Long-Term Pavement Performance 

(LTPP) Data Analysis Support: 

National Pooled Fund Study TPF-5(013)

Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Effects of Multiple Freeze Cycles and Deep Frost 

Penetration on Pavement Performance and Cost

Publication No. FHWA-HRT-06-121 November 2006
Foreword 

Understanding deterioration of pavements exposed to climates with multiple freeze-thaw 

cycles as compared to climates with sustained deep-frost penetration is important to State 

Highway Agencies (SHAs) across the country. Consideration must also be given to 

differential performances between pavements in these freezing climates and those in 

nonfreezing areas. This report documents a study conducted to evaluate pavement 

deterioration in various environmental settings. In addition, it documents local 

adaptations currently in use to mitigate frost-related damage along with the cost 

differences associated with constructing and maintaining pavements in the various 

climates. Performance models developed from the Long-Term Pavement Performance 

(LTPP) database were used to predict and compare performance in various environments. 

As demonstrated in the report, the prediction models are also an important tool in the 

calibration process outlined in the National Cooperative Highway Research Program 

(NCHRP) Guide for Mechanistic-Empirical Design of New and rehabilitated Pavement 

Structures as well as in pavement management applications for SHAs with limited 

quantities of regional performance data. 

Gary L. Henderson 

Director, Office of Infrastructure 

 Research and Development 

Notice 

This document is disseminated under the sponsorship of the U.S. Department of 

Transportation in the interest of information exchange. The U.S. Government assumes no 

liability for its contents or use thereof. This report does not constitute a standard, 

specification, or regulation. 

The U.S. Government does not endorse products or manufacturers. Trade and 

manufacturers’ names appear in this report only because they are considered essential to 

the object of the document. 

Quality Assurance Statement 

The Federal Highway Administration (FHWA) provides high-quality information to 

serve Government, industry, and the public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the quality, objectivity, 

utility, and integrity of its information. FHWA periodically reviews quality issues and 

adjusts its programs and processes to ensure continuous quality improvement. 
1. Report No. 

FHWA-HRT-06-121

2. Government Accession No. 3. Recipient's Catalog No. 

5. Report Date 

November 2006 

4. Title and Subtitle 

Long-Term Pavement Performance (LTPP) Data Analysis Support: 

National Pooled Fund Study TPF-5(013) 

Effects of Multiple Freeze Cycles and Deep Frost Penetration on 

Pavement Performance and Cost

6. Performing Organization Code 

7. Author(s) 

N. Jackson and J. Puccinelli 

8. Performing Organization Report No. 

123210-8

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

Nichols Consulting Engineers 

1885 South Arlington Avenue 

Suite 111 

Reno, NV 89509-3370

11. Contract or Grant No. 

DTFH61-02-D-00139

13. Type of Report and Period Covered 

Final Report 

March 2003 to May 2006

12. Sponsoring Agency Name and Address 

Office of Infrastructure R&D 

Federal Highway Administration 

6300 Georgetown Pike 

McLean, VA 22101-2296 14. Sponsoring Agency Code 

15. Supplementary Notes 

Contracting Officer’s Technical Representative (COTR): Larry Wiser, Long-Term Pavement Performance 

Team, HRDI-13 

16. Abstract 

The objectives of this study are to: (1) quantify the effects of frost penetration on pavement performance in climates 

with deep sustained frost as compared to environments with multiple freeze-thaw cycles, (2) investigate the effect 

that local adaptations have on mitigating frost penetration damage, and (3) estimate the associated cost of 

constructing and maintaining pavements in freezing climates. The approach consisted of modeling various 

pavement performance measures using both climatic and nonclimatic input variables and performance data 

collected as part of the Long-Term Pavement Performance program. Five climatic scenarios are defined in terms of 

climatic input variables for the models. Predicted performance measures are presented for each of the climatic 

scenarios and compared at a 95 percent confidence interval to determine statistically significant performance 

differences. Participating pooled fund States (PFS) were queried as to standard specifications, standard designs, 

average life expectancies, and construction costs specific to each State Highway Agency (SHA). This data along 

with information acquired through literature review of SHA standard practices is summarized with consideration 

given to the mitigation of frost-related damage. Life cycle cost analysis for each climatic scenario using predicted 

performance to determine average life and average agency construction costs for standard pavement sections is also 

discussed and compared. The use of the performance models for local calibration as required in the National 

Cooperative Highway Research Program Guide for Mechanistic-Empirical Design of New and Rehabilitated 

Pavemen Structures is explored along with the possible application of the performance models in pavement 

management systems. 

17. Key Words 

Frost, freeze-thaw, LTPP, life cycle cost analysis, 

performance modeling, climate, M-E pavement design 

guide, pavement management system, AC, PCC 

18. Distribution Statement 

No restrictions. This document is available to 

the public through the National Technical 

Information Service, Springfield, VA 22161. 

19. Security Classification (of this report)

Unclassified

20. Security Classification (of this page)

Unclassified

21. No. of Pages 

262 

22. Price
ii 

SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol 

LENGTH 

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha

mi2 square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

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

MASS 

oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

TEMPERATURE (exact degrees) o

F Fahrenheit 5 (F-32)/9 Celsius oC 

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH

mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2

 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees) o

C Celsius 1.8C+32 Fahrenheit oF 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for th International System of Units. Appropr e iate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
iii 

TABLE OF CONTENTS 

EXECUTIVE SUMMARY ................................................................................................ 1 

1. INTRODUCTION .......................................................................................................... 5 

2. BACKGROUND ............................................................................................................ 7 

3. DEVELOPMENT OF ANALYSIS DATASET............................................................. 9 

DATABASE STRUCTURE AND CONTENT.................................................... 16 

Pavement Types........................................................................................ 17 

Climatic Data and Frost Depth ................................................................. 17 

Performance Data...................................................................................... 18 

Soils and Material Properties.................................................................... 24 

Traffic Data............................................................................................... 27 

TEST SECTION SELECTION ............................................................................ 27 

4. MODEL FITTING STATISTICAL APPROACH ....................................................... 29 

5. PERFORMANCE MODEL DEVELOPMENT AND SELECTION........................... 39 

PAVEMENT ROUGHNESS PREDICTION MODELS...................................... 43 

RUTTING PREDICTION MODELS FOR FLEXIBLE PAVEMENTS ............. 50 

SURFACE DISTRESS PREDICTION MODELS FOR BOTH FLEXIBLE 

AND RIGID PAVEMENTS................................................................................. 51 

TRANSVERSE JOINT FAULTING PREDICTION MODELS FOR 

RIGID PAVEMENTS .......................................................................................... 66 

6. ENVIRONMENTAL PERFORMANCE COMPARISONS........................................ 69 

PAVEMENT ROUGHNESS COMPARISONS FOR FLEXIBLE 

PAVEMENTS....................................................................................................... 74 

PAVEMENT ROUGHNESS COMPARISONS FOR RIGID PAVEMENTS .... 76 

RUT DEPTH COMPARISONS FOR FLEXIBLE PAVEMENTS ..................... 77 

FATIGUE AND WHEELPATH CRACKING SURFACE DISTRESS 

COMPARISONS FOR FLEXIBLE PAVEMENTS ............................................ 80 

TRANSVERSE CRACKING SURFACE DISTRESS COMPARISONS 

FOR FLEXIBLE PAVEMENTS.......................................................................... 81 

LONGITUDINAL CRACKING SURFACE DISTRESS COMPARISONS 

FOR RIGID PAVEMENTS.................................................................................. 84 

TRANSVERSE CRACKING SURFACE DISTRESS COMPARISONS 

FOR RIGID PAVEMENTS.................................................................................. 86 
iv 

TRANSVERSE JOINT FAULTING COMPARISONS FOR RIGID 

PAVEMENTS....................................................................................................... 87 

7. INDEPTH AGENCY COMPARISONS ...................................................................... 91 

8. LOCAL ADAPTATIONS OF EMPIRICAL PAVEMENT DESIGN PRACTICES 

AND MATERIALS STANDARDS................................................................. 99 

LOCAL ADAPTATIONS OF PAVEMENT DESIGN PRACTICES ............... 101 

LOCAL ADAPTATIONS OF MATERIAL STANDARDS ............................. 107 

9. COST CONSIDERATION......................................................................................... 109 

10. APPLICATION TO MECHANISTIC DESIGN...................................................... 121 

11. APPLICATION TO PAVEMENT MANAGEMENT ............................................. 129 

12. KEY FINDINGS....................................................................................................... 135 

13. SUMMARY AND CONCLUSIONS ....................................................................... 137 

APPENDIX A. LITERATURE REVIEW...................................................................... 143 

THE EFFECTS OF FREEZE-THAW PERIODS ON A TEST PAVEMENT 

IN THE DANISH ROAD TESTING MACHINE.............................................. 143 

A DETERIORATION MODEL FOR PAVEMENTS IN FROST 

CONDITIONS .................................................................................................... 144 

ANALYSIS OF SEASONAL PAVEMENT DETERIORATION..................... 144 

DEVELOPMENT OF PERFORMANCE PREDICTION MODELS FOR 

DRY NO FREEZE AND DRY FREEZE ZONES USING LTPP DATA ......... 145 

DETERMINATION OF THE CRITICAL THAW-WEAKENED PERIOD 

IN ASPHALT PAVEMENT STRUCTURES.................................................... 146 

CALCULATED MAXIMUM FROST DEPTHS AT MN/ROAD 

WINTERS 1993–1994, 1994–1995, AND 1995–1996 ...................................... 147 

PARKS HIGHWAY LOAD RESTRICTION FIELD DATA ANALYSIS: 

A CASE STUDY ................................................................................................ 148 

COMMON CHARACTERISTICS OF GOOD AND POORLY 

PERFORMING PCC PAVEMENTS ................................................................. 148 

DETERMINATION OF FROST PENETRATION IN LTPP SECTIONS, 

FINAL REPORT ................................................................................................ 149 

DEVELOPMENT OF A PAVEMENT RUTTING MODEL FROM 

EXPERIMENTAL DATA.................................................................................. 150 

ANALYSIS OF EXPERIMENTAL PAVEMENT FAILURE DATA 

USING DURATION MODELS......................................................................... 151 

PAVEMENT PERFORMANCE DURING THAW WEAKENING ................. 151 
v 

EFFECTS OF FROST HEAVE ON THE LONGITUDINAL PROFILE OF 

ASPHALT CONCRETE PAVEMENTS IN COLD REGIONS........................ 152 

THERMAL ASPECT OF FROST-THAW PAVEMENT DIMENSIONING: 

IN SITU MEASUREMENT AND NUMERICAL MODELING ...................... 152 

PROBABILISTIC ANALYSIS OF HIGHWAY PAVEMENT LIFE FOR 

ILLINOIS............................................................................................................ 153 

EFFECTS OF ENVIRONMENTAL FACTORS ON PAVEMENT 

PERFORMANCE-THE INITIAL EVALUATION OF THE LTPP SPS-8 

EXPERIMENT ................................................................................................... 154 

LTPP DATA ANALYSIS: INFLUENCE OF DESIGN AND 

CONSTRUCTION FEATURES ON THE RESPONSE AND 

PERFORMANCE OF NEW FLEXIBLE AND RIGID PAVEMENTS ............ 154 

APPENDIX B. PERFORMANCE PREDICTION MODELS ....................................... 157 

ABSOLUTE IRI PREDICTION MODEL FOR FLEXIBLE PAVEMENTS.... 158 

Example of Absolute IRI Prediction Model for Flexible Pavements ..... 159 

ABSOLUTE IRI PREDICTION MODEL FOR RIGID PAVEMENTS ........... 161 

Example of Absolute IRI Predictions Model for Rigid Pavements........ 163 

FWPC PREDICTION MODEL FOR FLEXIBLE PAVEMENTS 

(DEDUCT VALUE) ........................................................................................... 164 

FWPC PREDICTION MODEL FOR FLEXIBLE PAVEMENTS 

(PERCENTAGE WHEELPATH AREA)........................................................... 166 

Example for FWPC Prediction Model for Flexible Pavements.............. 167 

TC PREDICTION MODEL FOR FLEXIBLE PAVEMENTS.......................... 169 

Example for TC Prediction Model for Flexible Pavements.................... 171 

LC PREDICTION MODEL FOR RIGID PAVEMENTS.................................. 172 

Example for LC Prediction Model for Rigid Pavements........................ 173 

TC PREDICTION MODEL FOR RIGID PAVEMENTS.................................. 175 

Example for TC Prediction Model for Rigid Pavements........................ 175 

RUT DEPTH PREDICTION MODEL FOR FLEXIBLE PAVEMENTS......... 177 

Example for Rut Depth Prediction Model for Flexible Pavements ........ 179 

TRANSVERSE JOINT FAULTING PREDICTION MODEL FOR RIGID 

PAVEMENTSEFFECTS OF FROST HEAVE ON THE LONGITUDINAL 

PROFILE OF ASPHALT CONCRETE PAVEMENTS IN COLD 

REGIONS ........................................................................................................... 152 
vi 

THERMAL ASPECT OF FROST-THAW PAVEMENT DIMENSIONING: 

IN SITU MEASUREMENT AND NUMERICAL MODELING ...................... 152 

PROBABILISTIC ANALYSIS OF HIGHWAY PAVEMENT LIFE FOR 

ILLINOIS............................................................................................................ 153 

EFFECTS OF ENVIRONMENTAL FACTORS ON PAVEMENT 

PERFORMANCE-THE INITIAL EVALUATION OF THE LTPP SPS-8 

EXPERIMENT ................................................................................................... 154 

LTPP DATA ANALYSIS: INFLUENCE OF DESIGN AND 

CONSTRUCTION FEATURES ON THE RESPONSE AND 

PERFORMANCE OF NEW FLEXIBLE AND RIGID PAVEMENTS ............ 154 

ABSOLUTE IRI PREDICTION MODEL FOR FLEXIBLE PAVEMENTS.... 158 

ABSOLUTE IRI PREDICTION MODEL FOR RIGID PAVEMENTS ........... 161 

FWPC PREDICTION MODEL FOR FLEXIBLE PAVEMENTS 

(DEDUCT VALUE) ........................................................................................... 164 

FWPC PREDICTION MODEL FOR FLEXIBLE PAVEMENTS 

(PERCENTAGE WHEELPATH AREA)........................................................... 166 

TC PREDICTION MODEL FOR FLEXIBLE PAVEMENTS.......................... 169 

LC PREDICTION MODEL FOR RIGID PAVEMENTS.................................. 172 

TC PREDICTION MODEL FOR RIGID PAVEMENTS.................................. 175 

RUT DEPTH PREDICTION MODEL FOR FLEXIBLE PAVEMENTS......... 177 

Transverse Joint Faulting Prediction Model for Rigid Pavements .........180 

Example for Fault Prediction Model for Rigid Pavements..................... 180 

APPENDIX C. AGENCY CLIMATIC INFORMATION ............................................. 183 

APPENDIX D. QUESTIONNAIRE SENT TO POOLED FUND STATES................. 193 

POOLED FUND STATES QUESTIONNAIRE................................................ 194 

APPENDIX E. RESPONSES RECEIVED FROM POOLED FUND STATES............ 197 

APPENDIX F. SPECIFICATION AND PAVEMENT DESIGN SUMMARIES ......... 229 

APPENDIX G: NCHRP 1-37A CALIBRATION FLOWCHART SAMPLE ............... 239 

REFERENCES ............................................................................................................... 241 
vii 

LIST OF FIGURES 

Figure 1. Graph. Plot of measured maximum frost depth to FI........................................ 15 

Figure 2. Graph. Individual distress deduct curves........................................................... 21 

Figure 3. Graph. Sample box plot..................................................................................... 30 

Figure 4. Scatter Plot. Sample augmented partial residual plot........................................ 32 

Figure 5. Graphs. Assumption validity check for absolute IRI model (before 

transformation).................................................................................................. 37 

Figure 6. Graphs. Assumption validity check for absolute IRI model. (after natural 

logarithm transformation of the performance measure). .................................. 38 

Figure 7. Scatter plot. Outlier-influential observation detection plot. .............................. 40 

Figure 8. Scatter plot. Observed versus predicted values of absolute IRI (shifted) 

using the robust method.................................................................................... 42 

Figure 9. Scatter plot. Observed versus predicted values of absolute IRI (shifted) 

using the GLM method..................................................................................... 42 

Figure 10. Graph. Example of predicted (without shifting) and observed values for 

test section 307066.......................................................................................... 44 

Figure 11. Graph. Example of predicted (shifted) and observed values for test 

section 307066. ............................................................................................... 44 

Figure 12. Scatter plot. Flexible IRI model without shifting............................................ 45 

Figure 13. Scatter plot. Flexible IRI model (shifted)........................................................ 45 

Figure 14. Scatter plot. Rigid IRI model without shifting. ............................................... 46 

Figure 15. Scatter plot. Rigid IRI model (shifted)............................................................ 47 

Figure 16. Scatter plot. Flexible IRI model with linear IRI-age relationship.................. 48 

Figure 17. Scatter plot. Flexible IRI model with IRI-exponential age relationship. ....... 48 

Figure 18. Scatter plot. Actual and predicted IRI values for test section 011001 

using IRI-exponential age relationship model. ............................................... 49 

Figure 19. Scatter Plot. Rigid IRI model with linear IRI-age relationship...................... 49 

Figure 20. Scatter plot. Rut depth model with linear rut–age relationship...................... 50 

Figure 21. Scatter plot. Rut depth model with rut–natural logarithm age 

relationship...................................................................................................... 51 

Figure 22. Scatter plot. Measured FWPC deduct values. ................................................. 52 

Figure 23. Graph plot. Measured FWPC values (using a subset of test sections). .......... 53 

Figure 24. Graph plot. Example of logistical analysis to predict distress initiation. ....... 54 
viii 

Figure 25. Graph plot. Observed FWPC deduct values for test section 100102. ............. 56 

Figure 26. Graph plot. Observed FWPC deduct values for test section 050121 (with 

regression line)................................................................................................ 57 

Figure 27. Scatter plot. FWPC model for flexible pavements with linear FWPC-age 

relationship...................................................................................................... 58 

Figure 28. Scatter plot. FWPC model for flexible pavements with FWPC-natural 

logarithm age relationship............................................................................... 58 

Figure 29. Scatter plot. TC model for flexible pavements with linear TC-age 

relationship...................................................................................................... 59 

Figure 30. Scatter plot. TC model for flexible pavements with TC-natural 

logarithm age relationship............................................................................... 60 

Figure 31. Scatter plot. CB model for rigid pavements with linear CB-age 

relationship...................................................................................................... 61 

Figure 32. Scatter plot. CB model for rigid pavements with CB-natural logarithm 

age relationship. .............................................................................................. 61 

Figure 33. Scatter plot. LC model for rigid pavements with linear LC-age 

relationship...................................................................................................... 62 

Figure 34. Scatter plot. LC model for rigid pavements with LC-natural logarithmage relationship. .............................................................................................. 63 

Figure 35. Scatter plot. TC model for rigid pavements with linear TC-age 

relationship...................................................................................................... 64 

Figure 36. Scatter plot. TC model for rigid pavements with TC-natural logarithm 

age relationship. .............................................................................................. 64 

Figure 37. Scatter plot. PUMP model for rigid pavements with linear PUMP-age 

relationship...................................................................................................... 65 

Figure 38. Scatter plot. PUMP model for rigid pavements with PUMP-natural 

logarithm age relations.................................................................................... 66 

Figure 39. Scatter plot. FLT model for rigid pavements with linear FLT-age 

relationship...................................................................................................... 67 

Figure 40. Scatter plot. FLT model for rigid pavements with FLT-natural logarithm 

age relationship. .............................................................................................. 67 

Figure 41. Scatter plot. Regional FI and FTCs values...................................................... 70 

Figure 42. Map. Geographic locations of climatic regions............................................... 70 

Figure 43. Scatter plot. Relationship between FI and FTCs............................................. 71 

Figure 44. Scatter chart. Mean predicted flexible pavement IRI values for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 75 
ix 

Figure 45. Bar chart. Predicted flexible pavement IRI values at 20 years for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 75 

Figure 46. Scatter graph. Mean predicted rigid pavement IRI values for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 76 

Figure 47. Bar chart. Predicted rigid pavement IRI values at 20 years for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 77 

Figure 48. Scatter graph. Mean predicted flexible pavement RUT values for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 78 

Figure 49. Bar chart. Predicted flexible pavement RUT values at 20 years for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 79 

Figure 50. Chart. Mean predicted flexible pavement FWPC values for each climatic 

region (BASE=DGAB/SG=FINE). ................................................................ 80 

Figure 51. Bar chart. Predicted flexible pavement FWPC values at 20 years for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 81 

Figure 52. Scatter chart. Mean-predicted flexible pavement TC values for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 82 

Figure 53. Bar chart. Predicted flexible pavement TC values at 20 years for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 83 

Figure 54. Scatter graph. Mean predicted rigid pavement LC values for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 85 

Figure 55. Bar chart. Predicted rigid pavement LC values at 25 years for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 85 

Figure 56. Scatter Graph. Mean predicted rigid pavement TC values for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 86 

Figure 57. Bar chart. Predicted rigid pavement TC values at 25 years for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 87 

Figure 58. Scatter chart. Mean predicted rigid pavement FLT values for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 88 

Figure 59. Bar chart. Predicted rigid pavement FLT values at 20 years for each 

climatic region (BASE=DGAB/SG=FINE).................................................... 89 

Figure 60. Scatter chart. Flexible pavement IRI for selected sites in each agency.......... 94 

Figure 61. Scatter chart. Flexible pavement RUT for selected sites in each agency....... 95 

Figure 62. Scatter chart. Flexible pavement TC for selected sites in each agency.......... 96 

Figure 63. Scatter graph. Flexible pavement FWPC for selected sites in each 

agency. ............................................................................................................ 96 

Figure 64. Scatter graph. FWPC predictions for sites 1001 and 6027 in Idaho. .............. 98 
x 

Figure 65. Scatter graph. Flexible TC predictions for the environments at sites 

0200 and 1004 in Michigan. ........................................................................... 98 

Figure 66. Photo. Road construction in Sweden with deep base section........................ 103 

Figure 67. Photo. Installation of longitudinal drainage to reduce frost heaving............. 104 

Figure 68. Diagram. Standard pavement section from a Midwestern State. .................. 106 

Figure 69. Diagram. Primary highway cross section...................................................... 112 

Figure 70. Diagram. Interstate highway, left section...................................................... 112 

Figure 71. Diagram. Interstate highway, right section.................................................... 113 

Figure 72. Distribution chart. Annualized costs for standard primary pavement 

sections.......................................................................................................... 117 

Figure 73. Distribution chart. Annualized costs for standard interstate pavement 

sections.......................................................................................................... 117 

Figure 74. Distribution chart. Annualized costs for mitigated primary pavement 

sections.......................................................................................................... 118 

Figure 75. Distribution chart. Annualized costs for mitigated interstate pavement 

sections.......................................................................................................... 119 

Figure 76. Graph. Comparison of fatigue cracking trends before and after local 

calibration. .................................................................................................... 126 

Figure 77. Graph. Comparison of rutting trends before and after local calibration........ 127 

Figure 78. Graph. Comparison of ride trends before and after local calibration............ 128 

Figure 79. Graph. Individual distress deduct curves....................................................... 130 

Figure 80. Graph. Example of fatigue cracking trends for different environments........ 131 

Figure 81. Chart. Fatigue cracking index trend for environmental case wet nofreeze............................................................................................................. 132 

Figure 82. Chart. Example of shifting trend line to fit index for a given location. ........ 133 

Figure 83. Map. Alaska geographic location of analysis test sections. .......................... 183 

Figure 84. Map. Idaho geographic location of analysis test sections. ............................ 184 

Figure 85. Map. Illinois geographic location of analysis test sections. .......................... 185 

Figure 86. Map. Indiana geographic location of analysis test sections. ......................... 186 

Figure 87. Map. Michigan geographic location of analysis test sections. ...................... 187 

Figure 88. Map. New York geographic location of analysis test sections...................... 188 

Figure 89. Map. North Carolina geographic location of analysis test sections. ............. 189 

Figure 90. Map. Ohio geographic locations of analysis test sections............................. 190 
xi 

Figure 91. Map. Pennsylvania geographic locations of analysis test sections................ 191 

Figure 92. Diagram. Typical section for rural primary (2 lanes) in Alaska.................... 200 

Figure 93. Diagram. Rigid pavement rural interstate typical section for Idaho. ............ 201 

Figure 94. Diagram. Flexible pavement rural interstate typical section for Idaho. ........ 201 

Figure 95. Diagram. Rigid pavement rural primary typical section for Idaho. .............. 202 

Figure 96. Flexible pavement rural primary typical section for Idaho. .......................... 202 

Figure 97. Diagram. Rigid pavement at LTPP site 163023 in Idaho.............................. 203 

Figure 98. Diagram. Flexible pavement at LTPP site 169032 in Idaho. ........................ 203 

Figure 99. Diagram. Typical portland cement concrete pavement section for 

New York...................................................................................................... 213 

Figure 100. Diagram. Typical hot-mix asphalt pavement section for New York........... 213 

Figure 101. Flowchart. Example of NCHRP 1-37A calibration methodology 

flowchart...................................................................................................... 239 
xii 

LIST OF TABLES 

Table 1. List of models and basic logistic and regression statistics.................................... 2 

Table 2. Wet freeze SMP sites.......................................................................................... 10 

Table 3. Wet no-freeze SMP sites..................................................................................... 11 

Table 4. Dry freeze SMP sites. ......................................................................................... 11 

Table 5. Number of pavement types in SMP sites............................................................ 12 

Table 6. SMP sites with measured frost depths. ............................................................... 14 

Table 7. LTPP experiments included in the analysis dataset............................................ 16 

Table 8. Sources of construction and rehabilitation dates. ............................................... 19 

Table 9. Fatigue and longitudinal wheelpath cracking for LTPP section 080501........... 22 

Table 10. Fatigue and longitudinal wheelpath cracking for LTPP section 068153......... 22 

Table 11. Material code classifications for each BASE type category............................. 25 

Table 12. BASE types assigned to structures with multiple base layers. ......................... 26 

Table 13. TST_LO5B data for test section 481094. ......................................................... 26 

Table 14. Summary of explanatory variables. .................................................................. 29 

Table 15. Sample of statistical parameters. ...................................................................... 31 

Table 16. Sample of correlation matrix. ........................................................................... 35 

Table 17. Regression coefficients with P-value statistics................................................. 36 

Table 18. Criteria to warrant additional investigation of unrecorded pavement 

improvements.................................................................................................... 39 

Table 19. Example of probability level effect on logistic prediction. .............................. 55 

Table 20. Overview of climatic scenarios for flexible pavements.................................... 72 

Table 21. Overview of climatic scenarios for rigid pavements. ....................................... 72 

Table 22. Details on selection of environmental variables............................................... 73 

Table 23. Measured environmental data for LTPP sites................................................... 91 

Table 24. Primary highway flexible pavement design summary...................................... 99 

Table 25. Primary highway rigid pavement design summary. ....................................... 100 

Table 26. Interstate highway flexible pavement design summary.................................. 100 

