998 79,642 687

No. of Vehicles

202,612

425,089

Weighted Avg ESAL Factors

0.56

0.45

Avg

12

ESAL Factor

1.68 0.39 0.52 1.02 0.34 0.70 0.65 0.67 0.39 0.48

21,327 120,989 59,655 641 211,269 3,874 10,619 118,998 79,642 687

No. of Vehicles

202,612

425,089

Weighted Avg ESAL Factors

0.57

0.45

165

Table 151. Truck ESAL factors for rigid pavement, Site 175-0247.

Slab

4

5

6

7

8

9

10

11

12

13

Thickness Vehicle Classes

(in.)

Single Unit Trucks

Multi-Unit Trucks

Avg

8

ESAL Factor

0.98 0.30 0.47 1.59 0.65 0.80 0.51 1.04 0.48 0.40

17,288 42,236 42,620 432 84,259 836,594 23,643 13,879 11,720 637

No. of Vehicles

102,576

970,732

Weighted Avg ESAL Factors

0.49

0.78

Avg

10

ESAL Factor

1.02 0.30 0.48 1.67 0.67 0.80 0.50 1.04 0.47 0.40

17,288 42,236 42,620 432 84,259 836,594 23,643 13,879 11,720 637

No. of Vehicles

102,576

970,732

Weighted Avg ESAL Factors

0.50

0.78

Avg

12

ESAL Factor

1.04 0.30 0.49 1.70 0.68 0.80 0.50 1.03 0.47 0.40

17,288 42,236 42,620 432 84,259 836,594 23,643 13,879 11,720 637

No. of Vehicles

102,576

970,732

Weighted Avg ESAL Factors

0.51

0.78

TRUCK ESAL FACTORS COMPARISON RESULTS The truck ESAL factors of VDOT, NCDOT, and GDOT for flexible pavement design are compared in figure 82. For comparison purposes, the ESAL factors from only interstate highways of VDOT and NCDOT have been considered. It is important to mention that the ESAL factors for the state of Georgia have been calculated twice: before and after applying the QC process. Figure 83 shows the same comparison for the purpose of rigid pavement design.

166

Figure 82. Bar graph. Comparison of truck ESAL factors for flexible pavement. Figure 83. Bar graph. Comparison of truck ESAL factors for rigid pavement. 167

APPENDIX E: PROPOSED STANDARD OPERATING PROCEDURE (SOP)
Georgia Department of Transportation Office of Materials and Testing
Proposed Standard Operating Procedure (SOP) DRAFT Development of Equivalent Single Axle Load (ESAL) Factor for Georgia Pavement Design
I. General The purpose of this Standard Operating Procedure is to outline the methodology for calculating the truck ESAL factors for the state of Georgia using Weigh-in-Motion (WIM) data. Generally, there are 14 WIM stations on interstate highways throughout Georgia. However, only 10 WIM stations have available data. The formula used for ESAL calculations of either flexible or rigid pavement is based on the AASHTO 1993 Pavement Design Guide. In addition to the ESAL factors, the methodology for generating traffic input data for the AASHTOWare Pavement ME software has been presented. Prior to ESAL factors and PMED inputs calculation, quality control rules are applied to the raw WIM data to ensure data are good enough quality. QC checks, truck ESAL factors calculations, and generating PMED input data are all accomplished using codes developed in the Python programming language.
II. Data Collection The first step in updating/generating truck ESAL factors is collecting data. WIM sensor installation, sensor calibration, and data collection are performed by the vendor.
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A. WIM System Components 1. The sensor array, which is the combination of WIM sensors and loop detectors within a weighing lane, is installed. 2. Electronics measure and process sensor outputs providing vehicle records for displayand storage. 3. Support devices transmit the collected data to a remote server. 4. Software installed in the WIM electronics processes sensor measurements, and analyzes, formats, and temporarily stores collected data.
III. Quality Control Vendors have the responsibility of calibrating WIM sensors regularly to obtain high qualityWIM data. However, missing and erroneous data are inevitable due to either system malfunctions or drivers' behavior. Below criteria can applied to the available WIM data before new QC procedure is developed.
A. First Step Quality Control Checks 1. Any field with a null value will be removed. 2. Invalid hour will be removed. 3. Invalid month will be removed. 4. Invalid vehicle class code will be removed. 5. Invalid station ID will be removed. 6. Invalid direction for station will be removed.
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7. Invalid lane number for station will be removed. 8. Invalid year will be removed. 9. Invalid day will be removed. 10. Hour without any weight records will be removed. 11. Axle count inconsistent with number of axle spacings will be removed. 12. Axle count inconsistent with number of axle weights will be removed. 13. Gross Vehicle Weight (GVW) inconsistent with sum of axle weights will be removed. 14. Axle weight that is out of acceptable range will be removed. 15. Axle spacing that is out of acceptable range will be removed. 16. Vehicle will be removed if sum of axle spacings exceeds maximum wheelbase. (This
check is not applicable to vehicles classes 11 through 13.) 17. Unusual patterns of average Day of Week (DOW) volumes by month will be removed. 18. Unusual patterns of GVW plots by class by month will be removed. 19. Unusual patterns of class distribution by month will be removed. 20. Unusual patterns of class percent distribution will be removed.
B. Second Step Quality Control Checks 1. Days with having vehicles in class 14 will be removed. 2. Days with having vehicles in class 15 will be removed. 3. Data that do not provide a complete 24 hours of truck data will be removed. 4. Days with no truck data will be removed. 5. Data with no truck traffic in one lane for the day will be removed. 6. Any daily truck traffic volume that is substantially different from the previous year will be removed.
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7. Days with no data will be removed. 8. Any day in which the class 9 average steer weight is outside the parameters will be
removed. 9. Any day in which the class 9 average BC axle spacing is outside the parameters will be
removed.
IV. Truck ESAL Factors Python codes have been developed and delivered to GDOT to generate truck ESAL factors for each WIM station.
1. First, read the dataset, which is a .csv file. 2. Select the considered vehicle class from the whole dataset. 3. Extract the axle weight list and change its format . 4. Extract the axle spacing list and change its format . 5. Based on the axle spacing list, determine the axle type and categorize them in different
lists. 6. Assume terminal serviceability of 2.5 for WIM stations located in Georgia interstate
highways. 7. Assume structural number for flexible pavement or slab thickness for rigid pavement
design for the calculations. 8. Calculate LEFs for different axle types of individual vehicles and then append them in a
list. 9. Sum the LEFs of individual vehicles to develop individual ESAL factors for all trucks.
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10. Calculate the average ESAL factor for the considered vehicle class using the list created in the previous step.
This process is repeated for FHWA vehicle classes 4 through 13, separately.
V. AASHTOWare Pavement ME Traffic Inputs Python codes were developed and delivered to generate traffic data as inputs to the Pavement ME software for WIM stations.
A. Vehicle Class Distribution (VCD) VCD is defined as the truck percentage of each vehicle class within the Annual Average Daily Truck Traffic (AADTT).
1. Determine the total number of trucks counted within each hour of traffic data in the sample. 2. Calculate the AADTT for each vehicle class, separately. 3. Total the AADTT of all truck classes. 4. Calculate the percentage of each vehicle class within the AADTT. The sum of the percent AADTT of all truck classes must equal 100.
B. Monthly Distribution Factor (MDF) MDF is defined as the seasonal differences in AADTT by allocating a normalized weight factor to each month of the year.
1. Select the considered vehicle class from the whole dataset. 2. Determine the total number of the specified vehicle class. 3. Determine the total number of vehicles for each month of the year from January through
December.
172