Table 27. Interstate highway rigid pavement design summary. ..................................... 101 

Table 28. Hot-mix asphalt concrete binder grading and mix designs used by the 

PFS for surfacing courses. .............................................................................. 107 
xiii 

Table 29. Action timing for individual distress categories............................................. 111 

Table 30. Overlay timing for the five environmental zones. .......................................... 113 

Table 31. Distribution of performance life for probabilistic analysis............................. 114 

Table 32. Unit cost information...................................................................................... 115 

Table 33. Deterministic LCCA results for standard sections. ........................................ 116 

Table 34. Deterministic LCCA results for mitigated sections........................................ 116 

Table 35. Summary of statistical comparisons. .............................................................. 135 

Table 36. Overview of developed performance models. ................................................ 138 

Table 37. Coefficients for flexible IRI model................................................................. 159 

Table 38. Example pavement section information. ........................................................ 160 

Table 39. Coefficients for rigid IRI model. .................................................................... 162 

Table 40. Example pavement section information. ........................................................ 163 

Table 41. Coefficients for flexible FWPC (deduct value) logistic model. ..................... 164 

Table 42. Coefficients for flexible FWPC (deduct value) regression model................. 165 

Table 43. Coefficients for flexible FWPC (percentage of wheelpath) logistic 

model. ............................................................................................................. 166 

Table 44. Coefficients for flexible FWPC (percentage of wheelpath) regression 

model. ............................................................................................................. 167 

Table 45. Example pavement section information. ........................................................ 168 

Table 46. Coefficients for flexible TC logistic model. ................................................... 169 

Table 47. Coefficients for flexible TC regression model. .............................................. 170 

Table 48. Example pavement section information. ........................................................ 171 

Table 49. Coefficients for rigid LC regression model. ................................................... 173 

Table 50. Example pavement section information. ........................................................ 174 

Table 51. Coefficients for rigid TC regression model. ................................................... 175 

Table 52. Example pavement section information. ........................................................ 176 

Table 53. Coefficients for flexible RUT model.............................................................. 178 

Table 54. Example pavement section information. ........................................................ 179 

Table 55. Coefficients for rigid FLT model.................................................................... 180 

Table 56. Example pavement section information. ........................................................ 181 

Table 57. Alaska environmental and pavement structure information for test 

sections............................................................................................................ 183 
xiv 

Table 58. Idaho Environment and pavement structure information for test sections. .... 184 

Table 59. Illinois environment and pavement structure information for test 

sections............................................................................................................ 185 

Table 60. Indiana environment and pavement structure information for test 

sections............................................................................................................ 186 

Table 61. Michigan environment and pavement structure information for test 

sections............................................................................................................ 187 

Table 62. New York environment and pavement structure information for test 

sections............................................................................................................ 188 

Table 63. North Carolina environment and pavement information for analysis test 

sections............................................................................................................ 189 

Table 64. Ohio environment and pavement structure information for analysis test 

sections............................................................................................................ 190 

Table 65. Pennsylvania environment and pavement structure information for 

analysis tests sections...................................................................................... 191 

Table 66. Average unit prices for Illinois. ...................................................................... 206 

Table 67. PCC thickness table for New York................................................................. 214 

Table 68. HMA thickness table for New York (Mr=28 MPa). ....................................... 214 

Table 69. HMA thickness table for New York (Mr=34 MPa). ....................................... 215 

Table 70. HMA thickness table for New York (Mr=41 MPa). ....................................... 215 

Table 71. HMA thickness table for New York (Mr=48 MPa). ....................................... 216 

Table 72. HMA thickness table for New York (Mr=55 MPa). ....................................... 216 

Table 73. HMA thickness table for New York (Mr=62 MPa). ....................................... 217 

Table 74. Pavement structure information for rural interstate in Pennsylvania. ............ 224 

Table 75. Pavement structure information for rural primary in Pennsylvania. .............. 225 

Table 76. Average unit prices for Pennsylvania. ............................................................ 226 

Table 77. AC wearing course specification summary. ................................................... 230 

Table 78. AC wearing course specification summary (continued)................................. 230 

Table 79. AC base course specification summary.......................................................... 231 

Table 80. AC base course specification summary (continued)....................................... 231 

Table 81. Asphalt-treated permeable base course specification summary. .................... 232 

Table 82. Unbound base course specification summary................................................. 232 

Table 83. Subbase course specification summary. ......................................................... 233 
xv 

Table 84. Select subgrade specification summary.......................................................... 233 

Table 85. Overview of rural interstate flexible pavement design................................... 234 

Table 86. Overview of rural interstate rigid pavement design........................................ 235 

Table 87. Overview of principal flexible pavement design............................................ 236 

Table 88. Overview of principal rigid pavement design................................................. 237 
xvi 

List of Acronyms 

AASHTO American Association of State Highway and Transportation 

Officials 

AASHO American Association of State Highway Officials 

AC Asphalt concrete 

ACTHICK Asphalt layer thickness 

AIRI Absolute international roughness index 

ATB Asphalt-treated base 

BASE Base material type 

BC Block cracking 

CB Corner breaking 

CI Annual cooling index 

COTR Contracting officer’s technical representative 

CRCP Continuously reinforced concrete pavement 

CSM Chemically stabilized material 

D Slab thickness 

DGAB Dense graded-aggregate base 

DIRI Change in international roughness index 

EALF Equivalent axle load factor 

ESAL Equivalent single axle load 

FC Fatigue cracking 

FHWA Federal Highway Administration 

FI Annual freezing index 

FLT Accumulation of faulting 

FTC Freeze-thaw cycle 

FWPC Combined LWP and FC 

GLM General linear model 

GPS General Pavement Study 

HMA Hot-mix asphalt 

HMAC Hot-mix asphalt concrete 

IMS Information management system 

IRI International roughness index 

JPCP Jointed plain concrete pavement 

JRCP Jointed reinforced concrete pavement 

LC Longitudinal cracking 

L.A. wear values Los Angeles wear values 

LCCA Life cycle cost analysis 

LEDT Logarithm of ESAL divided by depth 

LESN Logarithm of ESAL divided by structural number 

LTPP Long-Term Pavement Performance (program) 

LWP Longitudinal wheelpath cracking 

MBE Modified Berggren equation 

M-E Mechanistic-empirical 

MIRI Initial recorded international roughness index values 
xvii 

Mn/ROAD Minnesota Road Research Project 

Mr Resilient modulus 

NCHRP National Cooperative Highway Research Program 

NONBIT Nonbituminous-treated base 

NYSDOT New York State Department of Transportation 

PATB Permeable asphalt-treated base 

PCC Portland cement concrete 

PCCP Portland cement concrete pavement 

PCI Pavement condition index 

PFS Pooled fund States 

PG Performance grade 

PMS Pavement management systems 

PRECIP Annual precipitation data 

PUMP Pumping/water bleeding 

RMSE Root mean squared error 

RTM Road testing machine 

RUT Rut depth index value 

SAS® SAS software 

SG Subgrade material type 

SHA State Highway Agency 

SHRP Strategic Highway Research Program 

SMP Seasonal monitoring program 

SN Structural number 

SPS Specific Pavement Studies 

TDR Time domain reflectometry 

TC Transverse cracking 

TSR Tensile strength ratio 

WSDOT Washington State Department of Transportation 

 

1 

EXECUTIVE SUMMARY 

Pavements subjected to frost effects have different service lives than do similar 

pavements with no exposure to frost; however, limited national research is available 

quantifying the effect frost has on pavement performance let alone the costs resulting 

from reduced service life. This study provides some insight into pavement performance 

and service life, considering conditions of both deep-frost and moderate-frost depth with 

multiple freeze-thaw cycles (FTC). 

The study included a review of all available relevant literature to provide guidance and to 

support the project work. Literature directly related to this investigation was quite 

limited, with most of the literature regarding frost effects dealing with quantifying the 

change in material properties and performance characterization on particular projects. In 

addition, relatively limited information was found on modeling pavement performance in 

frost areas using Long-Term Pavement Performance (LTPP) data. State Highway Agency 

(SHA) Web sites were also reviewed to accumulate reports documenting studies of frost 

mitigation techniques. 

To study pavement performance in the various frost settings, models were developed 

using multivariate regression analysis. LTPP data from General Pavement Study (GPS) 

projects 1, 2, 3, and 6 as well as Specific Pavement Study (SPS) experiments 1, 2, and 8 

were used to generate the models. Data from more than 520 test sections were used in 

developing the prediction models for flexible pavements, while approximately 270 test 

sections were used for rigid pavement modeling. More than 20 models were developed to 

represent the rate of pavement deterioration with age unique to environmental regions. Of 

these, nine models were selected that were determined to best predict basic pavement 

trends with time. A summary of these nine models can be found in Table 1. 

Using the models presented above, statistical comparisons of pavement performance 

were made for the following five climatic region scenarios: 

• Deep-freeze, wet (low FTC). 

• Moderate-freeze, wet (high FTC). 

• No-freeze, wet. 

• Deep-freeze, dry (low FTC). 

• Moderate-freeze, dry (high FTC). 

All of the models (with the exception of flexible pavement roughness) predicted 

significantly different performance, at 95 percent confidence, between two or more of the 

climatic scenarios.
2 

Table 1. List of models and basic logistic and regression statistics. 

Model Pavement Model 

Type 

Logistic 

Cutoff 

Probability 

Percent 

Correct 

RSquared Number of 

Observations

Roughness Flexible 

Regression 

(shifted) NA NA 0.78 4544 

Roughness Rigid Regression NA NA 0.78 2652 

Rut Depth Flexible Regression NA NA 0.45 1966 

Faulting Rigid Regression NA NA 0.47 1384 

Logistic 0.7 72.6 NA 1977 Fatigue and 

Wheelpath 

Cracking 

Flexiblededuct Regression NA NA 0.63 1486 

Logistic 0.7 72.6 NA 1977 Fatigue and 

Wheelpath 

Cracking 

Flexiblepercent Regression NA NA 0.49 1481 

Transverse Logistic 0.7 78.4 NA 1920 

Cracking Flexible Regression NA NA 0.71 1077 

Longitudinal Logistic 0.55 63.5 NA 475 

Cracking Rigid Regression NA NA 0.38 240 

Transverse Logistic 0.6 63.5 NA 489 

Cracking Rigid Regression NA NA 0.54 228 

To gain an understanding of state design practices, a questionnaire was developed and 

sent to the pooled fund State participants. Basic information on standard roadway 

sections including structural design for given scenarios, standard specifications, and test 

procedures were requested. 

Responses to the survey revealed that there is a large variation in the roadway section for 

similar design situations. However, most of the States in the study experiencing deep 

frost did include a construction specification requiring additional surfacing or the 

replacement of frost-susceptible soils with frost-free surfacing for a depth of 1 to 2 meters 

(m) (3 to 6 feet (ft)). 

Agency responses also revealed that the use of Superpave mix design procedures has, to a 

large extent, eliminated local adaptations in mix designs and specifications that might 

have provided improved performance in areas of deep frost penetration or numerous 

FTCs, or both. The Superpave® mix design procedure does not differentiate between mix 

designs where pavements will be exposed to numerous FTCs and those that will 

experience little or no FTCs. Because many SHAs are in the process of adopting the 

Superpave binder specifications as well as the mix design procedures, local adaptations 

as far as mix designs and specifications were not found that would indicate improved 

pavement performance in areas with either deep frost penetration or numerous FTCs. 

An additional objective of the study involved evaluating the costs associated with 

performance differences in the various environments. Life cycle cost analysis (LCCA) 
3 

was used to evaluate pavement costs in the various climatic settings because it produces 

comparable results (i.e., equivalent uniform annual costs). Comparisons were made using 

both deterministic and probabilistic methods of LCCA. 

Standard flexible pavement sections were developed based on the 1993 AASHTO Guide 

for Design of Pavement Structures design procedures(1) and using input variables from 

the questionnaire. A cost comparison was performed using this standard section for all 

environmental zones; therefore, the initial and rehabilitation costs were constant for all 

regions. Cost differences were the result of changes in treatment timing because of 

performance variations between the regions. Predictions from the models were used to 

determine treatment timing for each climatic scenario. 

To account for local adaptations used to mitigate damage associated with freezing and 

thawing climates, an additional cost evaluation was performed in which the initial costs 

of the deep- and moderate-freeze regions included extra frost-free material (i.e., unbound 

base) to obtain a pavement structure with a total depth of 1 m (3 ft). Based on responses 

from the participating Agencies, this is a typical frost-free depth for many SHAs 

experiencing 1 to 1.5 m (3 to 4 ft) of frost penetration. The initial construction costs for 

the no-freeze region did not include additional base material. 

Using the standard section for all regions resulted in costs that were not significantly 

different for the five climatic scenarios. When the cost of additional surfacing was 

considered, the life cycle costs in the no-freeze region were significantly lower than in 

the deep- and moderate-freeze regions. 

Consideration was given to the use of the developed performance models in the 

implementation of mechanistic-empirical (M-E) design procedures. The National 

Cooperative Highway Research Program (NCHRP) Guide for Mechanistic-Empirical 

Design of New and Rehabilitated Pavement Structures,

(2) Project 1-37A final report, was 

developed using damage models that represent average pavement damage trends for the 

entire United States. The models developed in this project can be used to predict average 

rutting or fatigue cracking trends for a specific regional or statewide environment. In 

turn, these estimates can be used in the iteration and verification process described in the 

NCHRP 1-37A Guide designprocedure to determine if modified calibration factors are 

required in the design program for the specific environment. An example of how to use 

the models from this study to provide regional calibration is described in the report. 

The models developed for this project will also be useful in pavement management 

applications. The pavement distress trend models developed in this study can be used to 

provide general pavement deterioration trends for a specific environment. These trends 

can be used to develop a family of curves for use in a pavement management system 

(PMS) where an SHA or local agency does not have sufficient data to develop those 

curves. 

5 

1. INTRODUCTION 

It is well recognized that pavements subjected to frost effects have different service lives as 

compared to similar pavements that are not subjected to frost effects. However, there is a need to 

better understand the failure mechanisms (particularly the impact of multiple FTCs as compared 

to deep frost penetration) and how they are mitigated by various compensatory strategies 

implemented throughout the pavement community. Observably, deep frost penetration and 

extensive frost-thaw cycles have a pronounced effect on the service pavements provide. There 

has been very limited research on quantifying the effect frost has on pavement performance, as 

well as on the cost of that effect on reduced service life and the additional costs to maintain those 

pavements in serviceable condition. This study will help provide some insight into the total cost 

of frost action as it applies to pavement performance and service life, considering both deep-frost 

conditions and moderate-frost conditions with multiple FTCs. 

Following is a list of project’s research objectives: 

• Quantify the effect of seasonal frost penetration on the rate of loss of pavement 

performance for environments where deep, sustained frost penetration occurs, and for 

environments where multiple shallow FTCs occur. 

• Establish the extent to which local adaptations of materials standards and pavement 

thickness designs have compensated for or mitigated the effect of seasonal frost 

penetration. 

• Determine financial effects associated with freeze-thaw mitigation in the construction and 

rehabilitation of pavements. 

In addition to the objectives listed above, the use of the models developed for the project in PMS 

applications and NCHRP 1-37A Guide design procedures was explored. 

This study was structured with two phases. The first phase consisted of six tasks that essentially 

confirmed that the project could be accomplished with the data available. Phase 2 consisted of an 

analysis of the data collected. Work conducted in phase 1, and the resulting findings were used to 

tailor analysis conducted in phase 2. 

7 

2. BACKGROUND 

In phase 1 a literature review was conducted to provide guidance and support to the work on this 

project. The available literature directly related to this investigation was quite limited. While 

most of the literature regarding frost effects deals with quantifying the change in material 

properties and performance characterization on particular projects, studies have been conducted 

on modeling pavement performance using LTPP data. The literature review is included in 

appendix A. 

As part of phase 1, the contractor was asked to complete the following task: 

Identify the specific LTPP data to be used in the analysis, acquire the data, and process it 

as necessary to create the analysis database to be used in subsequent analysis. 

The analysis database was first developed to evaluate the potential of using only data from 

Seasonal Monitoring Program (SMP) test sections for the study. This evaluation showed that 

including additional test sections beyond SMP sites would increase the dataset to better represent 

the large number of variables and lead to improved results. With the approval from Federal 

Highway Administration (FHWA) and the pooled fund States (PFS) panel, the sample set was 

expanded to include GPS-1 and GPS-6 experiments for phase 1 analysis. The analysis database 

was expanded for phase 2, which included accumulating additional variables, combining data 

that had been segregated into environmental zones for phase 1, and thoroughly reviewing the 

data. Chapter 3 contains a more detailed discussion of the database development. 

Based on phase 1 findings, it was evident that the available data could support the study, and that 

performance differences did exist. Because the analysis dataset required additional data, it was 

proposed that it be expanded to include not only GPS-1 and GPS-6 sites, but also GPS-2, SPS-1, 

and SPS-8 projects for an investigation of AC sections for phase 2. Similarly, GPS-3, SPS-2, and 

SPS-8 test sections were proposed to model PCC pavements. The analysis datasets for phase 2 

were designed to combine the data into a more comprehensive form where the environmental 

factors could be fully addressed in a statistical analysis. This approach was presented to, and 

accepted by, the PFS panel. 

Another consideration from the phase 1 study was the use of frost depths, information that is 

collected only at SMP sites. The database could not be expanded from SMP sites without using a 

substitute for frost depths. The annual freezing index (FI) was shown to correlate quite well to 

frost depths, and it was subsequently used in both the phase 1 and phase 2 studies to represent 

relative freezing conditions. A complete analysis of these findings is reported in chapter 3 

An investigation of the interaction between FTCs and FI found that moderate- and deep-freeze 

zones experience approximately the same number of annual freeze cycles; therefore, the initial 

assumption that each was mutually exclusive, that the moderate-freeze zone would experience 

multiple FTCs while sections in the deep-freeze zone would experience few cycles, is not 
8 

confirmed by the data. Consideration of this finding in phase 2 analysis was essential to making 

performance comparisons, described in chapter 5. 

An initial trend analysis was conducted to determine if the rate of deterioration varied between 

environmental settings. Linear regression was performed on the preliminary dataset and 

differences in performance were observed; however, because of the large spread of data, most 

differences would not likely be statistically significant at the 95-percent confidence interval. 

Moreover, simple linear regression does not consider the large amount of independent variables 

that contribute to pavement performance. Considering this, a more complex investigation 

(consisting of multivariate regression analysis) was initiated in phase 2. 

A preliminary cost investigation was performed to determine the amount of cost data available 

for future analysis. Sufficient amounts of cost data were found to be available to make cost 

comparisons between States in different frost conditions; however, the unit descriptions were not 

consistent among the agencies. Therefore, phase 2 work consisted of determining standard 

roadway sections and identifying unit descriptions that were consistent. This was accomplished 

through inquiries with each SHA. 
9 

3. DEVELOPMENT OF ANALYSIS DATASET 

In the initial work on this project, the data analysis team specifically developed SMP site data as 

called for in the task order. The task order clearly identified SMP sites to be the basis of this 

project. Task 3 had the following requirement: 

Make a comparison of performance data from the LTPP Seasonal Monitoring Program 

(SMP) sites that are located in the southern reaches of the wet-freeze zone or the 

northern reaches of the wet no-freeze zone versus those sites that are further north in the 

wet-freeze zone. 

The task order also stated: 

Initial trend analysis studies should determine whether the SMP data support the 

contention that the rate of accumulation of pavement distress is greater (more rapid) in 

climatic zones where there are a large number of annual freeze-thaw cycles versus deep 

frost penetration. 

It is assumed that the intent of using SMP data for this study was to make use of frost depth 

measurements taken at the sites, which were based on resistivity, temperature, and moisture data. 

SMP sites are the only source of actual frost penetration measurements in the LTPP database. 

Tables 2, 3, and 4 list all SMP sites in the wet freeze, wet no-freeze, and dry freeze zones, 

respectively. Included in the tables are the experiment designations of each section, which are 

based on the type of pavement structure as defined in the Long Term Pavement Performance 

Information Management System Pavement Performance Database User Guide.

(3) Table 5 

summarizes the number of SMP test sections in each climatic zone separated by experiment type. 

The number of test sites listed in table 5 includes all SMP sites. The actual number of potential 

sites for use in this study is more limited when considering only the following factor: 

...sites that are located in the southern reaches of the wet-freeze zone or the northern 

reaches of the wet no-freeze zone versus those sites that are farther north in the wet 

freeze zone. 

There are GPS-1 sites in the wet no-freeze zone that could potentially be included in the 

“northern reaches of the wet no-freeze zone.” Combining these sites with the nine GPS-1 sites in 

the wet freeze zone provides a total of 13 test sections in the wet freeze zone to analyze. Six of 

the SPS sites could also be used; however, these SPS projects were constructed relatively 

recently and many have not yet developed a clear damage trend. 
10 

Table 2. Wet freeze SMP sites. 

State State 

Code 

SHRP ID Experiment 

CT 09 1803 GPS-1 

IN 18 3002 GPS-3 

KS 20 4054 GPS-4 

ME 23 1026 GPS-1 (and GPS-6B) 

MD 24 1634 GPS-2 

MA 25 1002 GPS-1 

MN 27 1018 GPS-1 

MN 27 1028 GPS-1 

MN 27 4040 GPS-4 

MN 27 6251 GPS-1 

NE 31 3018 GPS-3 

NH 33 1001 GPS-1 

NY 36 4018 GPS-4 

OK 40 4165 GPS-2 

PA 42 1606 GPS-4 

VT 50 1002 GPS-1 

MB 83 3802 GPS-3 

ON 87 1622 GPS-1 

PQ 89 3015 GPS-3 

DE 10 0102 SPS-1 

NE 31 0114 SPS-1 

NY 36 0801 SPS-8 (AC) 

OH 39 0204 SPS-2 

Note: GPS-1 AC over granular base 

 GPS-2 AC over bound base 

 GPS-3 JPCP 

 GPS-4 JRCP 

 GPS-6 AC overlay over existing AC 

 SPS-1 AC 

 SPS-2 PCC 

 SPS-8 Environmental (AC and PCC) 
11 

Table 3. Wet no-freeze SMP sites. 

State State Code SHRP ID Experiment 

GA 13 1005 GPS-1 

GA 13 1031 GPS-1 

GA 13 3019 GPS-3 

MS 28 1016 GPS-2 

MS 28 1802 GPS-2 

NC 37 1028 GPS-1 

TX 48 1060 GPS-1 

TX 48 1068 GPS-1 

TX 48 1077 GPS-1 

TX 48 1122 GPS-1 

TX 48 3739 GPS-1 

TX 48 4142 GPS-4 

TX 48 4143 GPS-4 

WA 53 3813 GPS-3 

AL 01 0101 SPS-1 

AL 01 0102 SPS-1 

NC 37 0201 SPS-2 

NC 37 0205 SPS-2 

NC 37 0208 SPS-2 

NC 37 0212 SPS-2 

VA 51 0113 SPS-1 

VA 51 0114 SPS-1 

Table 4. Dry freeze SMP sites. 

State State Code SHRP ID Experiment 

CO 08 1053 GPS-1 

ID 16 1010 GPS-1 

ID 16 3023 GPS-3 

MT 30 8129 GPS-1 

SD 46 9187 GPS-1 

UT 49 1001 GPS-1 

UT 49 3011 GPS-3 

WY 56 1007 GPS-1 

MB 83 1801 GPS-1 

SK 90 6045 GPS-1 (and GPS-6B) 

MT 30 0114 SPS-1 

NV 32 0101 SPS-1 

NV 32 0204 SPS-2 

SD 46 0804 SPS-8 (AC) 
12 

Table 5. Number of pavement types in SMP sites. 

Experiment Wet Freeze Wet No-Freeze Dry Freeze Total 

GPS-1 9 9 8 25 

GPS-2 2 2 0 4 

GPS-3 4 2 2 8 

GPS-4 3 2 0 5 

GPS-6 1 0 1 2 

SPS-1 2 4 2 8 

SPS-2 1 4 1 6 

SPS-8 1 0 1 2 

The number of test sections must be compared with the number of variables that can contribute 

to pavement performance. Task 3 calls for the consultant to conduct a trend analysis in which the 

rates of distress accumulation will be analyzed considering the following factors: 

• Number and duration of partial thaw events. 

• Tradeoff between material qualities. 

• Availability of moisture beneath the pavement. 

• Severity of frost penetration. 

In addition, task 5 asks the contractor to quantify the effect on pavement performance based on 

the analysis of the following factors: 

• Pavement types (rigid, flexible). 

• Climatic data (rainfall, FI, and thawing index from temperature data). 

• Frost depth (temperature sensors and resistivity data). 

• Deflection data (stresses and strains calculated from layer material properties). 

• Performance data (distress and permanent deformation). 

• Soils and material properties. 

• Traffic data. 

From these lists, a total of nine variables were incorporated into the dataset: 

• Rainfall. 

• FI. 

• Thawing index. 

• Annual cooling index (CI) (added by consultant to indicate heat loading). 

• Frost depth. 

• Pavement structure (either structural number (SN) or deflection data). 
13 

• Subgrade properties. 

• Pavement layer material properties. 

• Traffic data. 

Nineteen potential AC pavements and eleven potential PCC pavements do not compare well 

against the nine potential variables. For a reasonable statistical analysis, there should be a larger 

number of sites for each independent variable. Assuming a desired minimum of 10 to 20 sites for 

each independent variable, 90 to 180 sites would be desirable to meet the needs of this project. 

In addition, evaluation was performed to determine the actual number of SMP sites that were 

available with reasonable performance data. For the AC pavement sites, 9 sites of the 19 noted 

above had reasonably deterioration trends. At most, 15 sites could be included in the analysis 

equating to 8 sites in the deep-freeze environment, 4 sites in the moderate-freeze environment, 

and 3 sites in the no-freeze environment. With this breakdown, there are more independent 

variables than sites in either environment. This further illustrated the necessity to use additional 

LTPP test sections. 

The important question facing the analysis team was: “Could the objectives be met by expanding 

beyond the SMP sites?” As will be explained in the following paragraphs, the answer was a 

resounding “Yes.” 

Table 6 lists only the 23 reported SMP sites with measured, nonzero, frost depths. The actual 

measured frost depths vary from one measurement to four. The two frost depths reported in this 

table are the largest (representing the coldest year) and smallest (representing the warmest year) 

annual maximum frost depth over the monitoring period, which was—at most—4 years. One of 

the challenges when dealing with annual frost depths is that the actual frost depth can vary 

markedly from year to year. Simply stated, the frost depth is as changeable as the weather, and 

the severity of the winter weather can change drastically from one year to the next. As can be 

seen in table 6, frost depths vary between 25 percent and 50 percent from year to year. In the 

event that only one measurement was taken, the same value was reported in the table for both the 

maximum and minimum year. Where the minimum measured value is shown as zero, then two 

or more measurements were taken with 1 year receiving no frost penetration. The most 

noticeable variation in frost measurements is section 364018, which indicates 2.09 m (6.86 ft) of 

frost 1 year and no frost another year. 

In actuality, using a limited number of frost measurements is not a good design practice because 

of its variability. The maximum frost depth measured during one of the more severe winters is 

often used for design values. For example, the Washington State Department of Transportation 

(WSDOT) has used frost measurements taken in 1950, which was a very severe winter, as its 

basis for the part of the pavement design that accounts for frost.(4)
14 

Table 6. SMP sites with measured frost depths. 

State 

Code 

SHRP 

ID 

Pavement 

Type 

Average FI 

DegreeCelsius days 

Average 

FTC 

Maximum 

Frost 

Depth 

m (ft) 

Minimum

Frost 

Depth 

m (ft) 

42 1606 JRCP 353.8 91.3 0.415 (1.4) 0 (0) 

31 0114 AC 409.8 105.1 0.865 (2.8) 0.665 (2.2) 

36 0801 AC 436.9 86.6 0.835 (2.7) 0 (0) 

25 1002 AC 437.8 117.9 0.76 (2.5) 0 (0) 

31 3018 JPCP 478.8 117.8 1.22 (4.0) 0 (0) 

56 1007 AC 545.8 140.4 0.945 (3.1) 0.515 (1.7) 

30 8129 AC 579.7 151.8 1.025 (3.4) 0.82 (2.7) 

36 4018 JRCP 583.6 113.0 2.095 (6.9) 0 (0) 

16 1010 AC 665.2 135.9 0.76 (2.5) 0.61 (2.0) 

30 0114 AC 687.8 113.7 1.004 (3.3) 1.004 (3.3) 

46 9187 AC 757.1 117.0 1.465 (4.8) 1.115 (3.7) 

50 1002 AC 786.0 98.2 0.79 (2.6) 0.63 (2.1) 

23 1026 AC 827.6 115.1 1.77 (5.8) 1.00 (3.3) 

46 0804 AC 977.9 107.2 1.435 (4.7) 0.875 (2.9) 

87 1622 AC 1080.8 102.5 1.23 (4.0) 1.03 (3.4) 

27 1018 AC 1108.3 92.9 2.13 (7.0) 1.865 (6.1) 

89 3015 JPCP 1227.6 84.4 1.38 (4.5) 0.97 (3.2) 

27 4040 JRCP 1347.8 90.7 2.21 (7.3) 2.01 (6.6) 

27 1028 AC 1387.7 87.0 2.38 (7.8) 2.38 (7.8) 

27 6251 AC 1484.9 88.5 2.25 (7.4) 1.895 (6.2) 

83 1801 AC 1732.9 88.9 1.975 (6.5) 1.16 (3.8) 

83 3802 JPCP 1862.8 79.9 2.31 (7.6) 1.71 (5.6) 

90 6405 AC 1863.3 83.3 2.05 (6.7) 2 (6.3) 

There has been a considerable amount of work relating frost depth to FI. A detailed description 

in estimating frost depths based on soil properties and FI is covered in some detail in the WSDOT 

Pavement Guide for Design, Evaluation and Rehabilitation, Volume 2, Pavement Notes.

(4) In 

Minnesota, it was found that using a similar approach with modification in the standard nfactors, thermal conductivity, and mean annual soil temperature, the majority of the calculated 

depths fell within ±13.3 percent of the depths measured.(5)

There is clearly a relationship between FI and frost depth. As shown in figure 1, graphing the 

measured frost depth for the SMP sites and the FI for the same sites shows a reasonable trend 

between measured frost depth and the average FI from the virtual weather station for each site. 

The trend line developed by using a linear least squares regression approach indicates that the 

frost depth equals 0.0014 times the average FI. 
15 

y = 0.0014x

R2

 = 0.7374

0

1

2

3

0 500 1000 1500 2000

Freezing Index (degree-Celsius days)

Frost Depth (meters)

Figure 1. Graph. Plot of measured maximum frost depth to FI. 

The regression data indicated that this trend line fits the data with an R-squared (coefficient of 

determination) of 0.74, which is quite good using such widespread national data. In the context 

of this study, this correlation is acceptable because the FI would be used only to compare the 

frost conditions at each site. The approximate frost condition will provide enough information to 

account for frost exposure in the pavement performance models. 

Considering the variability in measured frost depths over only a few years, it could be argued 

that estimating the average frost depth based on almost 20 years of FI measurements would 

relate better to 15 to 25 years of pavement performance than four or fewer isolated frost 

measurements. 

For these reasons, the analysis team proposed to expand the study to include non-SMP, GPS, and 

SPS sites using the FI as an indicator of actual frost depth. This approach was included in the 

work plan for phase 2 of the study, which was approved by the PFS Technical Advisory 

Committee and the FHWA LTPP Data Analysis contracting officer’s technical representative 

(COTR). Therefore, the analysis team expanded the dataset to include LTPP experiments listed 

in table 7. Complete details on each experiment can be found in the Long Term Pavement 

Performance Information Management System Pavement Performance Database User Guide.

(3)
16 

Table 7. LTPP experiments included in the analysis dataset. 

Pavement 

Type 

LTPP 

Experiment Description 

Flexible GPS-1 Asphalt concrete pavement (ACP) on granular base 

Flexible GPS-2 ACP pavement on bound base 

Flexible GPS-6 ACP overlay of existing ACP 

Flexible SPS-1 Strategic study of structural factors for ACP 

Flexible SPS-8 Study of environmental effects in the absence of heavy loads 

Rigid GPS-3 Jointed plain concrete pavement (JPCP) 

Rigid SPS-2 Strategic study of structural factors for rigid pavements 

Rigid SPS-8 Study of environmental effects in the absence of heavy loads 

DATABASE STRUCTURE AND CONTENT 

All databases developed for use in this study were created in Microsoft Access 2000© format to 

be consistent with the format of the LTPP Information Management System (IMS) Data Release 

17.0 (version 2004.01). Some of the necessary tables were imported from the data release 

without requiring modification; other tables were created or imported from the data release and 

modified to aid in the data extraction process. Queries were then developed to acquire the desired 

data in the proper format as well as to perform calculations. 

The task order proposal request for this project outlined the following factors to be considered in 

the analysis: 

• Pavement types (rigid, flexible). 

• Climatic data (rainfall, FI, and thawing index from temperature data). 

• Frost depth (temperature sensors and resistivity data). 

• Deflection data (stresses and strains calculated from layer material properties). 

• Performance data (distress and permanent deformation). 

• Soils and material properties. 

• Traffic data. 

To evaluate the effect of frost penetration and FTCs on pavement performance, other 

contributing factors, listed above, had to be taken into consideration. There are two methods that 

can be used to address this. The first is to separate the dataset into groups of similar test sections 

(i.e., structural properties) under similar conditions in terms of climate, materials, and traffic. In 

turn, regression analysis would be performed on each of the groupings. Considering the number 

of variables that need to be accounted for, the data would be separated into a large number of 

groups to explain all of the combinations of interest. The primary disadvantage to this method is 

that the results can be confounded by the grouping method used. In addition, each regression is 

based on a smaller subset of the entire dataset making the models less robust. 
17 

The other alternative is to perform regression analysis on the entire dataset and include all of the 

factors as explanatory variables within the model. This allows the contribution of each factor to 

be explained independently in the model. Comparisons of predicted performance can be made by 

keeping all inputs constant except for the factors of interest for the study (i.e., climatic variables). 

This approach was adopted for modeling performance in this study; therefore, each of the factors 

listed previously was incorporated into the datasets and used as explanatory variables in the 

models. The following paragraphs contain descriptions of each explanatory variable as well as 

the performance measures selected for evaluation. 

Pavement Types 

Separate datasets were developed for each pavement type. To account for the performance of 

flexible pavements, test sections classified as SPS-1, SPS-8, GPS-1, GPS-2, and GPS-6 

experiments were included in the study. Only test sections constructed with jointed plain 

concrete pavement were considered for use in the rigid pavement dataset (SPS-2, SPS-8, and 

GPS-3 experiments). These experiments provide test sections that have various structures and are 

exposed to a wide array of climatic and traffic conditions. 

It should be noted than some test sections received rehabilitation during LTPP monitoring. This 

resulted in test sections changing experiment designation; therefore, one test section may be 

included in two experiments. Data collected both before and after the rehabilitation were 

included in the dataset (with different experiment designations). 

The experiment classification was included as an explanatory variable (labeled as EXP) in the 

models to account for possible differences in performance between the experiment types. For 

example, GPS-6 test sections have been overlaid while the other flexible experiments have not. 

Climatic Data and Frost Depth 

Precipitation, CI, FTCs, longitude, latitude, and elevation were included in the datasets to 

account for climatic effects. As discussed in previous sections, the FI was used to provide a 

relative comparison of frost depth conditions at each location. 

Annual FI and annual number of FTCs were obtained directly from the virtual weather station 

table (CLM_VWS_TEMP_ANNUAL). Operating weather stations are not available at every test 

section; therefore, virtual weather stations were created by extrapolating climatic data from up to 

five nearby weather stations. This gives an estimation of the actual weather conditions at the 

location of the test section. To obtain values that are representative over the performance life of 

the pavement section, annual averages were computed for all years available, which is at least 20 

years of data. 

Annual cooling index (CI) was not directly available from the LTPP data release, and it was 

computed for each year with temperature values in the CLM_VWS_TEMP_ANNUAL table. 

Like FI and FTC, the values used in the analysis dataset were annual averages from at least 

20 years of data. The equation to calculate CI is shown in equation 1. 
18 

Cooling Index = ∑ (T −18.33) if T >18.33 (1) 

 Cooling Index cooling index ( C- days)

mean daily temperature ( C) from virtual weather stations

where

= °

T = °

Annual precipitation data (PRECIP) also was obtained from virtual weather station data in the 

CLM_VWS_PRECIP_ANNUAL table. The data used in the analysis dataset were the annual 

average of values in the table for each section, which consist of least 20 years of data. 

Collectively, these four factors describe the relevant climatic conditions for this study. FI has 

been closely correlated to depth of frost penetration. Because actual frost depth measurements 

were not taken at the test sections, FI was used as a surrogate to frost depth. FI and FTC are the 

primary factors investigated in the analysis, and they describe the cold portion of the climatic 

spectrum. On the other hand, CI offers information regarding the warmer portion of the climate. 

Two sites with approximately the same winter weather conditions (similar FI values) could have 

drastically different summer conditions, which are indicated by CI. For example, test sections 

361001 (located in New York) and 851801 (located in Newfoundland) both exhibit similar 

winter conditions. The FI for both sections is 505 degree-Celsius days, and the FTC is 90 and 

100 for test sections 361001 and 851801, respectively. However, there is a large difference in CI. 

Newfoundland experiences a much milder summer with a CI of 25 degree-Celsius days, while 

the summer in New York is considerably warmer, exhibiting a CI of approximately 300 degreeCelsius days. This difference in summer conditions could contribute to variant performance and 

must be considered when comparing performance trends. 

Geographic parameters of longitude, latitude, and elevation were incorporated in the dataset, but 

their contribution to pavement performance was found to be insignificant. These data were 

queried from the INV_ID or SPS_ID tables, depending on the experiment. 

Performance Data 

In addition to strain values discussed previously, accumulated surface distress, change in 

roughness with age, absolute roughness, rut depth (flexible pavements), and faulting (rigid 

pavements) were all used as performance measures. 

Pavement performance is a function of pavement age. As such, pavement age was calculated and 

coupled with the performance measures in the dataset. Depending on the experiment type, either 

the date of original construction or date of rehabilitation was extracted from the LTPP database. 

Details on the extracted data can be found in table 8. Data collection dates were subtracted from 

the established construction/rehabilitation dates to establish pavement age. A new 

construction/rehabilitation date was generated for each new rehabilitation activity. Subsequent 

data collection dates were subtracted from the new reference age. Performance could then be 

predicted as a function of pavement age. 
19 

Table 8. Sources of construction and rehabilitation dates. 

LTPP Experiment LTPP Database Table 

GPS-1, -2, and -3 INV_ID 

SPS-1, -2, and -8 SPS_ID 

GPS-6 INV_MAJOR_IMP or RHB_IMP 

Only distress data collected using manual techniques were used in this study. Data collected 

using photographic/automated techniques were not included. Research that has been conducted 

by Rada et al.(6) evaluating the variability of LTPP distress data collection found that the overall 

variability of distress collected manually is lower than that collected by photographic methods. 

In addition, the apparent bias of data collected by photographic techniques was much higher than 

that collected manually. The study found a reasonable correlation between the two methods; 

however, manual data collection tended to yield higher amounts of distress than the photographic 

techniques. Considering these findings, only manual data collection was included in the study to 

eliminate variability introduced when data from multiple techniques are used. The study also 

concluded that total distress quantities exhibit less bias and variability than each severity level 

independently. The recorded amounts of distress at each severity level were summed. Different 

methods were used to combine severity levels for each dataset, each of which is explained 

subsequently. 

The LTPP pavement distress identification procedures characterize the various distress 

categories in terms of the type of the distress, the severity of the distress (i.e., low, moderate, and 

high severity), and the amount of the distress (area, length, number of occurrences). 

There are two primary alternatives available to represent measured distress accumulation with 

pavement age for performance modeling. The first option would be to represent the accumulation 

of distress over time for the three levels of severity separately. This would add a very high level 

of complexity to the process and segment an already limited amount of distress measurements. 

Studies have shown that there is greater variability in classification of severity level for a distress 

type than that associated with distress type identification.(6) Modeling each severity level 

independently would compound this variability. To counter this, the quantities of all three 

severity levels could be summed (without weighting factors) for each distress type, which would 

reduce the variation apparent between severity levels. For example, a survey consisting of 3 m2

of low severity, 5 m2

 of moderate severity, and 2 m2 of high severity fatigue would be treated as 

10 m2

 of fatigue for regression development. The disadvantage of this approach is that it 

eliminates the resolution gained by collecting pavement distress measurements in terms of 

severity levels. 

Pavement distress measurements have historically been collected in terms of low, moderate, and 

high severity levels,(7) which where developed for implementation into pavement management 

systems. The pavement distress measurements were initially compiled as a composite index 

where the measurements of several distress types and severity levels were combined into a single 

index(8) such as the Pavement Condition Index (PCI) used in the Micro PAVERTM PMS system. 

More recently, the distress measurements have been compiled as a unique pavement distress 
20 

index where the severity levels of a single distress type are combined to produce a specific 

distress index.(9) The distress index is usually expressed as 100 minus the sum of the deduct 

points, where separate deduct points are computed for the amount of distress in each severity 

level for a specific distress type. 

It is important to that that most SHAs use different pavement distress indices based to some 

extent on the specific pavement management system they have adopted, and a wide range of 

systems are in use throughout the United States. One of the predominant pavement condition 

deduct systems for unique pavement distress indices was used in this study to represent the 

change in flexible pavement distress with time.(10)

For the surface distress values of flexible pavements, all three severity levels for each distress 

type were combined through the use of deduct curves developed for the South Dakota 

Department of Transportation(10) to obtain a deduct value for each distress. The equations for 

these curves can be found in equations 2 through 4. Figure 2 provides a graphical representation 

of the low, moderate, and high severity deduct equations. The deduct values for the three severity 

levels were summed for each distress type and used in the regression analysis 

(i.e., equation 5). 

P recorded percentage of high severity distress

P recorded percentage of moderate severity distress

P recorded percentage of low severity distress

D total deduct value

D high severity deduct value

D moderate severity deduct value

D low severity deduct value

where :

5.2064*

4.4575*

3.4082*

H

M

L

T

H

M

L

0.6956

0.6107

0.514

=

=

=

=

=

=

=

= + +

=

=

=

T L M H

H H

M M

L L

D D D D

D P

D P

D P (2) 

(3) 

(4) 

(5) 
21 

1

10

100

0.1 1 10 100

Percent Occurence

Deduct Value

Low Severity

Moderate Severity

High Severity

Figure 2. Graph. Individual distress deduct curves. 

Fatigue cracking (FC), block cracking (BC), longitudinal wheelpath cracking (LWP), and 

transverse cracking (TC) were considered in the study. All distress data for the flexible dataset 

were queried from the MON_DIS_AC_REV table. Because LWP often progresses to FC, the 

two distress types were combined (FWPC). LWP was converted from a linear unit to a unit of 

area to be consistent with FC. This was done by applying a standard width of 0.3 m (1 ft) to the 

recorded length of LWP. All severities of LWP were considered as low severity to compute 

deduct values that would be combined with the FC. 

The recorded amounts of distress were converted into percentages (based on the total length or 

area of the test section). This was done to account for test sections with different lengths in the 

dataset. Percentages for FC and FWPC were determined based on the area of one wheelpath in 

the test section. The width of the wheelpath was assumed to be 1 m (3 ft). For BC, percentages 

by total area were used in the deduct equations, while the percentage of TC was based on the 

total length of the section. 

It has been observed by various researchers, as well as the regional support contractors, that there 

is considerable variability in the rating of longitudinal wheelpath cracking and low severity 

fatigue cracking. A study by Rada, et al.(6) observed that compensatory differences exist among 

fatigue and longitudinal wheelpath cracking. That is, a reduction in one of these distress types 
22 

generally coincides with an increase in the other type. For example, fatigue and longitudinal 

cracking values from section 080501 can be found in table 9. There is a reduction in fatigue 

cracking between 1996 and 1998 that is accompanied by an increase in longitudinal wheelpath 

cracking. The opposite is true between 1998 and 1999. 

Table 9. Fatigue and longitudinal wheelpath cracking 

for LTPP section 080501. 

Date 

Total Fatigue 

Cracking 

(m2

 (ft2)) 

Total Longitudinal 

Wheelpath Cracking 

(m (ft)) 

26-Oct-94 0 (0) 3.3 (10.8) 

12-Apr-96 114.7 (1234.6) 0 (0) 

31-Jul-98 19.9 (214.2) 180.3 (591.5) 

01-Sep-99 128.4 (1382.1) 2.9 (9.5) 

In addition, in the beginning stages fatigue cracking generally appears as longitudinal cracking in 

the wheelpath (and is rated as such by surveyors). Over time, with repeated traffic loading, this 

cracking develops patterns indicative of fatigue; therefore, longitudinal wheelpath cracking 

values diminish as fatigue values increase. An example of this can be found in table 10. The 

longitudinal wheelpath cracking reduces with age as the fatigue cracking increases at section 

068153 because the longitudinal cracking is progressing to fatigue cracking. Modeling these two 

distress types independently would result in significant variation within the regression. 

Table 10. Fatigue and longitudinal wheelpath cracking 

for LTPP section 068153. 

Date 

Total Fatigue 

Cracking 

(m2

 (ft2)) 

Total Longitudinal 

Wheelpath Cracking 

(m (ft)) 

25-Nov-91 3.7 (39.8) 86.6 (932.1) 

27-Jul-94 35.9 (386.4) 53.3 (573.7) 

27-Jul-95 247 (2658.7) 0 (0) 

For these reasons, fatigue and longitudinal cracking were combined. The combination provides a 

better indication of the damage in the wheelpaths because it is less variable and more consistent 

over time. This has improved the developed models. 

The format of distress data collected on rigid pavements does not match the required format used 

in the established deduct curves;(8) therefore, the severity levels were summed for each distress 

type. This total distress was then normalized based on the size of the test section in the same 

manner as the flexible sections. For example, the sum of all three severities of longitudinal 

cracking were summed and divided by the total area of the section. Distress data were extracted 
23 

from the MON_DIS_JPCC_REV table. Rigid distress types evaluated were longitudinal cracking 

(LC), transverse cracking (TC), corner breaking (CB), and pumping/water bleeding (PUMP). 

Pavement roughness was computed by averaging international roughness index (IRI) values 

from all runs available for each survey date and test section in the MON_PROFILE_MASTER 

table. Two forms of IRI were incorporated in the dataset for model development: change in IRI 

(DIRI) and absolute IRI (AIRI). 

Many factors, such as construction techniques, existing geometrics, and material quality 

contribute to the roughness of newly constructed or rehabilitated pavements. In turn, initial 

pavement roughness significantly affects the accumulation of roughness as the pavement ages. 

To negate this variability from the models used to make performance comparisons in different 

climatic regions, DIRI values were computed based on initial roughness values. Roughness data 

at the completion of construction were not available for the test sections; therefore, the first 

recorded roughness value was used as the initial value. Subsequent measurements were 

subtracted from this value. The initial roughness value was also included in the dataset for 

reference. Using test section 481094 as an example, the first roughness measurement—average 

IRI of 0.875 meters per kilometer (m/km) (55.5 inches per mile (inches/mi))—was performed in 

March 1990, equating to a pavement age of approximately 14 years. Successive testing 

performed in September 1997 resulted in an average IRI of 0.963 m/km (61.1 inches/mi). The 

DIRI value reported in the dataset was 0.088 m/km (5.6 inches/mi) at age 21. 

Alternatively, recorded IRI values in absolute terms (AIRI) were established as performance 

measures. However, to account for postconstruction variability, the MIRI and the age at which 

they were recorded (MIRI_AGE) were incorporated as explanatory variables in the regression 

analysis. 

The air temperature recorded during profile data collection was also included as an explanatory 

variable for the roughness models in the rigid dataset. Temperature variations throughout the day 

cause warping and curling that can contribute to variations in roughness values. 

Data from MON_T_PROF_INDEX_SECTION were queried to obtain rut depth information for 

the flexible dataset. This table contains computed rutting index parameters calculated from 

transverse profile information. The lane-width wire line rut index, which calculates the 

maximum rut depth (in each wheelpath) based on a wire line reference, was selected for use in 

this study. The reference is defined as a straight line between peak elevation points for each half 

of the lane.(3) Each time rutting was measured, rut depth values for both wheelpaths were 

averaged and incorporated into the analysis dataset (RUT). 

Faulting values were extracted from MON_DIS_JPCC_FAULT_SECT for the rigid dataset. The 

values used for the study were average values of all faulting recorded at the transverse joints. 

The faulting measurements were taken in the outside wheelpath. 
24 

Soils and Material Properties 

Multiple explanatory variables were included to account for the contribution of soils and material 

properties on pavement performance, and they consisted of: base material type (BASE); 

subgrade material type (SG); structural number (SN) for flexible pavements; asphalt layer 

thickness (ACTHICK) for flexible pavements; and slab thickness (D) for rigid pavements. 

Materials classifications for base layers were grouped into six categories. Information from the 

TST_LO5B table was used to obtain material codes for base layers (i.e., layer description=5). 

Data in the TST_LO5B table are the most representative information in the LTPP IMS Pavement 

Performance Database regarding pavement layering because it is derived primarily from 

laboratory test results. Table 11 lists each BASE category as well as the material codes used to 

define the category. Complete details on material code classifications can be found in 

Operational Guide No. SHRP-LTPP-OG-004.(11) Each test section was assigned a base type 

category as an explanatory variable. Each of the BASE categories established have significantly 

different characteristics (i.e., drainage, strength/in-situ moisture relationships), which directly 

effect the rate at which pavements deteriorate. In some cases multiple base layers existed in the 

pavement structure. Guidelines were established and they are provided in table 12 to assign 

BASE categories for these situations. 

Similarly, material code classifications of subgrade layers (layer description=7) were extracted 

from TST_LO5B and grouped into three categories. Test sections with material codes between 

100 and 178 were assigned a FINE subgrade type. All of these material codes correspond to 

material that has at least 50 percent passing the 0.075 millimeters (mm) (No. 200) sieve. 

Subgrade layers consisting of materials codes 200 to 267 were categorized as a COARSE 

subgrade. These material codes represent soils with less than 50 percent passing the 0.075 mm 

(No. 200) sieve. The third subgrade type established for this study was labeled ROCK/STONE 

and includes “naturally formed solid mineral matter occurring in large masses, and naturally or 

crushed angular particles of rock.”(11)

The type of subgrade material can make a large contribution to the performance of pavement 

structures. Fine-grained materials become very weak with excess moisture, and are susceptible to 

frost heave. 

For flexible pavements, structural capacity is most commonly defined in terms of SN. Methods 

detailed in SHRP-LTPP Technical Memorandum AU-167(12) were used to calculate SN values. 

The memo defines structural coefficients for each combination of material classification code 

and layer description. This coefficient was used in conjunction with layer thickness to obtain 

structural number. The method does not incorporate drainage coefficients to adjust structural 

capacity. Both material classification codes and layer thicknesses were extracted from the 

TST_LO5B table. If either the material classification code or the layer thickness was unavailable 

or unreasonable, data from agency project records, which are located in rehabilitation 

(RHB_LAYER) and inventory (INV_LAYER) tables, were used. 
25 

For rigid pavements, SN is not a valid parameter, and is replaced by slab thickness in design 

procedures.(13) As such, D was included as an explanatory variable in the dataset. Thickness 

values were extracted from the TST_LO5B table for surface layers (layer description=3). 

Table 11. Material code classifications for each BASE type category. 

BASE 

Category 

BASE 

Description 

Material 

Code Material Code Description 

302 Gravel (uncrushed) 

303 Crushed stone 

304 Crushed gravel 

305 Crushed slag 

306 Sand 

307 Soil-aggregate mixture (predominantly finegrained) 

308 Soil-aggregate mixture (predominantly coarsegrained) 

DGAB Unbound Base 

309 Fine-grained soils 

319 Hot mix asphalt concrete (HMAC) 

320 Sand asphalt 

321 Asphalt treated mixture 

322 Dense graded, hot laid, central plant mix 

323 Dense graded, cold laid, central plant mix 

324 Dense graded, hot laid, mixed in-place 

328 Recycled asphalt concrete, plant mix, hot laid 

329 Recycled asphalt concrete, plant mix, cold laid 

ATB Asphalt 

Treated Base 

330 Recycled asphalt concrete, mixed in-place 

325 Open graded, hot laid, central plant mix 

326 Open graded, cold laid, central plant mix 

PATB Permeable 

Asphalt 

Treated Base 327 Open graded, cold laid, mixed in-place 

331 Cement aggregate mixture 

332 Econocrete 

333 Cement-treated soil 

NONBIT Nonbituminous 

Treated Base 

335-360 Includes treated soils (e.g., lime, calcium chloride) 

LCB Lean Concrete 

Base 

334 Lean concrete 

NONE No Base N/A No layer description=5-inch layer structure 
26 

Table 12. BASE types assigned to structures with multiple base layers. 

Existing Base Layer 

Combinations BASE Category Assigned 

ATB/DGAB ATB 

PATB/ATB PATB 

PATB/DGAB PATB 

NONBIT/DGAB NONBIT 

LCB/NONBIT LCB 

NONBIT/ATB Sections removed from dataset (if present) 

NONBIT/PATB Sections removed from dataset (if present) 

LCB/ATB Sections removed from dataset (if present) 

LCB/PATB Sections removed from dataset (if present) 

In addition, the total thickness of the AC binder course layers (layer descriptions 1, 3, and 4) was 

also calculated and included as an explanatory variable in the dataset. Again, data in the 

TST_LO5B table were used to determine these values. 

To summarize all of the soil and material property variables discussed previously, TST_LO5B 

information from test section 481094 is provided as an example in table 13 along with structural 

coefficients from SHRP-LTPP Technical Memorandum AU-167.(12) 

The material classification code for the base layer (layer description 5) is 303; therefore, the 

BASE category dense graded aggregate base (DGAB) was assigned to test section 481094. The 

subgrade explanatory variable for this section was designated as COARSE because of the 

material code classification of 214 for the subgrade layer. The SN calculation provided in 

table 12 resulted in an SN of approximately 2.01. 