4. Obtain the MDF for each month by multiplying the number of vehicles within each month by 12 and dividing by the total number of vehicles in the year.
The sum of the MDF of each truck class within a year must equal 12.
C. Hourly Distribution Factor (HDF) HDF is defined as the percentage of total trucks within each hour using data measured continuously over a 24-hour period.
1. Determine the total number of trucks counted within each hour of traffic data in the sample. 2. Average the number of trucks for each of the 24 hours of the day in the sample. For
example, if the data include truck counts for the first hour of the day for 6 days, then total those 6 counts and divide by 6. 3. Total the 24-hourly averages from step 2. 4. Divide each of the 24-hourly averages from step 2 by the total from step 3 and multiply by 100. The sum of the precent of daily truck traffic per time increment must add up to 100 percent.
D. Axles per Truck Class This input represents the average number of axles for each truck class (class 4 to 13) for each axle group type (single, tandem, tridem, and quad).
1. Select the considered vehicle class from the whole dataset. 2. Determine the total number of the specified vehicle class . 3. Extract the axle spacing list and change its format . 4. Based on the axle spacing list, determine the axle types and categorize them in different
lists.
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5. Divide the number of specific axle type by the total number of vehicle class in step 2. Axles per truck class is defined for each axle type within each vehicle class. E. Normalized Axle Load Spectra (NALS) NALS represents the percentage of the total axle applications for load intervals in a specific axle group type (single, tandem, tridem, and quad) and vehicle classes 4 through 13.
1. Select the considered vehicle class from the whole dataset. 2. Extract the axle weight list and change its format . 3. Extract the axle spacing list and change its format . 4. Based on the axle spacing list, determine the axle types and categorize them in different
lists. 5. Determine the axle load bins for each axle group type. For example, single axle load bins
are from 3,000 lb to 40,000 lb at 1,000-lb load intervals. 6. Read each axle within the list. Append the axle to the list that corresponds to the specific
load bin. 7. Calculate the percentage of the total number of axle applications within each load range
(load bin) for each axle type for each year of data (i.e., normalize the number of axle load applications within each truck class and axle type). This process is repeated for FHWA each vehicle class 4 through 13, individually.
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ACKNOWLEDGMENTS The University of Georgia acknowledges the financial support for this work provided by the Georgia Department of Transportation (GDOT). The authors thank the technical manager, Mr. Ian Rish, and his Office of Materials and Testing (OMAT) team member, Ms. Robie Cunningham for research support and partnership. Special thanks also to the project manager, Mr. Sunil Thapa, who advised the research team in successfully performing the study and assisted in the coordination of project meetings with GDOT. Finally, the team expresses thanks for the continuous support from Supriya Kamatkar and her leadership in the Performance-based Management and Research Office.
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