Last, two AC binder course layers are present in the pavement structure with thicknesses of 1.2 

and 0.7 (layers 3 and 4, respectively); therefore, the ACTHICK value was computed as 1.9 for 

this section. 

Table 13. TST_LO5B data for test section 481094. 

Layer 

Layer 

Description 

from 

TST_LO5B 

Material 

Code from 

TST_LO5B 

Structural 

Coefficient 

from AU-167 

(a) 

Layer Thickness 

from 

TST_LO5B (D) 

(mm (inch)) 

Structural 

Contribution 

(a*D) 

1 7 214 0 NA 0 

2 5 303 0.14 213 (8.4) 1.176 

3 4 1 0.44 31 (1.2) 0.528 

4 3 1 0.44 18 (0.7) 0.308 

Total 2.012 

Note: a= structural coefficient, D=layer thickness, a*D=structural coefficient multiplied by layer thickness 
27 

Traffic Data 

Traffic data are one of the most significant factors affecting pavement performance. Equivalent 

single axle load (ESAL) values provide a representative value of the traffic loading experienced 

by a pavement structure. Equivalent axle load factor (EALF) values were computed using 

equations set forth in the American Association of State Highway and Transportation Officials 

(AASHTO) Guide for Design of Pavement Structures.

(13) Values of SN or D, discussed 

previously, were inputs for the calculation of EALF. In turn, the EALF values were used to 

convert weight bin data from the TRF_MONITOR_AXLE_DISTRIB table to ESALs. Values 

were averaged over all available years in the dataset. If monitored traffic data were unavailable, 

data from the estimated traffic (TRF_MON_EST_ESAL) table were used. These estimations are 

provided by the SHA. 

It is recognized by the analysis team that traffic load spectra has been introduced to represent 

loading conditions for design purposes; however, the tools to apply load spectra from LTPP data 

were not readily available at the onset of the project. 

To provide an indication of the structural capacity of the pavement relative to the amount of 

loading experienced, a ratio of SN and logarithm of ESALs was computed. Pavement 

performance is highly dependent on the relationship between these two. For example, the amount 

of alligator cracking is directly related to both the magnitude of loading and the overall strength 

of the pavement structure. Therefore, the ratio could be used to determine if differences in 

performance are related to frost conditions or are a consequence of the improper pairing of 

pavement strength and loading. A similar ratio was computed for rigid pavements with the 

exception that SN was replaced with slab thickness. The logarithmic relationship of ESALs was 

applied in both ratios to reflect the processes outlined in the 1993 AASHTO Guide for Design of 

Pavement Structures.

(1) The design charts in the design guide correlate accumulated damage to 

ESAL values using a logarithmic relationship. The SN or D values are integrated into the design 

equations using a linear relationship. 

In addition, the functional classification for each test section was included in the roughness 

datasets (both absolute and change in roughness). This factor was included to account for 

differences in the initial smoothness of the sections due to a range of construction smoothness 

specifications. 

TEST SECTION SELECTION 

Criteria were established to select test sections to be used in the development of regression 

models. This was to reduce variability within the dataset and improve the resultant models. 

The first condition was set forth in the statement of work for the project, which stated that 

performance comparisons would be made with sites that are located in: 
28 

...the southern reaches of the wet-freeze zone or the northern reaches of the wet no-freeze 

zone versus those sites that are farther north in the wet freeze zone. Contrast these 

findings with pavement performance in the dry freeze and wet no-freeze climatic regions. 

Therefore, only sections located in the wet freeze, wet no-freeze, and dry freeze regions were 

included in the analysis. While climatic differences were accounted for as explanatory variables, 

only data from the regions of interest for this study were included. 

In addition, to limit the variability of the performance data within the dataset, the TST_LO5B 

table was used to identify and remove test sections with surface treatments such as slurry and 

chip seals. Routine applications of surface treatments significantly reduce the progression of 

pavement deterioration. These treatments mask the surface of the pavement, thus improving 

performance measures such as pavement roughness and surface distress. If these test sections 

would have been included in the study, additional explanatory variables would have been 

required to properly account for their effect. 

Test sections with unavailable or questionable explanatory data were also removed from the 

analysis dataset. For example, the computed 1994 annual ESAL value for test section 124109 

was approximately 47.8 million. This value is very unreasonable, and the average ESAL value 

used in the dataset was computed using all other available years except 1994. 

The resultant datasets consisted of more than 520 test sections for flexible pavements and over 

270 test sections for rigid pavements. Between 2,500 and 4,500 observations (depending on the 

performance measure) were included to develop the flexible models. The number of observations 

for the rigid dataset ranged from 1,400 to 2,700. The size of the dataset is adequate to properly 

incorporate the relatively large number of variables required in the study. In addition, the number 

of test sections ensures pavements exposed to a wide range of conditions are represented in the 

models. 
29 

4. MODEL FITTING STATISTICAL APPROACH 

This section describes the overall procedure for developing regression models for each of the 

performance measures considered in the study. The intent of the process was to generate a model 

with the best prediction capability while ensuring assumptions inherent in the process were not 

violated. Statistical analysis was performed using SAS® software, version 9.1.3.(14)

All explanatory variables discussed in previous sections of this report were included in the initial 

regression analysis. These variables were both continuous (i.e., FI) and categorical (i.e., BASE) 

factors. Table 14 provides a summary of all variables considered in the study as well as details 

on the format of each parameter. 

Table 14. Summary of explanatory variables. 

Explanatory Variable Parameter 

Type 

Pavement Structure Categorical 

Freezing Index (FI) Continuous 

Freeze-Thaw Cycles (FTC) Continuous 

Cooling Index (CI) Continuous 

Annual Precipitation (PRECIP) Continuous 

Pavement Age (AGE) Continuous 

Subgrade Type (SG) Categorical 

Base Type (BASE) Categorical 

Asphalt Cement Concrete Thickness (ACTHICK) Continuous 

Slab Thickness (D) Continuous 

Traffic Loading/Structural Capacity Ration (LESN or LEDT) Continuous 

An initial investigation was performed on each of the predictor variables to gain an understanding of the range present in the dataset and the nature of the parameters to be used in the 

regression modeling. Graphical techniques and descriptive statistical measures were used for this 

evaluation. These visual techniques allowed for problems with calculations in the dataset or 

possible outliers to be identified. As an example, a box plot diagram is provided in figure 3, and 

table 15 presents a sample set of statistical parameters evaluated. 

Box plots provide an excellent visual summary of many important aspects of a distribution.(15)

The box plot is based on a 5-number summary that includes the median, quartiles, and extreme 

values. The box stretches from the lower hinge (Q1: 1st quartile) to the upper hinge (Q3: 3rd 

quartile) and therefore contains the middle half of the scores in the distribution. The median is 

shown as a line across the box. A quarter of the distribution is between this line and the top of 

the box and one quarter of the distribution is between this line and the bottom of the box. The 

plus (+) symbol in box plot represents the mean of the response within that group. The distance 

between Q3-Q1 is known as interquartile range (IQR). This measure is very useful in detecting 
30 

outliers in the data. Any observation falling outside Q3+1.5IQR or Q1−1.5IQR could be flagged 

as potential outlier. Box plots can be useful in detecting right and left skewness as well. 

Figure 3. Graph. Sample box plot. 
31

Table 15. Sample of statistical parameters. 

Variable N Mean Std Dev Sum Minimum Maximum 

ESAL 1991 209757.0 200366.0 417627102 1.10000 1484889 SN 1991 5.39970.0 1.92871 10751 0.60000 12.20000 ACTHICK 1991 6.33305.0 2.83518 12609 1.00000 22.80000 ELEV 1991 1384.0 1569.0 2755713 8.00000 7400 LAT 1991 39.50387 6.86929 78652 18.44200 64.94800 LONG 1991 93.88745 18.13777 186930 52.86900 156.67000 FTC 1991 85.63034 40.13912 170490 0 192.00000 FI 1991 360.47850 408.44595 717713 0 2584 CI 1991 644.74681 523.55940 1283691 0.10000 2506 PRECIP 1991 909.58970 388.44307 1810993 187.30000 2020 RUT_AGE 1991 7.86801 6.87675 15665 0 31.80000 RUT 1991 5.17353 4.13532 10301 0.50000 55.00000 
32 

Partial regression effects between the response and continuous predictor variables were 

evaluated, which provided information regarding the independent contribution of each 

parameter. Figure 4 shows an example of an augmented partial residual plot. 

Figure 4. Scatter Plot. Sample augmented partial residual plot. 

In augmented partial residual plots, both partial linear and quadratic effects of a continuous 

explanatory variable (equation 6) are plotted against one of the explanatory variables using 

symbol “R”. The simple regression line (symbol “O”) between the explanatory variable and the 

response variable is also overlaid in the same plot to show the differences between the simple 

and the partial effects. This augmented partial residual plot is considered very effective in 

detecting outliers, nonlinearity, and heteroscedasticity.(15)

2

i β1X1 β 3X1 ε = + (6) 

Where: 

explanatory variable

, coefficients

residual

1

1 3

=

=

=

X

i

β β

ε
33 

While partial regression coefficients present information on the contribution of each predictor 

variable after controlling for other effects in the model, correlation between variables (i.e., 

multicollinearity) as well as interacting effects of multiple predictor variables on the performance 

measure do exist and must be checked. A preliminary analysis of multicollinearity was 

conducted using an explanatory variable correlation matrix (table 16). In the presence of 

multicollinearity, the regression parameter estimates become unstable due to a large inflation of 

the parameter variance. Any two explanatory variables having a significantly larger correlation 

(>0.9) could be involved in multicollinearity and should be examined by the variance inflation 

factor (VIF > 10) estimate for each explanatory variable.(15) Significant interaction between any 

two continuous predictors or between a continuous and categorical predictor variables indicate 

that the performance measure is influenced by the interacting variables multiplicatively. 

Omitting significant interaction terms could under- or overestimate the model prediction 

significantly. Graphical methods were used to examine interaction between continuous and 

categorical parameters. Interaction plots and the P-values for the interaction terms from the full 

model were used to check for interaction between two continuous variables and between a 

continuous and a categorical variable. 

Using the knowledge gained through the preliminary review, regression models were developed 

with all of the explanatory variables and potential interaction terms (identified in the initial 

review). Resulting P-values were used to determine which variables contributed significantly to 

the regression model. Generally, parameters with a P-value greater than 0.15 were considered 

insignificant because there could be more than a 15 percent chance that the regression parameter 

estimates could be equal to zero, and therefore, should be removed from subsequent regression 

iterations. In some cases, the independent contribution of an explanatory variable was 

insignificant, but its interaction effect with other parameters was significant. Both the 

independent and interacting terms were included in subsequent models when this occurred. 

Terms that were marginally significant were incorporated in the model only if their contribution 

improved the prediction capability of the model, which was achieved by iteratively developing 

models and evaluating adjusted R-squared, root mean squared error and AIC statistics to select 

the model that best predicted the observed data. All parameters within a categorical variable 

were included if one of the parameters was found to be significant. For example, in table 17, all 

BASE types were included in the model because DGAB is significant. LCB was included even 

though its contribution was not significant. The entire category must be accounted for in the 

model if one parameter was found to be significant. 

As part of the model development activities, transformations were incorporated to reduce the 

violation of assumptions inherent in regression models. Figure 5 provides graphical results on the 

validity of assumptions for the AIRI model before transforming the data. As can be seen from 

the residual plot (upper right corner of figure 5), the shape of the plot indicates unequal error 

variance (signified by the diagonal orientation of the bottom boundary of data points). In 

addition, the normal probability plot (lower left figure) indicates non-normality in the dataset 

(residual points depart from the straight line). For these reasons, a natural logarithm 

transformation of the performance measure was performed. The results of the validity check after 

the transformation can be found in figure 6. As the figure indicates, both the unequal error 
34 

variance and non-normality have been reduced, thus improving the validity of assumptions in the 

model. 

The final regression models were used to predict mean performance values, and 95 percent 

confidence intervals were also computed and used in making performance comparisons between 

the regions. These predictions were made for climatic scenarios of interest in the study. 

Complete details on this process are discussed in the following section of this report 
35

Table 16. Sample of correlation matrix. 

ESAL SN ACTHICK ELEV LAT LONG FTC FI CI PRECIP

ESAL ESAL 

1.00000 0.25535 

<.0001 

0.15887

<.0001

-0.09449

<.0001

-0.25639

<.0001

-0.03808

0.0894

-0.16282 

<.0001 

-0.24556

<.0001

0.22145

<.0001

0.13579

<.0001

SN SN 

0.25535 

<.0001 

1.00000 0.43217

<.0001

0.05645

0.0118

0.06731

0.0027

-0.12238

<.0001

0.22680 

<.0001 

-0.05253

0.0191

-0.18038

<.0001

-0.10588

<.0001

ACTHICK ACTHICK 

0.15887 

<.0001 

0.43217 

<.0001 

1.00000 0.01245

0.5786

0.06886

0.0021

-0.09872

<.0001

0.11020 

<.0001 

0.03918

0.0805

-0.14686

<.0001

0.00599

0.7895

ELEV ELEV 

-0.09449 

<.0001 

0.05645 

0.0118 

0.01245

0.5786

1.00000 0.28208

<.0001

0.51202

<.0001

0.76208 

<.0001 

0.19521

<.0001

-0.43518

<.0001

-0.78481

<.0001

LAT LAT 

-0.25639 

<.0001 

0.06731 

0.0027 

0.06886

0.0021

0.28208

<.0001

1.00000 0.25897

<.0001

0.61287 

<.0001 

0.76147

<.0001

-0.89240

<.0001

-0.40206

<.0001

LONG LONG 

-0.03808 

0.0894 

-0.12238 

<.0001 

-0.09872

<.0001

0.51202

<.0001

0.25897

<.0001

1.00000 0.22746 

<.0001 

0.17877

<.0001

-0.13525

<.0001

-0.58561

<.0001

FTC FTC 

-0.16282 

<.0001 

0.22680 

<.0001 

0.11020

<.0001

0.76208

<.0001

0.61287

<.0001

0.22746

<.0001

1.00000 0.38152

<.0001

-0.78366

<.0001

-0.62650

<.0001

FI FI 

-0.24556 

<.0001 

-0.05253 

0.0191 

0.03918

0.0805

0.19521

<.0001

0.76147

<.0001

0.17877

<.0001

0.38152 

<.0001 

1.00000 -0.61977

<.0001

-0.40330

<.0001

CI CI 

0.22145 

<.0001 

-0.18038 

<.0001 

-0.14686

<.0001

-0.43518

<.0001

-0.89240

<.0001

-0.13525

<.0001

-0.78366 

<.0001 

-0.61977

<.0001

1.00000 0.43074

<.0001

PRECIP PRECIP 

0.13579 

<.0001 

-0.10588 

<.0001 

0.00599

0.7895

-0.78481

<.0001

-0.40206

<.0001

-0.58561

<.0001

-0.62650 

<.0001 

-0.40330

<.0001

0.43074

<.0001

1.00000 

RUT_AGE RUT_AGE 

0.03080 

0.1696 

-0.27243 

<.0001 

-0.04717

0.0353

-0.10769

<.0001

-0.08413

0.0002

-0.01362

0.5436

-0.18159 

<.0001 

0.05764

0.0101

0.10822

<.0001

0.13377

<.0001

RUT RUT 

0.00151 

0.9462 

-0.15508 

<.0001 

-0.02761

0.2181

-0.00238

0.9154

0.04866

0.0299

-0.02278

0.3097

0.02663 

0.2349 

0.03397

0.1297

-0.02763

0.2179

0.02610

0.2443

*The top number in each cell represents correlation; the bottom number denotes the P-value. 
36 

Table 17. Regression coefficients with P-value statistics. 

Regression Parameter Estimate Standard t Value Pr > |t|

Intercept −1.08 0.29 −3.79 0.0002 

BASE ATB 0.45 0.15 2.89 0.0040 

BASE DGAB 0.63 0.14 4.62 <.0001 

BASE LCB 0.93 0.92 1.02 0.3101 

BASE NONBIT 0.75 0.18 4.24 <.0001 

BASE NONE −0.28 0.35 −0.79 0.4284 

BASE PATB 0 . . . 

SG COARSE 0.12 0.20 0.58 0.5618 

SG FINE 0.16 0.20 0.78 0.4346 

SG 0 NA NA NA 

EXP G1 0.66 0.06 10.50 <.0001 

EXP G2 0.61 0.08 7.65 <.0001 

EXP G6 0.60 0.06 10.79 <.0001 

EXP S1 0.52 0.06 8.62 <.0001 

EXP S8 0 NA NA NA 

lesn 0.77 0.13 5.84 <.0001 

logrut age 0.50 0.04 14.10 <.0001 

CI 3.4 * 10-4 8.2 * 10-5 4.11 <.0001 

FI 1.5 * 10-4 1.7 * 10-4 0.91 0.3649 

PRECIP 1.2 * 10-5 6.4 * 10-5 0.19 0.8475 

FTC 3.5 * 10-3 6.9 * 10-4 5.02 <.0001 

FI*PRECIP 3.0 * 10-7 1.1 * 10-7 2.71 0.0068 

lesn*logrut age −8.4 * 10-2 2.6 * 10-2 −3.25 0.0012 

logrut age*CI −1.4 * 10-4 3.2 * 10-5 −4.44 <.0001 

logrut age*FI −1.4 * 10-5 3.7 * 10-5 −0.38 0.7063 

lesn*BASE ATB −0.44 0.16 −2.76 0.0059 

lesn*BASE DGAB −0.54 0.14 −4.00 <.0001 

lesn*BASE LCB −0.75 1.22 −0.61 0.5416 

lesn*BASE NONBIT −0.66 0.15 −4.28 <.0001 

lesn*BASE NONE 0.36 0.42 0.86 0.3901 

lesn*BASE PATB 0 NA NA NA 

FI*BASE ATB −3.8 * 10-4 1.5 * 10-4 −2.58 0.0099 

FI*BASE DGAB −4.2 * 10-4 1.3 * 10-4 −3.20 0.0014 

FI*BASE LCB −5.2 * 10-3 7.9 * 10-3 −0.66 0.5108 

FI*BASE NONBIT −1.3 * 10-3 3.1 * 10-4 −4.35 <.0001 

FI*BASE NONE −4.3 * 10-4 2.1 * 10-4 −2.07 0.0382 

FI*BASE PATB 0 NA NA NA 
37 

Figure 5. Graphs. Assumption validity check for absolute IRI model 

(before transformation). 
38 

Figure 6. Graphs. Assumption validity check for absolute IRI model 

(after natural logarithm transformation of the performance measure). 
39 

5. PERFORMANCE MODEL DEVELOPMENT AND SELECTION 

As described in the previous section, simple statistical tools such as maximum, minimum, 

average, and standard deviation were reviewed to identify data acquisition errors or 

problems with the calculations performed. Box plots, frequency graphs, and residual plots 

were also created to develop an understanding of the datasets and to study interaction, 

correlation, and the type of distribution present in the data. These tools were also used to 

verify whether the nature of the data violate any statistical assumptions made during the 

analysis. As part of this initial statistical review, the dataset was also inspected to identify 

and remove data collected after unrecorded pavement improvements were performed. 

Criteria were developed for each performance measure and applied to the dataset to flag 

instances of significant reductions in deterioration. Table 18 summarizes the checks used 

in this process. 

Table 18. Criteria to warrant additional investigation 

of unrecorded pavement improvements.

Pavement 

Type 

Performance 

Measure Reduction Criteria that Warrant Investigation 

AC IRI >0.4 m/km (25.4 inches/mi) reduction 

AC DISTRESS >30% reduction in sum of key distress typesa

AC RUTDEPTH >10 mm (0.4 inch) reduction 

PCC IRI >0.4 m/km (25.4 inches/mi) reduction 

PCC DISTRESS >30% reduction in sum of key distress typesb

PCC FAULTING >2 mm (0.08 inch) reduction 

a

Deduct values of fatigue cracking, block cracking, longitudinal wheelpath cracking, 

longitudinal nonwheelpath cracking, transverse cracking, and patching were summed for 

this evaluation. 

b

Normalized quantities of corner breaks, longitudinal cracking, transverse cracking, and 

patching were summed for this evaluation. 

Each test section flagged was thoroughly examined using all performance measures to 

determine if the reduction was most likely caused by an improvement to the pavement or 

if it could be attributed to data variability. In general, if the majority of the performance 

measures demonstrated a reduction in deterioration, it was concluded that an unreported 

pavement improvement was applied. Data collected after the improvement were removed 

from the dataset. 

Due to the subjective nature of distress data, additional quality reviews were performed 

on the data to remove records where a reduction was observed in one of the distress types 

as a result of rater variability. For example, distress data collected at test section 181037 

at pavement age equal to 10.2 years recorded a BC deduct value of 74.3 (and 0 values for 

TC and FWPC). The next distress survey (11.4 years) recorded a decreased BC value of 

33.2; however, the values of TC and FWPC increased significantly (values of 68 and 7.5, 

respectively). The first surveyor rated the distress as BC while the second rater opted to 

rate a series of longitudinal and transverse cracks. As such, the BC data recorded at 

11.4 years were removed from the dataset. 
40 

Test sections with known construction issues were also removed from the dataset. One 

such example is the Nevada SPS-2 project that experienced excessive cracking just after 

construction. These types of issues could not be accounted for in the models, and they 

would simply add variability to the analysis. 

Upon completion of the review, work began on developing regression models. Two 

regression methods were considered for use in the study: the general linear model (GLM) 

and the robust regression model. The GLM is susceptible to extreme outlying cases that 

cannot be definitively determined as erroneous data. Because this project incorporates 

national data with many contributing factors, extreme cases do exist that cannot be 

established as errors, and they need to be accounted for in the model. The robust 

regression techniques dampen the effect of these extreme cases by applying a weighting 

factor based on residuals. The robust model is used to make adjustments to the GLM and 

validate the model using an iterative process. 

Figure 7 is a plot of student residuals as a function of Hat values,(15) which is a graphical 

tool to evaluate observations in a dataset. The student residual is the ratio of a residual to 

its standard error. Large absolute values of the student residual (larger than 2.5) are an 

indication of outliers in the data. The Hat diagonal refers to the diagonal elements of the 

Hat matrix in the least squares estimation,(16) and it quantifies the leverage of each 

observation on the predicted value for that observation. The cluster of points located 

further to the right in figure 7 is a group of influential observations. 

Figure 7. Scatter plot. Outlier-influential observation detection plot. 

The robust method should be used if these influential observations are truly outliers or 

questionable data points that would negatively affect the model. On the other hand, these 

cases could be treated as any other with their full effect incorporated into the model (the 

GLM method) if they are valid data points that represent extreme conditions. 

To determine which method is more appropriate and will produce the most representative 

model, the nature of the dataset, as well as the number of quality control checks 

performed on the data, must be considered. If extreme cases are expected (given the 
41 

design of the experiment) and a rigorous quality review has been performed on the data, 

it is highly probable that the remaining influential observations are valid, and reducing 

their impact on the model would bias the model’s prediction capability. 

For this study, data come from a national database in which some of the variables may be 

set to extreme limits resulting in extreme performance observations. On a small scale, the 

SPS-1 projects can be used to illustrate this. Each of the 12 test sections at an SPS-1 

project has a different structural capacity, but all experience the same traffic loading. By 

experimental design, certain variables (in the case of SPS-1 projects, the ratio of traffic 

loading to structural capacity) would be set to the extreme ends of the spectrum. As such, 

extreme observations are to be expected in the dataset, and they are necessary to generate 

a model that reflects observed performance. 

In addition, the analysis team performed considerable logical quality review on the data 

to identify and remove data that were believed to be erroneous. The data have also 

undergone the quality control process used by the LTPP team before releasing the data 

for public use. It is unlikely that the remaining influential observations are erroneous or 

unrepresentative of the dataset. 

To further compare the two methodologies, two models were developed for absolute IRI 

of asphalt pavements. One model was developed using the robust method while the other 

used the GLM procedure. The predicted IRI values from the robust and GLM models 

versus the observed IRI values can be found in figures 8 and 9, respectively. The GLM 

method produces a model that has less bias than the robust model. In figure 8, the 

majority of the data points are clustered below the line of equality (circled in the figure). 

The cluster of the GLM model is more centered on the equality line compared with the 

robust model. This indicates that the robust method of reducing the effect of extreme 

observations results in a model that generally predicts values less than the observed 

values. 

Considering the nature of the dataset, the level of quality reviews performed on the data, 

and the results from the previous comparison, the GLM method was chosen to develop 

regression models for this study. 
42 

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Natural Logarithm of Observed Values

Natural Logarithm of Predicted Values

Figure 8. Scatter plot. Observed versus predicted values 

of absolute IRI (shifted) using the robust method. 

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Natural Logarithm of Observed Values

Natural Logarithm of Predicted Values

Figure 9. Scatter plot. Observed versus predicted values 

of absolute IRI (shifted) using the GLM method. 
43 

PAVEMENT ROUGHNESS PREDICTION MODELS 

It is well known that postconstruction roughness varies from one project to the next 

because of differences such as construction techniques and specifications. These 

differences also significantly affect the progression of roughness over time, and could 

add variability to the model. To counter this, the analysis team investigated models to 

predict change in IRI as the performance measure. As described previously, change in 

IRI was calculated by subtracting the first LTPP measurement from each of the 

subsequent measurements. Through this process, it was believed that the postconstruction 

differences were inherent to the initial IRI measurement and would be removed from the 

subsequent measurements. 

The resultant models, however, did not provide a good correlation with the observed 

dataset. The lack of fit can be partially contributed to the differences in age at which the 

first LTPP measurement was taken. For example, the first IRI measurement at test section 

086002 was taken at an age of 21.3 years while the initial measurement at test section 

100101 was taken at 1.1 years. The reference measurement used to calculate change in 

IRI for subsequent measurements was captured at different ages as well as at locations on 

the deterioration curve which was not accounted for in the model. 

Based on the observations made using the change in IRI, a decision was made to develop 

regression models using absolute IRI. To account for the postconstruction differences in 

roughness, initial IRI and the age of initial IRI measurement were incorporated as 

explanatory variables in the model. The models for this performance measure provided a 

better correlation than the change in IRI measure. Figure 10 provides a graph of actual 

values measured at test section 307066 along with values predicted by the model. As can 

be seen in the graph, the model is predicting the accumulation of roughness with time 

fairly accurately (indicated by equivalent slopes), but the model is offset from the actual 

measurements. Although only one example is shown, this offset was observed in many 

cases and varied for each test section. These differences can be reduced or eliminated by 

shifting the model to predict the initial IRI at the corresponding age of initial IRI 

measurement. The shifted model for test section 307066 can be found in figure 11. 

To further evaluate the prediction capability of the model, two scatter plots were 

generated using the flexible dataset. A scatter plot for the regression model (without 

shifting) is shown in figure 12, while the scatter plot for the shifted regression model is 

shown in figure 13. As can be seen from the figures, shifting the predicted values based 

on the initial IRI value results in an improvement in the model’s accuracy. 

Furthermore, the root mean squared error (RMSE) for the regression model (without 

shifting) was 0.18, while the shifted model exhibited a RMSE value of 0.17. RMSE is 

used to make relative comparisons on the “goodness of fit” between two models 

predicting the same performance measure (from the same dataset). Lower RMSE values 

are indicative of a model that represents the observed values better. 

In consideration of the reasons discussed, it was determined that the shifted model 

provided a better representation of the observed values in the dataset, and therefore, the 
44 

shifting methodology was used to predict pavement roughness over time for flexible 

pavements. 

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0 1 2 3 4 5 6 7 8 9 10

Age (years)

LN(IRI+0.1)

Observed IRI

Predicted IRI

Figure 10. Graph. Example of predicted (without shifting) and observed values 

for test section 307066. 

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0 1 2 3 4 5 6 7 8 9 10

Age (years)

LN(IRI+0.1)

Observed IRI

Normalized Predicted IRI

Figure 11. Graph. Example of predicted (shifted) and observed values 

for test section 307066. 
45 

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

OBSERVED LN(IRI+0.1)

PREDICTED LN(IRI+0.1)

Figure 12. Scatter plot. Flexible IRI model without shifting. 

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

OBSERVED LN(IRI+0.1)

PREDICTED LN(IRI+0.1)

Figure 13. Scatter plot. Flexible IRI model (shifted). 
46 

The same exercise was performed for rigid pavement roughness to determine if shifting 

the model made an improvement on its prediction capability. Figures 14 and 15 provide 

scatter plots for the model (without shifting) and the shifted model, respectively. In this 

case, the model (without shifting) resulted in a lower RMSE value (0.14) than the shifted 

model (RMSE equal to 0.15). Therefore, comparisons on rigid pavements were made 

using the model that was not shifted. 

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

OBSERVED LN(IRI+0.1) (m/km)

PREDICTED LN(IRI+0.1) (m/km)

1 m/km = 5.28 ft/mi 

Figure 14. Scatter plot. Rigid IRI model without shifting. 
47 

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

OBSERVED LN(IRI+0.1) (m/km)

PREDICTED LN(IRI+0.1) (m/km)

1 m/km = 5.28 ft/mi 

Figure 15. Scatter plot. Rigid IRI model (shifted). 

Different relationships between the performance measure, IRI, and pavement age were 

evaluated to ensure the model with the best prediction capability was selected for use in 

the environmental comparisons. Two models using different IRI-pavement age 

relationships were generated. The first model used a linear relationship between IRI and 

pavement age. Figure 16 provides a scatter plot of the predicted IRI values (shifted) 

versus the observed IRI values in the flexible dataset. An additional model was generated 

using an exponential relationship between IRI and pavement age. It is shown in figure 17 

for the flexible dataset. 

Using a linear relationship between IRI and age results in a model that is less biased than 

the exponential relationship. This is evident when comparing figures 16 and 17. In 

figure 16 the cluster of data points is more centered on the equality line as compared with 

figure 17 where the majority of the cluster falls below the line. This indicates a model 

that is generally predicting values less than the observed values. Further, the RMSE value 

for the linear model was considerably lower (0.17) than the exponential model (0.33). It 

is also interesting to note that the exponential model does not predict an increase in the 

IRI over time (as can be seen in figure 18). For these reasons, the linear relationship 

model was selected for consideration in the environmental comparisons. A scatter plot of 

predicted (shifted) versus observed values using the linear age relationship for the rigid 

dataset appears in figure 19. 
48 

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

OBSERVED LN(IRI+0.1)

PREDICTED LN(IRI+0.1)

Figure 16. Scatter plot. Flexible IRI model 

with linear IRI-age relationship. 

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0

OBSERVED LN(IRI+0.1)

PREDICTED LN(IRI+0.1)

Figure 17. Scatter plot. Flexible IRI model 

with IRI-exponential age relationship. 
49 

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0 2 4 6 8 10 12 14 16 18

Age (years)

LN(IRI+0.1) (m/km)

Observed IRI

Predicted IRI

1 m/km = 5.28 ft/mi

Figure 18. Scatter plot. Actual and predicted IRI values for test section 011001 

using IRI-exponential age relationship model. 

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

OBSERVED LN(IRI+0.1) (m/km)

PREDICTED LN(IRI+0.1) (m/km)

1 m/km = 5.28 ft/mi

Figure 19. Scatter Plot. Rigid IRI model 

with linear IRI-age relationship. 
50 

Equations representing the selected IRI models for both flexible and rigid pavements can 

be found in appendix B. The R-squared value for the flexible pavement IRI model 

(shifted) using the linear relationship between age and IRI is approximately 0.78 

(P-value < 0.0001). The rigid pavement IRI model exhibited an R-squared of 0.78 

(P-value < 0.0001). 

RUTTING PREDICTION MODELS FOR FLEXIBLE PAVEMENTS 

Two models were considered in the prediction of rut depth in flexible pavements. The 

first model incorporated a linear relationship between rut depth and pavement age. A 

scatter plot of predicted versus observed rut depth values for this model is shown in 

figure 20. It resulted in a RMSE value of 0.59. The second model under consideration for 

use in predicting rut depth incorporated a natural logarithm relationship between rut 

depth and pavement age. The scatter plot of predicted versus observed values can be 

found in figure 21 corresponding to an RMSE value of 0.55. 

-4

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4

OBSERVED LN(RUT+0.1)

PREDICTED LN(RUT+0.1)

Figure 20. Scatter plot. Rut depth model 

with linear rut–age relationship. 
51 

-4

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4

OBSERVED LN(RUT+0.1)

PREDICTED LN(RUT+0.1)

Figure 21. Scatter plot. Rut depth model 

with rut–natural logarithm age relationship. 

The second model was selected for use in comparing rut depth performance of pavement 

in different environmental regions because of the improved fit of the dataset. Rutting 

mechanisms generally result in an increased rate of rutting in the early years of pavement 

life. As the pavement ages, this rate diminishes and rutting values level off (following a 

logarithmic relationship). Applying the logarithmic relationship in the model provides a 

better representation of the dataset, which is evident in the improved RMSE value. 

Details on this model can be found in appendix B. The adjusted R-squared value for this 

model was approximately 0.45 (P-value < 0.0001). 

SURFACE DISTRESS PREDICTION MODELS FOR BOTH FLEXIBLE AND 

RIGID PAVEMENTS 

To develop the flexible dataset for distress measures, all three severity levels for each 

distress type were combined through the use of deduct curves developed for the South 

Dakota Department of Transportation(10) to obtain a deduct value for each distress. The 

study considered FC, BC, LWP, and TC. Because LWP often progresses to FC, the two 

distress types were combined into FWPC). LWP was converted from a linear unit to a unit 

of area to be consistent with FC. This was done by applying a standard width of 0.3 m (1 

ft) to the recorded length of LWP. All severities of LWP were considered as low severity 

to compute deduct values that would be combined with the FC. 

The format of distress data collected on rigid pavements does not match the required 

format used in the established deduct curves;(8) therefore, the severity levels were 

summed for each distress type. This total distress was then normalized based on the size 
52 

of the test section in the same manner as the flexible sections. CB, LC, TC, and PUMP 

distress types were used in the rigid dataset. 

Figure 22 provides a scatter plot of FWPC as a function of pavement age. As can be seen 

from the figure, there is a large amount of variability in the data, and numerous zeroes are 

recorded across the entire range of ages. For these reasons, regression models alone did 

not provide a good correlation with the measured values. 

A small subset of the measured FWPC values was plotted (figure 23). Each series in the 

figure represents data from one test section. It appears that a substantial portion of the 

variability in the data can be attributed to the differences in age at which distress initiates. 

For example, distress initiation occurs just after construction at two of the sections, while 

another section does not initiate distress until age 17. There does appear to be a 

reasonable trend in the accumulation of distress with age after the initiation of distress. 

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30

Age (years)

FWP (deduct value)

Figure 22. Scatter plot. Measured FWPC deduct values. 
53 

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30

Age (years)

FWPC

Figure 23. Graph plot. Measured FWPC values 

(using a subset of test sections). 

Given the nature and form of the distress accumulation with age, two models were used 

concurrently to predict distress progression. The first model was used to predict the age at 

which distress initiation occurs, while the second estimated the accumulation of distress 

with age (after initiation). 

The first model was developed using logistic analysis to predict age at which distress first 

appears. Logistical models predict the probability of an event occurring (e.g., distress 

initiation or nonzero distress value) given a set of variables including pavement age. Figure 

24 shows an example of a logistic model. A cutoff probability must be established to 

predict an initiation age from the given model. As the cutoff probability increases, the 

accuracy of the model predicting events goes down, while the accuracy of predicting 

nonevents goes up; therefore, the selection of the cutoff probability depends on the nature 

of the data and the relative importance of events compared to nonevents. In the case of 

distress prediction, events and nonevents are of equal importance, so a cutoff value was 

selected that predicted each with equal accuracy. To determine the initiation age, all inputs 

for the logistic model are held constant (for a particular pavement section) except for 

pavement age, which is increased until the predicted probability is equivalent to the 

selected cutoff probability. This pavement age is defined as the initiation age. Table 19 is 

provided as an example illustrating the effect of the probability level on the accuracy of the 

model. The sensitivity denotes the percentage of events correctly identified, while the 

specificity reflects the nonevent accuracy. As the probability increases, the sensitivity 

decreases and the specificity increases. These two measures are approximately equal at the 

0.7 probability level indicating events and nonevents, and are predicted with equal 

accuracy; therefore, 0.7 was established as the cutoff probability. 
54 

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10 12 14 16 18 20

AGE (years)

PREDICTED(deduct values)

Deep Freeze Wet Region

Moderate Freeze Wet Region

No Freeze Wet Region

Deep Freeze Dry Region

Moderate Freeze Dry Region

Cut-off Probability

Figure 24. Graph plot. Example of logistical analysis 

to predict distress initiation. 
55 

Table 19. Example of probability level effect on logistic prediction. 

Correct Incorrect Percentages 

Probability 

Level Event NonEvent Event NonEvent Correct Sensitivity Specificity False 

POS 

False

NEG 

0.1 1370 1 606 0 69.3 100.0 0.2 30.7 0.0

0.2 1365 60 547 5 72.1 99.6 9.9 28.6 7.7

0.3 1357 179 428 13 77.7 99.1 29.5 24.0 6.8

0.4 1326 272 335 44 80.8 96.8 44.8 20.2 13.9

0.5 1232 361 246 138 80.6 89.9 59.5 16.6 27.7

0.6 1128 429 178 242 78.8 82.3 70.7 13.6 36.1

0.7 955 480 127 415 72.6 69.7 79.1 11.7 46.4

0.8 753 517 90 617 64.2 55.0 85.2 10.7 54.4

0.9 511 556 51 859 54.0 37.3 91.6 9.1 60.7

1.0 0 607 0 1370 30.7 0.0 100.0 NA 69.3

After the crack initiation age is predicted, linear regression models were used to predict 

the accumulation of distress with age (after initiation). The example in figure 24 indicates 

that the distress initiation ranges from approximately 7 years to more than10 years for the 

different environmental regions. 

The pavement age variable in the dataset had to be adjusted to reflect age after distress 

initiation to develop the regression models used to predict surface distress. Two 

methodologies were used to adjust pavement age depending on the timing of distress 

initiation relative to the pavement monitoring period. 

Some of the test sections were monitored both before and after crack initiation. For these 

cases, crack initiation was directly determined as the maximum pavement age where a 

zero distress value was observed. This crack initiation age was then used to adjust the 

remaining pavement ages to ages after distress initiation. An example of one such test 

section is presented in figure 25. The crack initiation age was determined to be 2.4 years. 

The remaining ages were then adjusted by subtracting 2.4 years from the pavement age to 

obtain age after initiation. 
56 

0

10

20

30

40

50

60

01234567

Age (years)

FWPC (deduct value)

Figure 25. Graph plot. Observed FWPC deduct values for test section 100102. 

For test sections that were not monitored before the distress initiation, linear regression 

was performed on each test section and used to determine the age at which the distress 

was initiated. An example of this is shown graphically in figure 26. The initiation age 

estimated from the regression equation was subtracted from subsequent pavement ages to 

get the age after initiation. The regression models were developed using only nonzero 

distress values (i.e., values recorded after initiation) and replacing age with these 

calculated adjusted age values. 

Distress Initiation Age 
57 

0

5

10

15

20

25

30

35

40

45

0 1 2 3 4 5 6 7 8 9 10

Pavement Age (years)

FWPC deduct value

Figure 26. Graph plot. Observed FWPC deduct values for test section 050121 

(with regression line). 

Similar to other performance measures, multiple regression models were developed for 

each distress type using different distress-age relationships. These models were evaluated 

to select the model that predicts the observed values with the best accuracy. The first 

model incorporated a linear relationship between distress and pavement age while the 

second model was developed using a natural logarithm relationship. 

Figure 27 provides a scatter plot of observed versus predicted values for the FWPC 

model using the logistic analysis coupled with the linear distress/pavement age regression 

model. Figure 28 shows a scatter plot for values generated from the regression model 

with the natural logarithmic relationship. 

The vertical lines on the left side and the horizontal lines on the bottom of both figures 

indicate the error within the logistic analysis. The data points on the vertical lines (above 

the line of equality) are a result of the logistic analysis predicting distress initiation earlier 

than it was actually observed. Conversely, instances where the logistic analysis predicted 

crack initiation later than observed appear as data points on the horizontal line to the right 

of the line of equality. Although it looks as if only one of these data points falls on the 

line of equality, in actuality approximately 1,800 of the 2,400 points predicted using the 

logistic analysis were classified correctly as events or nonevents. Because the probability 

level of 0.7 resulted in equal sensitivity and specificity for the logistic model, it was 

selected as the cutoff probability. The vertical lines on the right side and the horizontal 

line on the top of both figures are the results of establishing a maximum allowable deduct 

value of 100. 

Distress Initiation Age 
58 

-5

-4

-3

-2

-1

0

1

2

3

4

5

-5 -4 -3 -2 -1 0 1 2 3 4 5

OBSERVED LN(FWPC+0.1)

PREDICTED LN(FWPC+0.1)

Figure 27. Scatter plot. FWPC model for flexible pavements 

with linear FWPC-age relationship. 

-5

-4

-3

-2

-1

0

1

2

3

4

5

-5 -4 -3 -2 -1 0 1 2 3 4 5

OBSERVED LN(FWPC +0.1)

PREDICTED LN(FWPC+0.1)

Figure 28. Scatter plot. FWPC model for flexible pavements 

with FWPC-natural logarithm age relationship. 
59 

In comparing the two models, the cluster of data points in figure 28 are more densely 

clustered around the line of equality. The linear regression model exhibits a larger RMSE 

value (1.85) as compared with the logarithmic regression model (1.41). In addition, the 

linear relationship model overpredicts a larger percentage of data points compared with 

the logarithmic relationship (evident in the horizontal line on the top of figure 27). For 

these reasons, the natural logarithmic relationship was selected for use in the 

environmental comparisons. 

Appendix B provides details on the logistic and regression models used to predict FWPC. 

The R-squared value for the regression model was 0.63 (P-value < 0.0001). In addition, a 

regression model was developed for FWPC accumulation in terms of percentage of 

wheelpath area, and it was used in the description of application to mechanistic design 

(chapter 10). The R-squared value for this regression model was 0.49 (P-value < 0.0001). 

Figures 29 and 30 provide scatter plots for TC models using linear and logarithmic 

relationships, respectively. To evaluate the logistic analysis, approximately 1,900 out of 

2,400 records were accurately categorized as events or nonevents by the logistic model. 

This was achieved with a cutoff probability of 0.6. Similar to the FWPC models, the 

logarithmic relationship model exhibits an improved RMSE value of 1.21 as compared to 

a value of 1.69 for the linear model; therefore, the natural logarithmic relationship model 

was selected for use in the environmental comparisons. 

Appendix B contains information on the TC logistic and regression models. The adjusted 

R-squared value for the TC regression model is approximately 0.71 (P-value < 0.0001). 

-5

-4

-3

-2

-1

0

1

2

3

4

5

-5 -4 -3 -2 -1 0 1 2 3 4 5

OBSERVED LN(TC+0.1)

PREDICTED LN(TC+0.1)

Figure 29. Scatter plot. TC model for flexible pavements 

with linear TC-age relationship. 
60 

-5

-4

-3

-2

-1

0

1

2

3

4

5

-5 -4 -3 -2 -1 0 1 2 3 4 5

OBSERVED LN(TC+0.1)

PREDICTED LN(TC+0.1)

Figure 30. Scatter plot. TC model for flexible pavements 

with TC-natural logarithm age relationship. 

Additional regression models were developed for block cracking. The regression models 

include a negative coefficient for pavement age. This indicates a reduction in pavement 

distress with increases in pavement age (under certain environmental conditions). The 

model’s poor correlation and negative distress age relationship can be attributed to the 

relatively small percentage of records in the dataset with nonzero BC deduct values 

(approximately 90 of the 2,400). In addition, it is probable that rather variability 

contributed to the regression results. Due to the subjective nature of data collection, an 

area of distress could be rated as block cracking during one data collection visit and 

subsequently rated as a series of longitudinal and transverse cracking on the next survey. 

This would result in a reduction in block cracking quantities with age. Therefore, given 

these issues as well as the results of the regression model, block cracking models were 

not evaluated as part of the performance comparisons. 

CB was modeled using data from the rigid pavements selected for use in this study. 

Scatter plots for the linear and logarithmic CB-age relationship models appear in 

figures 31 and 32, respectively. 
61 

-2.5

-2

-1.5

-1

-0.5

0

-2.5 -2 -1.5 -1 -0.5 0

OBSERVED LN(CB+0.1) occurences/panel

PREDICTED LN(CB+0.1) occurences/panel

Figure 31. Scatter plot. CB model for rigid pavements 

with linear CB-age relationship. 

-2.5

-2

-1.5

-1

-0.5

0

-2.5 -2 -1.5 -1 -0.5 0

OBSERVED LN(CB+0.1) occurences/panel

PREDICTED LN(CB+0.1) occurences/panel

Figure 32. Scatter plot. CB model for rigid pavements 

with CB-natural logarithm age relationship. 
62 

As can be seen from the figures, both models provide poor correlation between predicted 

and observed values. This is caused, in part, by the limited number of nonzero 

observations in the dataset (approximately 3 percent of the records) in which the 

regression analysis was developed. The quantity of data points used in the regression was 

not large enough to account for the contribution of each explanatory variable. As such, 

the models developed from CB data were not used to make performance comparisons. 

Additional analysis was performed on the rigid dataset to predict longitudinal cracking 

(LC). Consistent with the other distress performance measures, two models with different 

relationships between LC and pavement age were developed. Scatter plot results from the 

linear relationship model can be found in figure 33, while figure 34 provides similar 

results for the logarithmic model. The RMSE values for the linear and logarithmic 

relationship models were determined to be 1.49 and 1.42, respectively. 

Because the model developed using the logarithmic relationship between LC and 

pavement age provides less variability compared with the linear relationship, it was 

selected for use in the performance comparison analysis. The logistic model correctly 

classified approximately 990 of the 1,350 records in the dataset. A cutoff probability of 

0.55 was selected to determine initiation age. The regression portion of the model 

exhibits an R-squared value of 0.38 (P-value < 0.0001). Appendix B provides equations 

detailing the logistic and regression models for LC performance on rigid pavements. 

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

OBSERVED LN(LC+0.1) % of total section length

PREDICTED LN(LC+0.1) % of total section length

Figure 33. Scatter plot. LC model for rigid pavements 

with linear LC-age relationship. 
63 

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

OBSERVED LN(LC+0.1) % of total section length

PREDICTED LN(LC+0.1) % of total section length

Figure 34. Scatter plot. LC model for rigid pavements 

with LC-natural logarithm-age relationship. 

Similar analysis was performed for TC of rigid pavements. Scatter plots for each of the 

models is provided in figures 35 and 36. As can be seen from the graphs, the logarithmic 

relationship model (RMSE equals 1.17) results in a better correlation between observed 

and predicted values as compared with the linear model (RMSE equals 1.24). The 

adjusted R-squared for the regression model is 0.53 (P-value < 0.0001) and the logistic 

analysis correctly categorized 920 of the 1,350 records in the dataset (at the selected 

cutoff probability of 0.6). 
64 

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

OBSERVED LN(TC+0.1) % Total Section Length

PREDICTED LN(TC+0.1) % Total Section Length

Figure 35. Scatter plot. TC model for rigid pavements 

with linear TC-age relationship. 

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

OBSERVED LN(TC+0.1) (% of affected length)

PREDICTED LN(TC+0.1) (% of affected length)

Figure 36. Scatter plot. TC model for rigid pavements 

with TC-natural logarithm age relationship. 
65 

Last, PUMP was modeled for the rigid pavement dataset. It should be noted that there 

was limited accumulation of pumping in the dataset. Approximately 110 records (8 percent) of the dataset exhibited nonzero PUMP values. The number of nonzero records is 

relatively small compared with the number of explanatory variables considered in the 

regression developed. Figure 37 provides the prediction capabilities of the model 

developed with a linear relationship between PUMP and pavement age. Figure 38 shows 

the results from the logarithmic model. Predicted values from the models do not correlate 

well with observed values, and therefore, they were not used to make performance 

comparisons. 

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

OBSERVED LN(PUMP+0.1) % of total section length

PREDICTED LN(PUMP+0.1) % of total section length

Figure 37. Scatter plot. PUMP model for rigid pavements 

with linear PUMP-age relationship. 
66 

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

OBSERVED LN(PUMP+0.1) % of total section length 

PREDICTED LN(PUMP+0.1) % of total section length

Figure 38. Scatter plot. PUMP model for rigid pavements 

with PUMP-natural logarithm age relations. 

TRANSVERSE JOINT FAULTING PREDICTION MODELS FOR RIGID 

PAVEMENTS 

Faulting provides an indication of the joint integrity (load transfer efficiency) as well as 

the condition of the underlying unbound layers. In addition, pavement roughness is 

directly affected by the magnitude of faulting, and performance models were developed 

for transverse joint faulting of rigid pavements. The average faulting value for all joints 

on a test section was established as the performance measure. 

Two models were developed to predict the accumulation of faulting (FLT) with pavement 

age. The first model incorporated a linear relationship between the performance measure 

and pavement age; the second regression was developed with a logarithmic relationship. 

Figure 39 provides the scatter plot for the linear relationship model. Figure 40 represents 

the observed versus predicted values for the logarithmic relationship model. 

The RMSE for the linear relationship model is approximately 0.37, which is an 

improvement over the logarithmic model (RMSE equal to 0.39). In addition, the 

logarithmic model predicted a decrease in accumulated faulting with an increase in 

pavement age in some cases. This is not logical because faulting should not improve as 

the pavement structure ages and is exposed to traffic loading. In consideration of this fact, 

as well as the improved goodness of fit, the linear relationship model was selected for use 

in the study. 
67 

Details regarding the model, which resulted in an adjusted R-squared value of 0.47 (Pvalue < 0.0001), appear in appendix B. 

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

OBSERVED LN(FLT+1)

PREDICTED LN(FLT+1)

Figure 39. Scatter plot. FLT model for rigid pavements 

with linear FLT-age relationship. 

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

OBSERVED LN(FLT+1)

PREDICTED LN(FLT+1)

Figure 40. Scatter plot. FLT model for rigid pavements 

with FLT-natural logarithm age relationship.

69 

6. ENVIRONMENTAL PERFORMANCE COMPARISONS 

One of the main objectives of this study was to compare pavement performance in 

various climatic settings. The environmental regions of interest were loosely defined as 

(1) southern freeze/northern no-freeze, (2) northern freeze, and (3) southern no-freeze 

regions. These regions were intended to capture pavement performance in areas with 

moderate frost depth (with multiple FTCs), deep sustained frost depth, and very little 

frost depth to provide a means of comparing the effect of FTC compared with the 

contribution of deep frost penetration (represented by FI) on pavement deterioration. As 

such, the following groupings were established for this study: 

• Deep-freeze, wet region. 

• Deep-freeze, dry region. 

• Moderate-freeze, wet region. 

• Moderate-freeze, dry region. 

• No-freeze, wet region. 

The deep-freeze regions were established to represent typical climatic conditions of the 

northern freeze zones where deep sustained frost penetration was presumed to occur. The 

moderate-freeze regions correspond to the southern freeze/northern no-freeze zone, 

which implied a multiple FTC climate. The region was established to represent the 

southern no-freeze region where frost penetration and multiple FTCs are very minimal. 

While LTPP has defined wet/dry regions in terms of annual precipitation—508 mm/year 

(20 inches/year) and freeze/no-freeze regions as a function of FI (83.3 degree-Celsius 

days), the limits of the regions listed above have not been previously established; 

therefore, an investigation of FI and FTC relationships was performed on the analysis 

dataset to define limits for each of the scenarios. 

Figure 41 provides the relationship between FI and FTC based on data from all test 

sections evaluated in the study. The no-freeze region was limited to areas with an FI less 

than 50 degree-Celsius days while the moderate-freeze was defined with FI between 50 

and 400 degree Celsius days. The deep-freeze region consists of locations exhibiting an 

FI greater than 400 degree-Celsius days. These limits were established based on the 

natural break points inherent in the dataset, while also considering the LTPP-defined 

criteria for the freeze-, no-freeze regions (i.e., the moderate-freeze region must be 

centered on the LTPP freeze/no-freeze cutoff and include data points in both zones). The 

geographic boundaries for each of the regions were used to verify that selected FI limits 

were logical. The geographic location of each test section is plotted in figure 42. 
70 

0

1

10

100

1000

0 1 10 100 1000 10000

FI (degree-Celsius days)

FTC

No-Freeze (FI<50)

Moderate-Freeze (50<FI<400)

Deep-Freeze (FI>400)

LTPP Freeze/No-Freeze limit (FI=83.3)

Figure 41. Scatter plot. Regional FI and FTCs values. 

Figure 42. Map. Geographic locations of climatic regions. 

Moderate-Freeze ▲

Deep-Freeze ■

No-Freeze ●
71 

The majority of test sections classified as no-freeze is located in the Southern States with 

a few remaining sites located along the Pacific coast of the United States. The deepfreeze region consists of sites located in Canada and the northern United States. The band 

of sites in the moderate-freeze region is located across the middle of the United States 

running from northern Texas in the south to southern Washington in the north. These 

geographic locations are logical and consistent with the expected climates across the 

continent. 

It should be noted that the following statement from the Task Order Proposal Request 

(TOPR) description of work indicates an inference that there are fewer FTCs in the 

northern freeze zone as compared with the moderate-freeze zone: 

Near the southern edge of the freeze/no-freeze zone, pavements are subjected to 

multiple freeze-thaw cycles each year. Farther north, pavements may be subjected 

to few freeze-thaw cycles, but the depth of frost penetration is generally greater. 

By inspection of figure 43, FTC values do not decrease in the deep-freeze regions (i.e., 

relatively high FI locations). Rather, the amount of FTC is approximately equivalent in 

the moderate- and deep-freeze regions; therefore, deep frost depths and multiple freezethaw cycles are not necessarily mutually exclusive factors and pavements do exist in 

areas that are classified as deep-freeze that also experience a large number of FTC. 

0

20

40

60

80

100

120

140

160

180

200

0 200 400 600 800 1000 1200 1400 1600 1800 2000

FI (degree-Celsius days)

FTC

No-Freeze (FI<50)

Moderate-Freeze (50<FI<400)

Deep-Freeze (FI>400)

LTPP Freeze/No-Freeze limit (FI=83.3)

Figure 43. Scatter plot. Relationship between FI and FTCs. 
72 

The above analysis was used to define the limits of deep-freeze, moderate-freeze, and nofreeze regions based on FI. To represent these climatic scenarios in the performance 

models, input values for each had to be established. Tables 20 and 21 provide the 

explanatory variable (i.e., input) values used to represent each of the regions for flexible 

and rigid pavements respectively. Nonenvironmental variables such as ACTHICK and 

logarithm ESAL divided by structural number (LESN) were held constant for each of the 

regions; therefore, performance predictions were made for consistent pavement structures 

exposed to similar traffic loading. This allowed the climatic effects on pavement 

performance to be compared without confounding the results by varying other 

contributing factors. The values for these nonclimatic factors were selected as the median 

value present in the analysis dataset. The scenarios listed in tables 19 and 20 incorporate 

constant BASE and SG types (DGAB and FINE, respectively). Predictions made using 

these two layer types are presented in the following text. Performance estimates were also 

established for other base/subgrade combinations, and key findings from those 

combinations are provided in the following discussion. This includes predictions for 

flexible pavement structures with overlay layers as well. 

Table 20. Overview of climatic scenarios for flexible pavements. 

Scenarios ACTHICK BASE SG LESN EXP FI FTC PRECIP CI 

Deep-Freeze, Wet 

Region (low FTC) 6.5 DGAB FINE 1.02 G1 688 80 1140 205

Moderate-Freeze, 

Wet Region (high 

FTC) 

6.5 DGAB FINE 1.02 G1 137 130 1140 645

No-Freeze, Wet 

Region 6.5 DGAB FINE 1.02 G1 10 10 1140 1300

Deep-Freeze, Dry 

Region (low FTC) 6.5 DGAB FINE 1.02 G1 688 80 380 205

Moderate-Freeze, 

Dry Region (high 

FTC) 

6.5 DGAB FINE 1.02 G1 137 130 380 645

Table 21. Overview of climatic scenarios for rigid pavements. 

Scenarios FC D BASE SG LEDT EXP FI FTC PRECIP CI 

Deep-Freeze, Wet 

Region (low FTC) 2 9.5 DGAB FINE 0.59 S2 688 80 1140 205

Moderate-Freeze, Wet 

Region (high FTC) 2 9.5 DGAB FINE 0.59 S2 137 130 1140 645

No-Freeze, Wet 

Region 2 9.5 DGAB FINE 0.59 S2 10 10 1140 1300

Deep-Freeze, Dry 

Region (low FTC) 2 9.5 DGAB FINE 0.59 S2 688 80 380 205

Moderate-Freeze, Dry 

Region (high FTC) 2 9.5 DGAB FINE 0.59 S2 137 130 380 645
73 

Table 22 gives process details used to select environmental values for each climatic 

region shown in tables 20 and 21. With the exception of FTC, the values selected for all 

environmental variables were the median value present in the region. For example, the 

median FI of all test sections in the no-freeze region (i.e., 0<FI<50) was found to be 10 

degree-Celsius days. Similarly, the CI value of 205 degree-Celsius days was determined 

to be the median CI value present for test sections in the deep-freeze region (FI>400). 

Because the aim of the study is to compare the trade-off in pavement deterioration 

between deep frost and multiple FTCs, the percentile used to select FTC values was 

different for each region. In the deep-freeze region, one of the lowest FTC values present 

(10th percentile) was selected while one of the highest values was identified for the 

moderate-freeze region (90th percentile). This approach was used to isolate the 

contribution of FI from FTC in the deep-freeze region and allow the contribution of FTC 

to be investigated in the moderate-freeze region. As a caveat, it is apparent that 

pavements do exist in areas with both deep-frost penetration and multiple FTCs as can be 

seen in figure 43. 

Table 22. Details on selection of environmental variables. 

Variable Percentile Criteria Value 

Deep-Freeze (FI) 50th, deep-freeze (>400 FI) 688 

Moderate-Freeze (FI) 50th, moderate-freeze (50<FI<400) 137 

No-Freeze (FI) 50th, no-freeze (<50 FI) 10 

Low FTC 10th, FTC values in deep-freeze (>400 FI) 80 

High FTC 90th, FTC values in mod-freeze (50<FI<400) 130 

No-Freeze FTC 50th, FTC values in no-freeze (<50 FI) 10 

Dry (PRECIP) 50th, dry region (<508mm) 380 

Wet (PRECIP) 50th, wet region (>508mm) 1140 

Deep-Freeze (CI) 50th, deep-freeze (>400 FI) 205 

Moderate-Freeze (CI) 50th, moderate-freeze (50<FI<400) 645 

No-Freeze (CI) 50th, no-freeze (<50 FI) 1300 

When making comparisons, a determination must be made as to whether performance 

differences in the prediction of each scenario are statistically significant. The relatively 

small differences in performance can be confounded by the error inherent in the models. 

These differences cannot be attributed solely to the contribution of climatic setting. 

Rather, it is likely that the variability in the model is the source of the differences. 

Confidence intervals were used to determine if differences in performance were 

statistically significant. A confidence interval of 95 percent was selected for this study. 

Using a lower confidence interval (i.e., 90 percent) would possibly lead to additional 

significant differences between the scenarios. Scenario departures that are marginally 

insignificant at the 95 percent confidence interval would likely be significant at the 

90 percent level. The 95 percent level was used to provide highly significant differences, 

and it can also indicate the significance of variant performance. One can use the 
74 

confidence intervals provided to determine which marginal differences could be 

significant at a lower confidence interval. 

In general, performance comparisons were made at a pavement age of 20 years for 

flexible pavements. This timeframe allows deterioration to accumulate to a sufficient 

level to make performance comparisons, and it is well within the range of pavement ages 

in the dataset used to develop the models. Approximately 80 out of 520 test sections in 

the flexible dataset were monitored at ages greater than 20 years. As such, there are 

sufficient quantities of observed data past 20 years represented in the models, and 

predictions made at an age of 20 years are not extrapolations from the dataset. In 

addition, the objective of the analysis was to study long term differences in performance 

so that financial effects of climate can be evaluated through life cycle cost analysis. The 

20-year basis will provide a means of accomplishing this. 

Comparisons were made at 20 years for the majority of the rigid pavement performance 

measures as well. Approximately 50 of the 275 test sections were monitored at ages 

greater than 20 years. Some of the scenarios evaluated initiated surface distress at after 

20-year ages. To make comparisons at a point where all scenarios have initiated distress, 

the reference age was changed to 25 years for both TC and LC surface distress 

comparisons. Even at 25 years, the observations were not based on extrapolated 

predictions because approximately 29 of the 275 test sections were monitored at ages 

greater than 25 years. This reference age was not extended to 30 years because only six 

test sections were monitored at greater ages, which is a relatively limited number 

compared to the number of explanatory variables incorporated in the models. 

Because transformations were incorporated into the model development process, it is 

essential that comparisons were made using the transformed scale. Mean predictions and 

confidence intervals were established based on transformed values; therefore, 

comparisons must be made in the same scale used to develop the estimates to be accurate. 

As such, all of the following prediction and confidence interval figures use a transformed 

scale (i.e., natural logarithm). Results can be converted to a linear scale if the model 

output is used solely as a predictive tool and the confidence intervals are not used for 

comparison. 

PAVEMENT ROUGHNESS COMPARISONS FOR FLEXIBLE PAVEMENTS 

Mean pavement roughness predictions were made for each of the scenarios defined 

above, and they are provided in figure 44 as a function of age. These predictions were 

established using an initial IRI of 1 m/km (63.3 inches/mi) at a pavement age of 1 year. 

Mean predicted values at 20 years along with the upper and lower limits of the 95 percent 

confidence interval are provided in figure 45 for each climatic region. 

Figure 44 makes it apparent that pavement roughness accumulates at a more rapid rate in 

the wet deep-freeze and dry deep-freeze regions compared to the other regions, based on 

mean prediction values; however, these differences are not significant at 95 percent 

confidence and pavement age of 20 years as indicated in figure 45. The variability in the 

model confounds the differences observed in the mean predictions. The same 
75 

observations can be made for all other base/subgrade combinations for both overlay and 

nonoverlay pavement structures. 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 2 4 6 8 10 12 14 16 18 20

AGE (years)

PREDICTED Ln(IRI+0.1) (m/Km)

Deep-Freeze, Wet Region

Moderate-Freeze, Wet Region

No-Freeze, Wet Region

Deep-Freeze, Dry Region

Moderate-Freeze, Dry Region

1 m/km = 5.28 ft/mi 

Figure 44. Scatter chart. Mean predicted flexible pavement IRI values 

for each climatic region (BASE=DGAB/SG=FINE). 

0.50

0.52

0.54

0.56

0.58

0.60

0.62

0.64

0.66

0.68

0.70

Deep-Freeze, Wet Region

(low FTC)

Moderate-Freeze, Wet

Region (high FTC)

No-Freeze, Wet Region Deep-Freeze, Dry Region

(low FTC)

Moderate-Freeze, Dry

Region (high FTC)

Predicted LN (IRI+0.1) (m/Km)

1 m/km = 5.28 ft/mi 

Figure 45. Bar chart. Predicted flexible pavement IRI values at 20 years 

for each climatic region (BASE=DGAB/SG=FINE). 
76 

As documented by Kameyama et al.(16) considerable increases in roughness during the 

freezing season do exist in areas of moderate and deep frost penetration; however, the 

increased roughness subsides by the middle of the following summer season. Based on 

the results in figure 45, any adverse long-term effects on flexible pavements from these 

seasonal cycles observed in the freezing areas are confounded by other variability. 

PAVEMENT ROUGHNESS COMPARISONS FOR RIGID PAVEMENTS 

Figure 46 provides mean rigid pavement roughness predictions as a function of age for 

each of the scenarios listed in table 20. Performance comparisons can be made using 

figure 47, which provides 95-percent-confidence interval information at a pavement age 

of 20 years for each of scenario. 

1 m/km = 5.28 ft/mi

Figure 46. Scatter graph. Mean predicted rigid pavement IRI values 

for each climatic region (BASE=DGAB/SG=FINE). 

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 2 4 6 8 10 12 14 16 18 20

Pavement Age (years)

PREDICTED IRI (m/km)

Deep-Freeze, Wet Region (low FTC)

Moderate-Freeze, Wet Region (high FTC)

No-Freeze, Wet Region

Deep-Freeze, Dry Region (low FTC)

Moderate-Freeze, Dry Region (high FTC)
77 

0.3

0.4

0.5

0.6

0.7

Deep-Freeze, Wet Region

(low FTC)

Moderate-Freeze, Wet

Region (high FTC)

No-Freeze, Wet Region Deep-Freeze, Dry Region

(low FTC)

Moderate-Freeze, Dry

Region (high FTC)

PREDICTED LN(IRI+0.1) (m/km)

1 m/km = 5.28 ft/mi

Figure 47. Bar chart. Predicted rigid pavement IRI values at 20 years 

for each climatic region (BASE=DGAB/SG=FINE). 

Predicted pavement roughness at 20 years in areas of deep frost penetration is 

significantly different than in areas experiencing multiple FTCs. This can be seen in both 

the dry and wet climates. The wet no-freeze region exhibits roughness values that are 

significantly lower than that of the moderate- and deep-freeze wet regions. Rigid 

pavements in relatively wet climates and in areas exposed to frost penetration accumulate 

roughness more rapidly than wet climates in areas with no frost. 

Similarly, the same observations for other base/subgrade combinations can be made. One 

exception is prediction values for pavements with ROCK/STONE subgrade type. 

Variability with this subgrade type results in insignificant performance differences 

between the dry multiple freeze-thaw climate and the wet no-freeze region. 

RUT DEPTH COMPARISONS FOR FLEXIBLE PAVEMENTS 

Mean predictions for the progression of rut depth with pavement age can be found in 

figure 48 for each of the climatic regions. The 20-year confidence interval results are 

presented in figure 49. Both of these graphs were established for DGAB base and FINE 

subgrade. 

The following conclusions can be drawn for figure 49: While multiple FTCs and deep 

sustained frost penetration do not result in changes in rutting performance in wet climates 

(at a 95 percent confidence interval), pavements in both of these climates accumulate 

larger amounts of rutting compared to the no-freeze climates; conversely, in the dry 
78 

region, multiple FTCs contribute to more rutting deterioration than both the deep-frost 

and no-freeze zones. The contribution of deep-frost penetration in relatively dry climates 

on rutting is not significantly different than that of the no-freeze wet region. 

0

1

2

3

4

5

6

7

8

9

10

0 2 4 6 8 10 12 14 16 18 20

Pavement Age (years)

PREDICTED RUT (mm)

Deep-Freeze, Wet Region (low FTC)

Moderate-Freeze, Wet Region (high FTC)

No-Freeze, Wet Region

Deep-Freeze, Dry Region (low FTC)

Moderate-Freeze, Dry Region (high FTC)

1 mm = 0.04 inch

Figure 48. Scatter graph. Mean predicted flexible pavement RUT values 

for each climatic region (BASE=DGAB/SG=FINE). 
79 

1.5

1.6

1.7

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

Deep-Freeze, Wet Region

(low FTC)

Moderate-Freeze, Wet

Region (high FTC)

No-Freeze, Wet Region Deep-Freeze, Dry Region

(low FTC)

Moderate-Freeze, Dry

Region (high FTC)

PREDICTED LN(RUT+0.1) (mm) 

1 mm = 0.04 inch

Figure 49. Bar chart. Predicted flexible pavement RUT values at 20 years 

for each climatic region (BASE=DGAB/SG=FINE). 

Rutting predictions for different base and subgrade combinations as well as overlay 

structures were also evaluated. In general, the observations discussed above for DGAB 

base, FINE subgrade, and nonoverlay structures can be made for other combinations. 

However, ROCK/STONE subgrade as well as LCB base types exhibit relatively large 

variability in both overlay and nonoverlay structures. This results in any performance 

differences between climates being statistically insignificant. 

Rutting accumulation in pavements with PATB base type is not significantly different in 

areas exposed to deep-frost penetration and multiple FTCs (regardless of precipitation 

level). These accumulations are, however, significantly larger than the no-freeze wet 

region. 

For pavements with NONBIT base, the multiple FTC climate exhibits significantly larger 

quantities of rutting as compared with the deep-frost penetration and no-freeze regions. 

Differences between the contribution of deep-frost penetration and no-freeze wet climates 

on rutting development are confounded by the variability in the model. 
80 

FATIGUE AND WHEELPATH CRACKING SURFACE DISTRESS 

COMPARISONS FOR FLEXIBLE PAVEMENTS 

Mean predictions of FWPC progression for pavements with FINE subgrade and DGAB 

base are provided in figure 50. These predictions were generated by combining logistic 

analysis with regression models. As can be seen, variations in crack initiation timing as 

well as the rate of distress accumulation (after initiation) contribute to FWPC 

performance differences. 

Confidence interval data for FWPC are illustrated in figure 51. The contribution of deep 

sustained frost significantly outweighs the effect of multiple FTCs on accumulation of 

FWPC. This holds true in both the wet and dry regions. The no-freeze region exhibits the 

largest FWPC values which, statistically speaking, are larger than FWPC predictions in 

areas experiencing multiple FTCs (regardless of the amount of precipitation). 

-3

-2

-1

0

1

2

3

4

5

0 2 4 6 8 10 12 14 16 18 20

Pavement Age (years)

PREDICTED LN(FWPC+0.1) (% of affected length)

Deep-Freeze, Wet Region (low FTC)

Moderate-Freeze, Wet Region (high FTC)

No-Freeze, Wet Region

Deep-Freeze, Dry Region (low FTC)

Moderate-Freeze, Dry Region (high FTC)

Figure 50. Chart. Mean predicted flexible pavement FWPC values 

for each climatic region (BASE=DGAB/SG=FINE). 

The same general trends are apparent for different base/subgrade pavement structure 

combinations. Increased variability in some combinations results in performance 

differences to be less than the error band of the model. This is the case for 

ROCK/STONE subgrade and LCB base types in both pavement structure groups. To a 

lesser extent, variability for the NONE base type results in insignificant differences 

between the deep-frost regions and the multiple FTC climates. Predicted accumulation in 
81 

the no-freeze wet region is significantly larger than the multiple FTC climates (both 

precipitation levels). 

TRANSVERSE CRACKING SURFACE DISTRESS COMPARISONS FOR 

FLEXIBLE PAVEMENTS 

Figure 52 shows transverse cracking performance predictions for each climatic region in 

for DGAB base and FINE subgrade. Transverse cracking initiation ranges between 6 and 

12 years, initiating earlier in the deep-freeze regions that experience colder minimum 

temperatures in the winter months. Conversely, pavements in the no-freeze regions, with 

mild winter temperatures, initiate transverse cracking much later. 

3.0

3.2

3.4

3.6

3.8

4.0

4.2

4.4

4.6

4.8

5.0

Deep-Freeze, Wet

Region (low FTC)

Moderate-Freeze, Wet

Region (high FTC)

No-Freeze, Wet Region Deep-Freeze, Dry

Region (low FTC)

Moderate-Freeze, Dry

Region (high FTC)

PREDICTED LN(FWPC+0.1) (deduct value)

Figure 51. Bar chart. Predicted flexible pavement FWPC values at 20 years 

for each climatic region (BASE=DGAB/SG=FINE). 
82 

-3

-2

-1

0

1

2

3

4

5

0 2 4 6 8 10 12 14 16 18 20

Pavement Age (years)

PREDICTED LN(TC+0.1) (% of affected length)

Deep-Freeze, Wet Region (low FTC)

Moderate-Freeze, Wet Region (high FTC)

No-Freeze, Wet Region

Deep-Freeze, Dry Region (low FTC)

Moderate-Freeze, Dry Region (high FTC)

Figure 52. Scatter chart. Mean-predicted flexible pavement TC values 

for each climatic region (BASE=DGAB/SG=FINE) 

Figure 53 provides mean TC predictions accumulated at a pavement age of 20 years 

along with the upper and lower limits of the confidence interval. Pavements exposed to 

deep sustained frost penetration exhibit quantities of transverse cracking that are 

significantly larger than pavements in the multiple FTC climates. In addition, pavements 

exposed to climatic conditions that are representative of the southern reaches of the nofreeze region accumulate less transverse cracking (at 95 percent confidence) compared to 

areas with both deep sustained frost and multiple FTCs. 
83 

2.5

3.0

3.5

4.0

4.5

5.0

Deep-Freeze, Wet

Region (low FTC)

Moderate-Freeze, Wet

Region (high FTC)

No-Freeze, Wet Region Deep-Freeze, Dry

Region (low FTC)

Moderate-Freeze, Dry

Region (high FTC)

PREDICTED LN(TC+0.1) (deduct value)

Figure 53. Bar chart. Predicted flexible pavement TC values at 20 years 

for each climatic region (BASE=DGAB/SG=FINE). 

Changes in subgrade, base, and pavement structure types do alter the absolute magnitude 

of predicted TC. In some cases, the predicted TC deduct value reaches the equivalent of 

100 on a natural logarithmic scale at a pavement age less than 20 years. Because the 

model is predicted natural logarithm of TC plus 0.1, a deduct value of 100 corresponds to 

the natural logarithm of 100.1 or 4.606. For example, if the predicted TC value for both 

of the deep-freeze regions reaches this maximum at age 16 while the other climatic 

scenarios do not reach the maximum value within 20 years, comparing confidence 

intervals at age 20 for this case would not account for the regions that reached the 

maximum value 4 years earlier. The deep-freeze regions were capped at 4.606 for those 

4 years, while the other regions accumulated additional quantities of TC. To counter this, 

confidence intervals for all regions would be compared at the earliest age that one region 

reached the maximum value. For this example, age 16 would be used to make 

comparisons. 

In general terms, comparisons between the various climatic scenarios are similar to those 

made for DGAB base, FINE subgrade, and nonoverlay pavement structure types. Key 

exceptions are noted below: 

• Predictions for pavements with ROCK/STONE subgrade type are relatively 

variable and performance differences for these cases are confounded by the error 

within the model. This is also the case for LCB base types. 
84 

• For permeable asphalt-treated base (PATB) and no-base (NONE) base types 

(nonoverlay structures), deep frost (in both wet and dry climates) contributes to 

significantly larger accumulations of TC as compared with the multiple FTC 

climates and the no-freeze wet region. There is no significant difference between 

the multiple freeze-thaw climates and the no-freeze wet region. This is also true 

for overlay structures with DGAB/FINE, PATB/FINE, PATB/COARSE, 

ATB/FINE, NONBIT/FINE, NONBIT/COARSE, NONE/FINE, and 

NONE/COARSE combinations. 

• TC predictions on overlay pavement structures exhibit distress initiation at earlier 

pavement ages as compared with nonoverlay pavement structures. This is likely 

caused by cracking in the existing pavement layers reflecting through the overlay 

layers. 

• The observations in transverse cracking initiation as well as accumulation at 

20 years are logical in consideration of the driving mechanism of this distress 

type. Transverse cracking is largely a result of tensile stress that accrues as the 

pavement contracts as a result of low temperatures. Propagation occurs when the 

tensile stress exceeds the tensile strength of the asphalt mixture. In addition, the 

tensile strength of the mixture diminishes with each low temperature cycle. FI 

represents the duration and severity of the freezing season with higher values of 

the FI correlating to a longer duration or colder winter, or both (both of which 

contribute to transverse cracking propagation). Therefore, larger quantities of 

transverse cracking are expected in areas with a relatively high FI. 

LONGITUDINAL CRACKING SURFACE DISTRESS COMPARISONS FOR 

RIGID PAVEMENTS 

As mentioned previously, comparisons of rigid surface distress predictions were made at 

25 years because of crack initiation ages greater than 20 years. Figure 54 presents the 

mean predicted LC values for each climatic scenario. As can be seen, distress initiation 

occurred at 25 years in the wet no-freeze region. Figure 55 provides the confidence 

intervals at 25 years. 
85 

-2.5

-2

-1.5

-1

-0.5

0

0.5

0 5 10 15 20 25

Pavement Age (years)

PREDICTED LN(LC+0.1) (% of pavement area)

Deep-Freeze, Wet Region (low FTC)

Moderate-Freeze, Wet Region (high FTC)

No-Freeze, Wet Region

Deep-Freeze, Dry Region (low FTC)

Moderate-Freeze, Dry Region (high FTC)

Figure 54. Scatter graph. Mean predicted rigid pavement LC values 

for each climatic region (BASE=DGAB/SG=FINE). 

-2.4

-1.9

-1.4

-0.9

-0.4

0.1

0.6

1.1

1.6

Deep-Freeze, Wet

Region (low FTC)

Moderate-Freeze, Wet

Region (high FTC)

No-Freeze, Wet Region Deep-Freeze, Dry

Region (low FTC)

Moderate-Freeze, Dry

Region (high FTC)

PREDICTED LN(LC+0.1) (% of pavement area)

Figure 55. Bar chart. Predicted rigid pavement LC values at 25 years 

for each climatic region (BASE=DGAB/SG=FINE). 
86 

Comparing the deep-freeze region to the moderate-freeze region for both wet and dry 

climates shows no significant difference in LC performance. On the other hand, the nofreeze wet region at 25 years exhibits significantly lower LC accumulation than the other 

regions. 

The same observations can be made when evaluating longitudinal cracking for different 

subgrade and base combinations. 

TRANSVERSE CRACKING SURFACE DISTRESS COMPARISONS FOR RIGID 

PAVEMENTS 

Figure 56 shows transverse cracking predictions in for each of the climatic scenarios. 

Figure 57 shows confidence intervals. Crack initiation varies significantly with the 

moderate-freeze dry region initiating first and the deep-freeze wet region initiating 

11 years after construction. 

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

0 5 10 15 20 25

Pavement Age (years)

PREDICTED LN(TC+0.1) (% of pavement area)

Deep-Freeze, Wet Region (low FTC)

Moderate-Freeze, Wet Region (high FTC)

No-Freeze, Wet Region

Deep-Freeze, Dry Region (low FTC)

Moderate-Freeze, Dry Region (high FTC)

Figure 56. Scatter Graph. Mean predicted rigid pavement TC values 

for each climatic region (BASE=DGAB/SG=FINE). 
87 

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

Deep-Freeze, Wet

Region (low FTC)

Moderate-Freeze, Wet

Region (high FTC)

No-Freeze, Wet Region Deep-Freeze, Dry

Region (low FTC)

Moderate-Freeze, Dry

Region (high FTC)

PREDICTED LN(TC+0.1) (% of pavement area)

Figure 57. Bar chart. Predicted rigid pavement TC values at 25 years 

for each climatic region (BASE=DGAB/SG=FINE). 

For wet environments, the contribution of deep frost and multiple FTCs did not result in 

significant transverse crack accumulation differences as compared to the no-freeze 

region. In addition, transverse cracking differences between the deep-freeze and 

moderate-freeze climates in dry environments are insignificant; however, the effect of the 

wet no-freeze region resulted in significantly larger quantities of transverse cracking than 

the deep-freeze dry region at 25 years. 

These observations hold true for the majority of base/subgrade combinations. Rigid 

pavement structures with PATB and asphalt-treated base (ATB) type categories exhibit 

considerable variability resulting in all performance differences to be insignificant and 

confounded by model error. 

TRANSVERSE JOINT FAULTING COMPARISONS FOR RIGID PAVEMENTS 

Mean transverse joint faulting predictions as a function of age are shown in figure 58 for 

each climatic scenario. The models used to develop the predictions were based on 

average faulting over the entire test section; therefore, the model does not predict faulting 

for one joint but rather for the average for a pavement segment. 

Figure 59 provides confidence interval data at a pavement age of 20 years. Faulting in 

climates with considerable amounts of annual precipitation is not significantly different 

between any of the frost settings. 
88 

In drier climates, deep-frost penetration contributes to an increased accumulation of 

faulting as compared with the multiple FTC and no-freeze wet regions. 

The same observations can be made for the majority of the base/subgrade combinations. 

The variability inherent in all combinations with ROCK/STONE subgrade is relatively 

high, which confounds the differences in predicted performance. For structures with ATB 

and NONBIT, deep frost (in the dry climate) contributes to significantly larger 

accumulations of faulting than the multiple freeze-thaw (dry) climate at a pavement age 

of 20 years; however, predictions in these climates are not statistically different than the 

no-freeze wet region. 

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 2 4 6 8 10 12 14 16 18 20

Pavement Age (years)

PREDICTED LN(FLT+1) (mm)

Deep-Freeze, Wet Region (low FTC)

Moderate-Freeze, Wet Region (high FTC)

No-Freeze, Wet Region

Deep-Freeze, Dry Region (low FTC)

Moderate-Freeze, Dry Region (high FTC)

1 mm = 0.04 inch

Figure 58. Scatter chart. Mean predicted rigid pavement FLT values 

for each climatic region (BASE=DGAB/SG=FINE). 
89 

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Deep-Freeze, Wet

Region (low FTC)

Moderate-Freeze, Wet

Region (high FTC)

No-Freeze, Wet Region Deep-Freeze, Dry

Region (low FTC)

Moderate-Freeze, Dry

Region (high FTC)

PREDICTED LN(FLT+1) (mm)

1 mm = 0.04 inch

Figure 59. Bar chart. Predicted rigid pavement FLT values at 20 years 

for each climatic region (BASE=DGAB/SG=FINE). 

91 

7. INDEPTH AGENCY COMPARISONS 

To further the investigation of frost penetration and multiple FTCs, an indepth 

comparison of predicted performance was conducted for all of the States contributing to 

the pooled fund study. Information on the environment for every test section used in the 

analysis dataset and located in one of the PFS is provided in table 23. The test sections 

highlighted in the table were selected as the representative climate conditions for the 

respective agency. These values were used along with constant nonenvironmental factors, 

shown in table 20, to predict flexible pavement performance. 

Table 23. Measured environmental data for LTPP sites. 

State Site FTC FI CI PRECIP 

Alaska 1002 112 793 0 2020 

Alaska 1001 124 890 0 1116 

Alaska 1004 87 1110 2 420 

Alaska 6010 92 1248 1 413 

Alaska 9035 106 1513 3 729 

Alaska 1008 74 2584 23 317 

Alaska average 99 1356 5 836 

Idaho 1001 113 217 158 693 

Idaho 1005 121 399 339 627 

Idaho 1007 121 326 271 254 

Idaho 1009 137 351 221 262 

Idaho 1010 136 665 144 303 

Idaho 1020 128 328 331 280 

Idaho 1021 129 622 149 342 

Idaho 3017 125 356 344 342 

Idaho 3023 107 278 400 295 

Idaho 6027 162 817 57 380 

Idaho 9032 111 259 121 718 

Idaho 9034 117 316 89 807 

Idaho average 127 432 213 398 

Illinois 1003 79 212 742 1052 

Illinois 6050 78 239 747 995 

Illinois 1002 96 652 383 871 

Illinois average 84 368 624 973 
92 

Table 23. Measured environmental data for LTPP sites (continued). 

State Site FTC FI CI PRECIP

Indiana 1028 80 217 696 1209 

Indiana 1037 73 152 821 1186 

Indiana 2008 84 399 471 963 

Indiana 3002 84 451 520 950 

Indiana 3003 88 454 453 1005 

Indiana 3030 88 386 476 1017 

Indiana 3031 70 230 772 1165 

Indiana 6012 71 215 798 1165 

Indiana average 80 313.1 625.9 1082.5 

Michigan 0200 86 382 443 866 

Michigan 0100 105 510 324 870 

Michigan 1010 91 532 309 825 

Michigan 1013 109 568 251 915 

Michigan 1012 115 612 230 931 

Michigan 3068 109 670 215 827 

Michigan 1001 109 759 174 790 

Michigan 6016 100 787 173 751 

Michigan 1004 77 960 131 878 

Michigan average 100 642 250 850 

New York 0800 87 437 319 891 

New York 1011 90 505 298 1007 

New York 1008 87 582 257 1133 

New York 1643 99 618 270 1006 

New York 1644 109 990 111 1110 

New York average 94 627 251 1029 

North Carolina 0800 47 14 998 1343 

North Carolina 1645 57 19 975 1260 

North Carolina 1030 50 24 951 1192 

North Carolina 2825 52 28 965 1093 

North Carolina 1352 69 32 876 1232 

North Carolina 1028 54 32 870 1186 

North Carolina 3008 67 33 815 1212 

North Carolina 1006 60 35 887 1153 

North Carolina 2819 69 43 799 1139 

North Carolina 3816 71 44 830 1141 

North Carolina 3011 71 45 867 1151 

North Carolina 3807 77 46 772 1142 

North Carolina 1817 74 46 779 1126 

North Carolina 0200 83 47 773 1151 

North Carolina 3044 77 49 784 1161 

North Carolina 1992 73 53 779 1221 

North Carolina 2824 73 54 780 1224 
93 

Table 23. Measured environmental data for LTPP sites (continued). 

State Site FTC FI CI PRECIP

North Carolina 1802 82 58 775 1110 

North Carolina 1024 93 59 475 1308 

North Carolina 1814 100 70 446 1477 

North Carolina 1803 107 86 407 1371 

North Carolina 1801 88 116 323 1205 

North Carolina 1040 109 141 218 1441 

North Carolina average 74 51 745 1219 

Ohio 3801 89 249 480 1037 

Ohio 3013 89 250 533 1140 

Ohio 0100 96 375 414 972 

Ohio 0200 96 375 414 972 

Ohio 0800 96 375 414 972 

Ohio average 93 325 451 1018 

Pennsylvania 3044 99 269 433 1164 

Pennsylvania 1623 95 309 415 1004 

Pennsylvania 1608 107 311 334 944 

Pennsylvania 1605 107 331 339 1092 

Pennsylvania 1618 100 343 270 1028 

Pennsylvania 1597 111 514 196 865 

Pennsylvania 1599 124 546 141 1121 

Pennsylvania average 106 375 304 1031 

Note: Shaded cells denote climate selected as representative for agency. 

Figure 60 shows pavement roughness predictions. In comparing these results with the 

confidence interval results in figures 44 and 45, it appears that Alaska’s climate 

contributes to larger accumulations of pavement roughness compared to the other 

agencies; however, because the differences in performance are relatively small, it is 

doubtful that they are significant. 
94 

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2.0

0 2 4 6 8 10 12 14 16 18 20

AGE (years)

PREDICTED IRI (m/Km)

Alaska

Idaho

Michigan

Illinois

Ohio

New York

Pennsylvania

North Carolina

Indiana

1 m/km = 5.28 ft/mi 

Figure 60. Scatter chart. Flexible pavement IRI for selected sites 

in each agency. 

Figure 61 lists rutting performance predictions for each of the agency’s representative 

climates. The confidence interval information in figures 48 and 49 can be used to 

determine the significance of the predicted differences between the participating 

agencies. The most obvious observation is the small accumulation of rutting predicted in 

Alaska and North Carolina in comparison with all other PFS, which appears to be 

significantly less than the climates in Idaho, Pennsylvania, and New York. All other 

variations in rutting performance do not appear to be significant. 

While the climate in Alaska contributes to minimal amounts of rutting, the tradeoff is 

evident in the development of transverse cracking. From figure 62, transverse cracking in 

Alaska initiates very early in the pavement life and reaches a maximum deduct value at 

age 16 years. This is the likely source of increased pavement roughness values predicted 

for Alaska in figure 60. Transverse cracking predictions for North Carolina are 

significantly smaller than all of the other agencies. Climates in Illinois and Indiana 

exhibit transverse cracking that is very similar, and the contribution of these climates 

results in significantly smaller amounts of transverse cracking compared to Michigan and 

New York. Predictions for Idaho, Ohio, and Pennsylvania fall in between the 

Illinois/Indiana and Michigan/New York climates; however, the differences are 

insignificant, and they may be the result of error within the regression models. 
95 

Last, figure 63 provides fatigue and wheelpath cracking predictions for each of the 

respective agency climates. Predicted cracking in the New York and Indiana regions are 

slightly less than the other agencies. These differences, however, are not significant. 

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

0 2 4 6 8 10 12 14 16 18 20

AGE (years)

PREDICTED RUT (mm)

Alaska

Idaho

Michigan

Illinois

Ohio

New York

Pennsylvania

North Carolina

Indiana

 1 mm = -0.04 inch 

Figure 61. Scatter chart. Flexible pavement RUT for selected sites 

in each agency. 
96 

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16 18 20

AGE (years)

PREDICTED TC (deduct value)

Alaska

Idaho

Michigan

Illinois

Ohio

New York

Pennsylvania

North Carolina

Indiana

Figure 62. Scatter chart. Flexible pavement TC for selected sites 

in each agency. 

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16 18 20

AGE (years)

PREDICTED FWPC (deduct value)

Alaska

Idaho

Michigan

Illinois

Ohio

New York

Pennsylvania

North Carolina

Indiana

Figure 63. Scatter graph. Flexible pavement FWPC for selected sites 

in each agency. 
97 

It should be noted that these comparisons were based on the environmental conditions 

observed at one representative location within the agency. It is well known that climates 

can vary drastically from one location to the next within an agency and a so-called 

representative climate does not accurately describe climatic conditions for all areas of the 

agency. As such, an evaluation of regional environments was conducted for each 

participating state. 

Figures 83 through 91 in appendix C show State maps containing the geographic location 

of each test section used in the study. Tables below each of the figures contain 

environmental and pavement structure information (GPS test sections only) 

corresponding to the test sections. Test sections in each agency were grouped based on 

FI. Some States exhibit climates that fall within the different climatic zones established 

for the analysis (i.e., deep-freeze, moderate-freeze, and no-freeze). Test sections were 

grouped based on climatic zone for these cases. In other States, all climates were 

characteristic of one of the defined zones; however, the test sections were divided where 

large differences in FI were noted. For example, all test sections in New York fall into the 

deep-freeze region, but the FI at site 1644 is considerably higher; therefore, this site was 

separated from the average of the remaining sites in New York. 

Some of the largest climatic variations occur in Idaho and Michigan. To highlight the 

differences in performance across one state, environmental factors from two sites in each 

of these States were used, along with the standard nonenvironmental factors in table 19, 

to predict performance. In Idaho sites 1001 and 6027 were selected because they are at 

the two climatic extremes in the State. Similarly, sites 0200 and 1004 were selected from 

Michigan. 

Figure 64 provides FWPC predictions for the Idaho sites. Site 1001 exhibits a predicted 

deduct value of more than 2.5 times larger than the prediction for site 6027 at 20 years. 

This is a significant difference. It is interesting to note that site 1001 has lower FI and 

FTC values in comparison with site 6027. However, site 1001 is in the wet region while 

site 6027 is in the dry region. In addition, site 6027 has a larger CI than site 1001. 

TC predictions for the two sites in Michigan can be found in figure 65. Predictions for 

site 1004 reach a maximum level of 100, while the predictions for the environment at site 

0200 are at approximately 50. The environment at site 1004 has a very large FI value in 

comparison to site 0200. Conversely, CI is smaller at site 1004 than at site 0200. Both are 

in the wet region and experience similar amount of FTCs (77 and 86 for site 1004 and 

0200, respectively). 

These examples highlight the regional differences in performance that can be expected in 

one State given a constant pavement structure and traffic loading conditions. 
98 

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16 18 20

AGE (years)

PREDICTED FWPC (Deduct Value)

1001

6027

Figure 64. Scatter graph. FWPC predictions for sites 1001 and 6027 in Idaho. 

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16 18 20

AGE (years)

PREDICTED TC (deduct value)

0200

1004

Figure 65. Scatter graph. Flexible TC predictions for the environments 

at sites 0200 and 1004 in Michigan. 
99 

8. LOCAL ADAPTATIONS OF EMPIRICAL PAVEMENT DESIGN PRACTICES 

AND MATERIALS STANDARDS 

One of the primary study areas requested in the proposal was for the “Analysis of the 

extent to which local adaptations of materials standards and empirical pavement design 

practices have been effective at reducing the rate of pavement deterioration.” 

To gain an understanding of State design practices, a questionnaire was developed and sent 

to the PFS participants. The complete questionnaire is provided in appendix D. Basic 

information on standard roadway sections including structural design for given scenarios, 

standard specifications, and test procedures was requested. Questions regarding average 

unit bid prices and typical service life estimates were included for purposes of LCCA. A 

query on the knowledge of current treatment practices implemented by adjacent SHAs was 

also incorporated. 

The pavement sections or specific pavement designs provided by the States are shown in 

tables 24 through 27. 

Table 24. Primary highway flexible pavement design summary. 

States 

AC 

(mm 

(inch)) 

Treated 

Base 

(mm 

(inch)) 

Untreated 

Base 

(mm 

(inch)) 

Total 

Depth 

(mm 

(inch)) 

Treated SG 

(mm (inch)) 

Total FFa

Depth 

(mm 

(inch)) 

Alaska 

130 

(5) NA 480 (19) 710 (24) 

915 (36) 

select 1270 (50) 

Idaho 

165 

(6.6) NA 305 (12) 470 (18.6) NA 470 (18.6)

Illinois 

355 

(14) NA 0 (0) 355 (14) 

305 (12) 

lime 660 (26) 

Michigan 

170 

(6.5) NA 710 (24) 775 (30.5) var. gr. surf 

1065-

1525 (42– 

60) 

New York 

150 

(6) 

100 (4) 

ATPB 305 (12) 560 (22) NA 560 (22) 

North 

Carolina 

265 

(10.5) NA 255 (10) NA NA 520 (20.5)

Ohio 

203 

(8) NA 150 (6) 355 (14) 

no A-4b for 

915 (36) 1270 (50) 

Pennsylvania 

445 

(17.5) NA 0 (0) 445 (17.5) NA 445 (17.5) a

FF=frost-free. 

Design conditions: 30-year design; 5 million ESALs; frost-susceptible fine-grained soil with 

resilient modulus of 68,950 kilopascals (kPa) (10,000 pounds of force per square inch (lbf/in2)). 
100 

Table 25. Primary highway rigid pavement design summary. 

States 

PCC 

(mm 

(inch)) 

Treated 

Base 

(mm 

(inch)) 

Untreated 

Base 

(mm 

(inch)) 

Treated SG 

(mm (inch)) 

Total FFa

Depth 

(mm (inch))

Alaska NA NA NA NA NA 

Idaho 

230 

(9) 

50 (2) 

ATPLC 305 (12) NA 585 (23) 

Illinois 

250 

(9.75) 100 (4) NA 305 (12) lime 655 (25.75) 

Michigan 

215 

(8.5) 

150 (6) 

UTB 255 (10) var. gran. mat 

1065-1525 

(42–60) 

New York 

255 

(10) 

100 (4) 

ATPB 305 (12) NA 660 (26) 

North Carolina 

205 

(8) 115 (4.5) NA 205 (8) lime 520 (20.5) 

Ohio 

205 

(8) NA 152 (6) 

no A-4b for 

915 (36) 1270 (50) 

Pennsylvania 

205 

(8) NA 205 (8) NA 405 (16) a

FF=frost free 

Design conditions: 30-year design; 5 million ESALs; frost-susceptible, fine-grained soil with 

resilient modulus of 68,950 kPa (10,000 lbf/in2

). 

Table 26. Interstate highway flexible pavement design summary. 

States 

AC 

(mm 

(inch)) 

Treated

Base 

(mm 

(inch)) 

Untreated

Base 

(mm 

(inch)) 

Treated 

Subgrade 

(mm (inch)) 

Total 

Depth 

(mm 

(inch)) 

Alaska NA NA NA NA NA 

Idaho 200 (7.8) NA 710 (24) NA 810 (31.8) 

Illinois 515 (20.25) NA 0 (0) 305 (12) lime 820 (32.25)

Michigan 185 (7.25) NA 710 (24) 

1065 (42) 

granular 

1860 

(73.25) 

New York 180 (7) 

100 (4) 

ATPB 305 (12) NA 585 (23) 

North 

Carolina 405 (16) NA 255 (10) NA 660 (26) 

Ohio 290 (11.5) NA 150 (6) 

no silt for 915 

(36) 1360 (53.5)

Pennsylvania 420 (16.5) NA 255 (10) NA 675 (26.5) 

Design conditions: 30-year design; 10 million ESALs; frost-susceptible fine-grained soil with 

resilient modulus of 68,950 kPa (10,000 lbf/in2

).. 
101 

Table 27. Interstate highway rigid pavement design summary. 

States 

PCC 

(mm 

(inch)) 

Treated 

Base 

(mm 

(inch)) 

Untreated

Base 

(mm 

(inch)) 

Treated 

Subgrade 

(mm (inch)) 

Total 

Depth 

(mm 

(inch)) 

Alaska NA NA NA NA NA 

Idaho 305 (12) 

50 (2) 

ATPLC 305 (12) NA 660 (26) 

Illinois 

265 

(10.5) 100 (4) 0 (0) 305 (12) lime 675 (26.5) 

Michigan 

290 

(11.5) NA 405 (16) 

1065 (42) 

granular 1765 (69.5)

New York 255 (10) 

100 (4) 

ATPB 305 (12) NA 660 (26) 

North 

Carolina 280 (11) 115 (4.5) 0 (0) 205 (8) lime 600 (23.5) 

Ohio 

290 

(11.5) NA 150 (6) 

no silt for 915 

(36) 1360 (53.5)

Pennsylvania 330 (13) 

100 (4) 

ATPB 100 (4) NA 535 (21) 

Design conditions: 30-year design; 10 million ESALs; frost-susceptible fine-grained soil with 

resilient modulus of 68,950 kPa (10,000 lbf/in2

). 

LOCAL ADAPTATIONS OF PAVEMENT DESIGN PRACTICES 

As can be seen from these tables, there was a large variation in the roadway design 

sections reported for each SHA. There was no specific trend in the pavement designs 

between those SHAs that experience deep frost and those that experience moderate to no 

frost penetration. No SHA reported any specific design thickness requirement based on 

frost depth. One SHA, Pennsylvania, noted design consideration for frost heave in 

accordance with the 1993 AASHTO Design Guide. In particular, the design is based on 

the loss in serviceability because of frost heave as described in appendix G of the 1993 

AASHTO Guide for Design of Pavement Structures.

(1)

All of the SHAs provided a pavement design based on the subgrade soils resilient 

modulus (MR) value noted in the questionnaire without accounting for frost effects; 

however, one SHA does not design pavement structures for MR values greater than 

41,370 kPa (6,000 lbf/in2

). This may reflect either the weaker soils that are encountered 

in the State or a method to partially account for thaw weakening in the environment of 

the State. Two SHAs indicated that their standard construction procedures require that 

frost-susceptible subgrade soils be removed for a depth of 1m (3 ft) or more. This 

additional provision is shown as treated subgrade in tables 24 through 26 (Michigan and 

Ohio). 

Alaska traditionally places roadways on embankment sections that consist of a minimum 

height of 1m (3 ft) of select frost-free material. This practice provides for a minimum of 
102 

1 m (3 ft) of frost-free material, in addition to the normal pavement design thickness, to 

protect the pavement against weakening from frost heave and thaw. 

To be consistent with the pavement performance indicated from the LTPP database, 

Idaho reported design sections that generally represent design procedures that were in 

place during the development of the LTPP test sections. The SHA is now incorporating a 

rock cap in much of its new construction that consists of 0.3 to 0.6 m (1 to 2 ft) of rock 

ballast placed between the subgrade and the surfacing layers, which has been shown to 

provide long-term benefits in the reduction of frost and moisture-related pavement 

damage. Nordic countries such as Sweden typically use a similar ballast layer that is 

usually placed at depths of a little more than 1 m (3 ft).(17) Figure 66 provides a 

photograph of a deep-base section in Sweden. 

In earlier studies, many northern SHAs were found to require a minimum depth of frostfree material ranging from 50 to 100 percent of the maximum measured frost depth(4) for 

the specific design area. SHAs such as Utah require 100 percent of the measured frost 

depth, which ranges from 0.6 m (2 ft) to more than 1.2 m (4 ft) in that region. The recent 

reconstruction of I-15 through Salt LakeCity used a 330-mm (13-inch) thick PCC over a 

205-mm (8-inch) open graded drainage layer with an additional 380 mm (15 inches) of 

granular subbase to provide for the required 915 mm (36 inches) of frost measured in the 

area.(18) 

The effect of these treatments is difficult to quantify because the treatments may or may 

not be incorporated into the existing LTPP sites depending on individual SHA practice. 

This may be accounted for in the models developed from the LTPP database because 

these practices are part of the roadway sections in the GPS experiment. The consultant 

did not directly separate sites with extra surfacing depths based on individual agency 

policy to either subexcavate frost-susceptible soils or add additional granular base based 

on local frost depths from the remaining sites. To a limited extent, additional surfacing 

beyond normal AASHTO design depths is represented and accounted for in the LESN 

term that was found to have a significant contribution as an explanatory variable in the 

models developed in this research study. 

In general, it appears that many SHAs deal with greater frost depths by adding additional 

granular surfacing to reduce or prevent frost heaving. Some SHAs accomplish this by 

replacing the frost-susceptible subgrade with an acceptable frost-free material at fairly 

specific depths or at variable depths depending on the environmental range in that State. 

Other SHAs increase the pavement depth using additional gravel surfacing consistent 

with measured frost depths. Not all SHAs follow these practices, and the LTPP database 

incorporates pavement deterioration trends from all SHAs including northern SHAs that 

increase the surfacing and those who do not. 

Many SHAs increase the depth of frost-free material over that required by the AASHTO 

Guide for Design of Pavement Structures (1) to minimize frost heaving effects where 

significant frost depths occur. The literature review and SHA research review did not find 

any specific examples where a difference in pavement performance was quantified 

relative to increased surfacing depths or removal of frost-susceptible soils. Considering 
103 

the prevalence of the practice across the northern tier SHAs, it appears to be a general, 

but not universal, practice. 

For years, Washington has minimized frost heaving effects and thaw weakening on lowvolume roadways by adding gravel surfacing beyond that designated by the AASHTO 

pavement design procedure for frost depth. The use of additional surfacing significantly 

increased the service life of the roadway and minimized or eliminated the need for spring 

load restrictions to protect the roadway. Washington State, as part of its Highway System 

Plan, includes an Economic Initiative Strategy and subprogram for “All Weather 

Roadways (Freeze/Thaw),” which is directed to improving low-volume roadways that 

should be open throughout the winter for the transportation of goods and services.(19) That 

program consists largely of rebuilding low-volume roadways by increasing the surfacing 

depth to minimize the effect of frost heaving and thaw weakening. The ultimate goal is to 

minimize or eliminate the need for spring load closures to protect the roadway, which 

often causes an economic hardship to the local community. That program has been quite 

successful. 

Figure 66. Photo. Road construction in Sweden with deep base section. 

There was a very limited amount of literature available from the participating and 

adjacent SHAs regarding frost penetration and FTC mitigation. A study conducted for 

Ohio found that overlays in areas with high snowfall deteriorate faster than those in other 

areas.(20) Two studies performed in Alaska considered differential thaw settlement when 

considering pavement structural performance and the statewide pavement management 

system.(21,22) Soil characteristics were investigated and empirically linked to pavement 

performance. 

One source of information on frost heaving and thaw weakening was the WSDOT 

Pavement Guide, which includes a description of frost action as well as several reviews 

of other SHA practices.(4) It provides a general description of frost heaving and thaw 
104 

weakening as well as a discussion on other SHA practices including added surfacing 

based on depth of frost. Drainage and the use of capillary blankets are also addressed. 

In special cases, such as in the Western States and Northeastern States, where rolling to 

mountainous terrain are present, moisture enters the roadway prism flowing along lateral 

soils deposits. This happens in varied silt and sand layers or on top of rock contact zones. 

Washington State has this terrain, where much of the moisture enters the roadway prism 

laterally instead of vertically from capillary tension, which also produces ice lenses and 

causes frost heaving. WSDOT has significantly reduced frost heaving in these areas 

through the use of longitudinal drainage where the water can be intercepted before it 

enters the roadway prism, as shown in the photograph in figure 67. Most of the worst 

frost heaving areas in the State highway system exists in the northeastern slopes of the 

Cascade Mountain Range. This has been corrected either by digging out and increasing 

the surfacing depths, adding more surfacing depth, or installing longitudinal drainage 

along the inslope of the ditch line.(23) 

Similar treatments have been used by the New York State Department of Transportation 

(NYSDOT).(24) In addition, NYSDOT has required the use of rock subgrade 

fragmentation where the rock is fragmented for a depth of .9 to 1.2 meters (3 to 4 ft) 

below subgrade during rock excavation to provide adequate drainage in areas of rock 

cuts. 

Figure 67. Photo. Installation of longitudinal drainage to reduce frost heaving. 

The photograph in figure 67 shows the installation of a longitudinal drain in the in-slope 

of the ditch line. A geotechnical study revealed that considerable moisture was entering 

the roadway prism from lateral flow in and below the cut slope, which caused the frost 
105 

heaving in the limits of the cut section. Longitudinal drains were installed to intercept this 

water and reduce or eliminate the frost heaving. It should be noted that this application 

was successful but its potential application is limited by the nature of the area and local 

environment. In this particular case, the frost penetration is approximately 1 m (3 ft) 

deep, and the ditch line is covered with snow through most of the winter. This 

significantly reduces the frost depth along the ditch line and does not allow the drain line 

to freeze. 

There was, for a period, some application of foam board to reduce or eliminate frost 

heaving. There were several installations across the country from Colorado to Maine as 

well as in Canada and the Nordic countries.(25) The use of foam provided favorable 

results as far as reducing frost heaving; however, several States experienced a problem 

with the roadway frosting in the area where the foam was used. There is currently very 

little or no use of foam insulation for this application. 

Maintaining the same frost-free surfacing depth throughout the roadway prism is an 

additional design consideration to reduce differential frost heave. This was not mentioned 

in the SHA responses to the questionnaire, but it was recognized in the literature of one 

adjacent SHA as having contributed to shoulder heave,(26) which resulted in early distress 

initiation. This appears to be a treatment required more by Western States than those in 

the Midwest or East. 

Most western SHAs maintain the same depth of frost-free surfacing, be it bound 

pavement or untreated surfacing, throughout the entire roadway prism. This is usually 

done to prevent differential frost heaving over the full width of the roadway section. 

Some of the roadway sections that were provided in response to the questionnaire 

indicated that the pavement section would not have been designed with a uniform depth 

of frost-free material across the full roadway prism. In States where a significant depth of 

frost-susceptible subgrade is removed and replaced (such as in Michigan and Ohio), there 

would be no frost-susceptible material within 1 m (3 ft) or more of the subgrade surface. 

In other States where frost-susceptible soils can be placed in the roadway prism, such as 

that shown in figure 68, there is a potential for differential frost heaving across the 

roadway prism. 
106 

 1 inch = 2.54 cm; 1 ft = 0.3 m 

Figure 68. Diagram. Standard pavement section from a Midwestern State. 

Using a uniform depth of frost-free material across the entire roadway prism appears to 

represent standard practice by some SHAs, but it may not represent universal or even 

general practice. The amount of differential frost heaving that would be experienced in a 

roadway section such as that shown in figure 68 is likely dependent on the environment 

where it is used. In milder environments, where the freezing front advances slowly 

through the upper portion of the roadway prism, the amount of moisture that is brought 

into the frost-susceptible material in the outer edges of the prism might be enough to 

cause greater heaving in that area, causing differential heaving of the roadway prism. On 

the other hand, a rapidly advancing freezing front in a more extreme climate may plunge 

below the surfacing layers quickly enough to minimize any increase in moisture and 

heaving in the outer sections of the roadway prism. SHAs experiencing differential 

heaving of the outer edges of the roadway prism could benefit by investigating the use of 

uniform depths of frost-free material across the entire roadway section. 

Applying chip seals on hot-mix asphalt pavements is standard practice in many northern 

tier SHAs. Idaho, Montana, Wyoming, North Dakota, and South Dakota chip seal hotmix pavements just after construction, and continue chip seal applications at 6 to 8 year 

intervals until the next resurfacing project, which might be 25 years or more after initial 

construction.(10)

A limited amount of research is available that addresses the use of chip seals to improve 

pavement performance in deep frost areas. North Dakota has documented the used of 

sand and chip seals to protect pavements from FTCs and expansive soil problems.(10) It is 
107 

quite likely that the use of chip seals on hot-mix asphalt pavement reduces the amount of 

surface raveling in areas with deep frost penetration and numerous FTCs. While the 

treatment appears to provide some protection against raveling, the relative advantage in 

improving pavement performance was not quantified. It should be noted that the use of 

chip seals on hot-mix asphalt pavement was confined to SHAs that generally have lower 

traffic volumes, where the application of chip seals are usually more acceptable. 

LOCAL ADAPTATIONS OF MATERIAL STANDARDS 

The responses from SHAs on material standards appear in appendix E, as well as in data 

gathered from standard specifications available on individual State Web sites. 

Most SHAs indicated that they have adopted the use of the Superpave PG binder 

specification and the Superpave mix design procedures. An abbreviated summary of the 

responses to the questionnaire and the review of standard specifications on the web sites 

are shown in table 28. 

Table 28. Hot-mix asphalt concrete binder grading and mix designs used 

by the PFS for surfacing courses. 

State HMAC Reference AC Grade Mix Design 

Alaska 

Hot-Mix Asphalt (HMA), 

Type II, Class B PG 58/64–28 Marshall 

Idaho Plant Mix Pavement PG 64–28 Marshall 

Illinois SP HMA Surface Course PG 58/64–22 Superpave/Marshall 

Michigan Gap Graded SP PG 70–22P Superpave 

New York 

12.5 mm Superpave 

HMA 

PG 64–28/PG 70–

22 Superpave 

North 

Carolina S-12.5C PG 70–22 Superpave 

Ohio Item 880 (7 yr warranty) 

PG 64–22/PG 70–

22M Superpave/Marshall 

Pennsylvania 

Superpave HMA Wearing 

Cr 

PG 64–22/PG 58–

22 Superpave/Marshall 

Appendix F contains the full set of specification comparisons for wearing course and 

base course, as well as treated and untreated surfacing. An overview of standard design 

practices for each SHA is also provided in the appendix. 

Because cold weather performance was a major consideration in the development of the 

Superpave binder specifications, it is logical that their use will lead to improved 

performance; however, the use of Superpave mix design procedures has, to a large extent, 

eliminated local adaptations in mix design procedures and specifications that might have 

provided improved performance in areas of deep frost penetration and numerous FTCs. 

The Superpave mix design procedure does not differentiate between mix designs where 

mixes will be exposed to numerous FTCs and those that will experience little or no FTCs. 
108 

A review of mix specifications shows there are still some local adaptations in acceptable 

Los Angeles (L.A.) wear values (35 to 55 maximum) and sulfide soundness values (9 to 

18 percent maximum). These differences probably represent material availability issues 

more than environmental issues in the respective SHAs. In addition, minor differences 

exist in the requirements for antistrip agents. Most agencies required a minimum retained 

strength of 70 to 80 percent after Lottman conditioning of the test samples to eliminate 

the need to add antistrip to the mix design. North Carolina requires a minimum of 85 

percent of the retained strength after a modified Lottman conditioning. The modified test 

procedure excludes freezing the sample before the tensile strength ratio (TSR) tests were 

run. That requirement may not be as demanding as a 70-percent requirement based on the 

normal Lottman conditioning (with the FTC). It is interesting to note that North Carolina 

also has the mildest environment as far as frost depth and freeze-thaw cycling is 

concerned when compared with the other SHAs evaluated in the study. As such, SHAs 

with more aggressive winter climates should not consider the same modification to 

antistrip requirements. 

Because many SHAs are in the process of adopting the Superpave binder specifications 

as well as the mix design procedures, local adaptations in mix designs and specifications 

do not indicate improved pavement performance in areas with either deep frost 

penetration or numerous FTCs. Many other SHA specifications were reviewed to 

determine if differences in grading and density requirements existed between the more 

northern SHAs as compared to the more southern SHAs. Most agencies showed control 

points and density requirements similar to those contained in the Superpave mix 

guidelines. Some SHAs have maintained their grading requirements, but there was no 

consistent pattern to lead to strong conclusions. 
109 

9. COST CONSIDERATION 

An additional objective of the study involved evaluating the costs associated with 

performance in the various environments. Cost differences associated with maintaining 

pavements in deep frost or multiple freeze-thaw climates relative to costs in other areas 

(i.e., no-freeze) were of particular interest. The following excerpt was taken from the 

problem statement for this study: 

“Determination of the cost associated with building and maintaining similar 

pavements to equal performance standards in various freeze to no-freeze climatic 

region...These costs should highlight any changes in material quality cost for new 

construction and life cycle cost associated with rehabilitation needed to maintain 

the pavement at similar levels of service.”

LCCA was used to evaluate pavement costs in the various climatic settings because it 

produces comparable results (i.e., equivalent uniform annual costs). Comparisons were 

made using both deterministic and probabilistic LCCA methods. The deterministic method 

does not account for variation inherent in the inputs, and therefore, it does not provide 

information on the distribution of the resulting annualized costs. Because only mean input 

values are used in the deterministic approach, the results are in terms of mean values with 

no indication of variability. Probabilistic analysis does incorporate the variation of the 

inputs and provides distribution statistics of the resulting annualized costs. Realcost 

version 2.2 was used to conduct the probabilistic analysis. Distributions of construction 

cost and treatment timings were modeled as a triangle distribution, which required 

maximum, minimum, and most likely values as inputs. The maximum and minimum unit 

costs received from the PFS were used to develop the maximum and minimum distribution 

inputs for construction cost. The average unit costs were used as the most likely value. For 

the treatment timing distributions, the upper and lower 95-percent confidence intervals 

were used to determine the maximum and minimum inputs, respectively, while the mean 

prediction was used as the most likely value. 

Annualized and present worth costs for maintaining pavements over a 30-year period were 

used in the following comparisons. Preventive maintenance activities were assumed to be 

consistent for all regions, and they were not included in the analysis. It should be noted 

that some northern SHAs have implemented the routine application of chip seals to 

pavement sections to mitigate freeze-thaw deterioration. Chip seals are not represented in 

the performance models; therefore, the contribution to the reduction in deterioration cannot 

be incorporated in the LCCA, and they were not included in the cost analysis. User costs 

were assumed to be constant, and they were not included in the analysis. The models 

developed in this study were used to predict pavement performance for typical roadway 

sections in the following five region climates: 

• Deep-freeze wet (low FTC). 

• Deep-freeze dry (low FTC). 

• Moderate-freeze wet (high FTC). 
110 

• Moderate-freeze dry (high FTC). 

• No-freeze wet region. 

The performance trends for both new and overlay flexible pavements were determined 

for fatigue cracking, transverse cracking, rut depth, and ride. Similarly, the performance 

trends for rigid pavement designs were determined for longitudinal cracking, transverse 

cracking, faulting, and ride. 

Performance trends were evaluated only for pavement aged less than 30 years, so that the 

inference space of the data used to develop the models was not exceeded. For the rigid 

pavement designs, the performance trends indicated that no specific damage category 

reached an action level before 30 years. Because any performance prediction beyond 

30 years would greatly exceed the inference range, it was decided not to continue with 

the economic analysis of the rigid pavements. 

Standard flexible pavement sections were developed based on the standard 1993 

AASHTO Guide for Design of Pavement Structures design procedures(1) using the input 

variables contained in the questionnaire. One cost comparison was performed using this 

standard section for all environmental zones; therefore, the initial and rehabilitation costs 

were constant for all regions. Cost differences were the result of treatment timing 

differences because of performance differences between the regions. The resulting 

roadway sections were similar to the average pavement sections provided by the PFS. 

To account for local adaptations used to mitigate damage associated with freezing and 

thawing climates, an additional cost evaluation was performed in which the initial costs 

of the deep- and moderate-freeze regions included additional frost-freeze material (i.e., 

unbound base) to obtain a pavement structure with a total depth of 915 mm (36 inches). 

The 915 mm (36 inches) depth was used because it represents a typical frost-free depth 

for many SHAs where 1 to 1.2 m (3 to 4 ft) of frost is experienced. The standard section 

derived from the 1993 AASHTO design guide was used for the no-freeze region. In this 

evaluation, variations in cost resulted from differences in treatment timing as well as in 

initial cost (rehabilitation treatment costs were constant). 

For the flexible pavement design, the action timing for resurfacing was based on the 

following distress levels: 

• Fatigue cracking—35 deduct points or a fatigue cracking index of 65. 

• Transverse cracking—50 deduct points or a transverse cracking index of 50. 

• Rut depth—12 mm (0.5 inch). 

• Ride—IRI of 2.7 m/km (171.2 inch/mi). 

The fatigue cracking deduct value of 35 represents about 10 to 15 percent medium 

severity fatigue cracking in the wheelpaths. This lower level of fatigue cracking was 

picked so that a 50-mm (2-inch) overlay would provide reasonable service, and a more 

intensive overlay design process would not need to be incorporated. 
111 

The transverse crack deduct level of 50 represents fairly extensive transverse cracking 

with medium severity transverse cracking occurring at about 9-m (30-ft) spacing. This is 

about the level where many SHAs decide to take action to resurface the pavement.(10,27)

The common treatment at this level is to place a 50-mm (2-inch) overlay. The rut depth 

of 12 mm (0.5 inch) is a common level to initiate some type of treatment. 

The ride value of 2.7 m/km (171.2 inch/mi) represents a roughness level at which many 

SHAs will generally place a 50-mm (2-inch) overlay. FHWA has also established this as 

the maximum acceptable roughness level for primary highway facilities in the Federal aid 

system.(28)

The timing at which the various distress categories reached the level to trigger 

improvement was then determined for each of the five environmental zones. Table 29 

summarizes the service life at which treatment was initiated along with the distress type 

driving the event. The timeline includes multiple treatment events for each of the 

climates. Only fatigue and transverse cracking were included in the table because they 

were achieved before the rutting and ride levels by significant margins. 

Table 29. Action timing for individual distress categories. 

Service Life (years) 

Region 1

3 

1

4

1

5

2

0

2

1

2

3

2

4

2

5

2

6

3

5

3

6

3

7 

3

8 

4

1 

4

2 

4

3

Deep-Freeze Wet 

Region (low FTC) 

F

C

T

C 

F

C 

F

C 

Deep-Freeze Dry 

Region (low FTC) 

F

C 

T

C 

F

C 

F

C 

Moderate-Freeze Wet 

Region (high FTC) 

F

C 

T

C 

F

C 

Moderate-Freeze Dry 

Region (high FTC) 

F

C 

T

C 

F

C

No-Freeze Wet 

Region 

F

C 

F

C 

F

C

T

C 

FC=50.8-mm (2-inch) overlay initiation based on fatigue cracking level. 

TC=50.8-mm (2-inch) overlay initiation base on transverse cracking level. 

In table 29, the fatigue cracking criteria was reached before the other criteria for all 

environmental settings; therefore, treatment timing was based on fatigue cracking 

accumulation for all regions. The timing of the first treatment (using fatigue as the 

driving factor) was then used as a basis for determining the frequency of subsequent 

treatments, which were obtained from performance curves for overlay pavements. 

Based on this information, a standard deterministic LCCA was performed for both the 

primary highway design and the interstate highway design. Figures 69 through 71 

provide the cross sections for primary and interstate standard sections. These initial 

pavement sections were developed using the design criteria in the PFS questionnaire and 

the 1993 AASHTO Guide for Design of Pavement Structures.

(1) The sections did not 
112 

include the added surfacing observed in the PFS response nor the literature review. To 

account for the added surfacing that is used in the frost areas, a second set of roadway 

sections were set up that included the extra surfacing or layer of frost-free material used 

by many States to mitigate frost effects. A second cost analysis was performed that 

considered the local adaptation of additional frost-free material and used the same 

geometric cross section with the exception of the increased depth of base material for the 

deep- and moderate-freeze regions. The additional surfacing depth was based on an 

assumed frost depth of 1 to 1.2 m (3 to 4 ft), which would usually be met with a 

requirement of around 915 mm (36 inch) of total surfacing depth. The base course under 

the mainline of the primary section was increased to 762 mm (30 inch), while the 

interstate section was increased to 660 mm (26 inch) of base course for the deep- and 

moderate-freeze regions to provide a minimum frost-free surfacing depth of 762 mm (36 

inches). These mitigated pavement sections come much closer to matching the pavement 

sections from the PFSs in response to the questionnaire. 

1 inch = 25.4 mm; 1 ft = 304.8 mm 

Figure 69. Diagram. Primary highway cross section. 

1 inch = 25.4 mm; 1 ft = 304.8 mm 

Figure 70. Diagram. Interstate highway, left section. 
113 

1 inch = 25.4 mm; 1 ft = 304.8 mm 

Figure 71. Diagram. Interstate highway, right section. 

As can be seen, a typical two lane section 1.6 km (1 mi) long with shoulders was used for 

the primary highway section. The interstate highway system consisted of a typical fourlane divided highway 1.6 km (1 mi) in length. 

Table 30 provides the treatment events used in the deterministic LCCA over the 30-year 

analysis period. The timing of the first overlay was based on the new construction 

performance curves. Subsequent events were obtained from overlay performance curves. 

All events were estimated from the mean predicted performance values. The values in 

this table are all with respect to the total pavement age. For example, the second overlay 

on the deep-freeze wet region takes place 26 years after initial construction and 12 years 

after the first overlay. 

Table 30. Overlay timing for the five environmental zones. 

Region Overlay 1 Overlay 2 Overlay 3

Deep-Freeze Wet Region (low FTC) 14 yr 26 yr 38 yr 

Deep-Freeze Dry Region (low FTC) 13 yr 24 yr 35 yr 

Moderate-Freeze Wet Region (high FTC) 20 yr 41 yr NA 

Moderate-Freeze Dry Region (high FTC) 21 yr 43 yr NA 

No-Freeze Wet Region 14 yr 25 yr 36 yr 

Treatment timing inputs for the probabilistic analysis can be found in table 31. The 

values in this table are not relative to the total pavement age. Rather, the format of 

Realcost was such that the performance life of each treatment (relative to the application 

of that treatment) was required. 
114 

Table 31. Distribution of performance life for probabilistic analysis. 

Initial Construction Life 

(year) Overlay Life (year) Region 

Min Likely Max Min Likely Max 

Deep-Freeze Wet (low FTC) 11 14 15 11 12 14 

Moderate-Freeze Wet (high 

FTC) 18 20 23 17 21 25 

No-Freeze Wet 12 13 14 10 11 13 

Deep-Freeze Dry (low FTC) 11 13 15 9 11 15 

Moderate-Freeze Dry (high 

FTC) 18 21 26 18 22 30 

Table 32 summarizes the unit cost information provided by the participating SHAs that 

was used to determine initial and treatment costs. For the deterministic LCCA evaluation, 

the average unit price for each material was used. The maximum, minimum, and mean 

values were used to determine the distribution of costs in the probabilistic analysis. A 

discount rate of 4 percent was used for all analysis. Salvage value was also included in 

the analysis for remaining life at the end of the analysis period. 

The results from the deterministic analysis for both the standard and mitigated sections 

can be found in tables 33 and 34, respectively. As can be seen, differences do exist 

between the regions. Using the standard section for all regions resulted in the deep-freeze 

regions having approximately the same costs as the no-freeze regions. The moderatefreeze regions were slightly lower in costs than the other regions. 

The cost differences in this analysis were due solely to changes in treatment timing 

because of variations in performance. The no-freeze region accumulated fatigue cracking 

relatively rapidly in comparison with the other regions. The addition of frost-free material 

to pavements in the Northern States could be contributing to this improved performance

period compared to the Southern States. The frost-free material adds structural capacity 

to the pavement section. Increases in strength relate to smaller stresses and strains leading 

to slower accumulation of damage, and hence improved performance in the deep- and 

moderate-freeze regions. 

Considering this, the mitigated sections provide more accurate cost comparisons because 

they include the additional costs associated with placement of a deeper unbound base 

course, which has contributed to extended performance. Mitigated pavements in the 

deep-freeze climates have the highest costs followed by the moderate-freeze regions. The 

no-freeze region exhibits the lowest annual cost. 

While there are differences in the deterministic analysis, there is no means to compare 

these differences to the distribution of data. The probabilistic analysis is a vital 

component of LCCA because it provides distribution statistics that can be used to 

determine if cost differences are significant. Figures 72 and 73 provide the distribution of 

total present worth costs for the primary and interstate standard sections. One standard 

deviation was used to compute the distributions provided in the figures. 
115 

Table 32. Unit cost information. 

State Reference 

Standard Unit Cost - 

$/m2 per 1 cm depth 

($/yd2 per 1 inch 

depth) 

AC Wearing Course 

Alaska HMA, Type II, Class B 0.67 (1.43) 

Idaho Plant Mix Pavement N/A 

Illinois SP HMA Surface Course 0.93 (1.98) 

Michigan Gap Graded SP N/A 

New York 12.5 mm Superpave HMA 1.59 (3.39) 

North Carolina S-12.5C 1.11 (2.37) 

Ohio Item 880 (7 yr warranty) 0.72 (1.53) 

Pennsylvania 

Superpave HMA Wearing 

Cr. 1.32 (2.81) 

MINIMUM 0.67 (1.43) 

MAXIMUM 1.59 (3.39) 

AVERAGE 1.05 (2.25) 

AC Binder/Leveling Course 

Alaska HMA, Type II, Class B 0.67 (1.43) 

Idaho Plant Mix Leveling Course N/A 

Illinois SP HMA Binder Course 0.93 (1.98) 

Michigan 4E50 1.09 (2.33) 

New York 

Binder Course/19.0 SP 

HMA 1.35 (2.87) 

North Carolina I-19.0 C 0.98 (2.10) 

Ohio Item 880 (7 yr warranty) 0.72 (1.53) 

Pennsylvania Superpave HMA Binder Cr 1.09 (2.33) 

MINIMUM 0.67 (1.43) 

MAXIMUM 1.35 (2.87) 

AVERAGE 0.97 (2.08) 

AC Base Course 

Alaska HMA, Type II, Class B 0.67 (1.43) 

Idaho Plant Mix Base Course NA 

Illinois SP HMA Base Course 0.93 (1.98) 

Michigan 4E50 1.09 (2.33) 

New York 

Base Course/25.0 mm SP 

HMA 1.22 (2.60) 

North Carolina B-25.0C 1.03 (2.19) 

Ohio Item 880 (7 yr warranty) 0.72 (1.53) 

Pennsylvania Superpave HMA Base Cr 1.12 (2.39) 

MINIMUM 0.67 (1.43) 

MAXIMUM 1.22 (2.60) 

AVERAGE 0.97 (2.06) 
116 

Table 32. Unit cost information, continued. 

State Reference

Standard Unit Cost - 

$/m2 per 1 cm depth 

($/yd2 per 1 inch 

depth)

Untreated Base Course

Alaska Grading D - 1 0.32 (0.68) 

Idaho Rock Cap NA 

Illinois NA 0.44 (0.93) 

Michigan 21AA 0.34 (0.72) 

New York Sub-base 0.34 (0.73) 

North Carolina Aggregate Base Course 0.49 (1.04) 

Ohio Aggregate Base 0.37 (0.80) 

Pennsylvania 2A NA 

MINIMUM 0.32 (0.68) 

MAXIMUM 0.49 (1.04) 

AVERAGE 0.38 (0.82) 

Table 33. Deterministic LCCA results for standard sections. 

Equivalent Uniform Annual 

Costs Region Primary 

($) Interstate ($) 

Deep-Freeze Wet Region (low FTC) 28,445 87,634 

Deep-Freeze Dry Region (low FTC) 29,165 89,002 

Moderate-Freeze Wet Region (high FTC) 25,771 82,556 

Moderate-Freeze Dry Region (high FTC) 25,538 82,112 

No-Freeze Wet Region 29,165 89,002 

Table 34. Deterministic LCCA results for mitigated sections. 

Equivalent Uniform 

Annual Costs Region Primary 

($) Interstate ($) 

Deep-Freeze Wet Region (low FTC) 52,924 117,231 

Deep-Freeze Dry Region (low FTC) 53,644 118,599 

Moderate-Freeze Wet Region (high FTC) 50,251 112,153 

Moderate-Freeze Dry Region (high FTC) 50,017 111,709 

No-Freeze Wet Region 29,165 89,002 
117 

0

500000

1000000

1500000

2000000

2500000

Deep-Freeze, Wet

Region (low FTC)

Moderate-Freeze,

Wet Region (high

FTC)

No-Freeze, Wet

Region

Deep-Freeze, Dry

Region (low FTC)

Moderate-Freeze, Dry

Region (high FTC)

Total Present Worth Costs ($)

Figure 72. Distribution chart. Annualized costs 

for standard primary pavement sections. 

0

500000

1000000

1500000

2000000

2500000

Deep-Freeze, Wet

Region (low FTC)

Moderate-Freeze,

Wet Region (high

FTC)

No-Freeze, Wet

Region

Deep-Freeze, Dry

Region (low FTC)

Moderate-Freeze, Dry

Region (high FTC)

Total Present Value Costs ($)

Figure 73. Distribution chart. Annualized costs 

for standard interstate pavement sections. 
118 

All of the mean cost differences between the regions fall within one standard deviation; 

therefore, it cannot be concluded that significant differences exist among the different 

climates based on the standard roadway sections. 

However, as noted above, comparisons based on the mitigated sections are more 

representative of the standard practice of northern SHAs. The results using these 

mitigated primary and interstate sections can be found in figures 74 and 75, respectively. 

0

500000

1000000

1500000

2000000

2500000

Deep-Freeze, Wet

Region (low FTC)

Moderate-Freeze,

Wet Region (high

FTC)

No-Freeze, Wet

Region

Deep-Freeze, Dry

Region (low FTC)

Moderate-Freeze, Dry

Region (high FTC)

Total Present Worth Costs ($)

Figure 74. Distribution chart. Annualized costs 

for mitigated primary pavement sections. 
119 

0

500000

1000000

1500000

2000000

2500000

Deep-Freeze, Wet

Region (low FTC)

Moderate-Freeze,

Wet Region (high

FTC)

No-Freeze, Wet

Region

Deep-Freeze, Dry

Region (low FTC)

Moderate-Freeze, Dry

Region (high FTC)

Total Present Value Costs ($)

Figure 75. Distribution chart. Annualized costs 

for mitigated interstate pavement sections. 

For the mitigated pavement sections on both primary and interstate highways, the 

annualized cost for the no-freeze region was lower than any other region. These 

differences fell outside the one standard deviation range. 

The mitigated pavement sections provide a more representative comparison of costs 

between the regions. The costs to maintain pavements in the no-freeze region are lower 

than the other regions. These differences do fall outside one standard deviation of the 

data. Using one standard deviation does not provide direct confidence intervals, but it 

does allow the distribution of the data to be evaluated because it relates to observed cost 

differences. 

121 

10. APPLICATION TO MECHANISTIC DESIGN 

One of the additional objectives indicated in the proposal for this research was for the 

contractor to make: 

“...recommendation as to ways in which mechanistic design methods can 

appropriately consider the most effective adaptations or materials standards to 

minimize the acceleration of pavement damage due to freezing and thawing.”

NCHRP 1-37A Guide procedures basically model a set pavement section and determine 

the stresses and strains that are produced in that pavement section based on given 

material properties and dimensions, as well as the loads imposed on those materials. 

These strains are compared to empirically based damage models to determine the 

incremental damage for each loading. The incremental damage values are totaled over 

time to predict the performance of the selected pavement section relative to the specific 

damage categories (i.e., rutting or fatigue cracking). While only a limited amount of local 

adaptations were discovered in this study, most can be accounted for in the M-E design 

procedures. 

The effect of local adaptations in M-E design procedures can be applied to the basic 

material properties or to the empirical damage models. Increasing the depth of frost-free 

material can be reflected in the M-E design procedures through an adjustment to the 

material strength properties (i.e., thaw weakening will not be as pronounced, yielding less 

reduction in subgrade stiffness during the springtime). This same local adaptation can 

also be accounted for by adjusting the empirical damage models so that the accumulation 

of deterioration is less rapid over time, given the same stress and strain. 

The use of additional frost-free material incorporated either in the pavement design or as 

a specific subgrade treatment can be accommodated in most mechanistic design 

procedures. Seasonal subgrade stiffness variation can be accounted for by inputting into 

the design strength parameters and the duration at which they occur. In these cases, the 

added frost-free layers are considered as either an improved subgrade or added subbase in 

the program. For instance, the basic AASHTO pavement design used for the cost analysis 

described in the previous chapter produced a pavement section consisting of 150 mm 

(6 inches) of ACP over 205 mm (8 inches) of untreated base. Constructing this section 

over frost-susceptible soil and in an area experiencing 1 m (3 ft) or more of frost 

penetration would result in a structure that is significantly weakened during spring 

thaw—possibly a reduction of up to 50 percent in the subgrade stiffness. This is 

addressed in the 1993 AASHTO Guide for Design of Pavement Structures(1) by using the 

effective modulus to account for the reduced springtime stiffness. In a design procedure 

based on the NCHRP 1-37A Guide, the increased damage accrued during the spring 

weakening of the subgrade can be accounted for in the program by a reduction in the 

subgrade stiffness and subsequent increase in stresses incurred during that period as a 

result of the lower stiffness. If the subgrade soil was replaced with 0.6 m (2 ft) or more of 

frost-free material, as is done in several of the PFSs, the stiffness of the subgrade can be 

increased because of the increased layer thickness and the reduced spring weakening 

effects. Combined, these inputs reduce the tensile stresses on the bottom of the pavement 
122 

as well as reducing the compressive stresses on the top of the subbase and subgrade 

layers, thus resulting in longer service life for the pavement section being analyzed. 

The NCHRP 1-37A Guide pavement design procedure (2) was developed using damage 

models that represent average pavement damage trends for the entire United States. The 

program computes different stress levels in a pavement section depending on the input, 

which can reflect different material properties based on environmental input. The 

empirically based damage models, however, were developed to produce the same 

increment of damage in New York, Los Angeles, and Chicago for a given stress. This 

was done so that the program could produce reasonable results throughout the country. 

This is acknowledged in the NCHRP 1-37A Guide (2), which provides a process for 

agencies to calibrate the damage models to local conditions. 

Section 3.3.6 of the NCHRP 1-37A Guide describes the need and recommended approach 

for local calibration of the M-E models. Following are specific directions recommended 

in the report:(2)

Because this design procedure is based on mechanistic principals the procedures 

should work reasonably well within the inference space of the analytical 

procedure and the performance data from which the procedure was calibrated. 

However, this is a very complex design procedure and it must be carefully 

evaluated by highway agencies wishing to implement. The following is the 

recommended calibration/validation effort required to implement this 

mechanistic-empirical design procedure.... 

• Review all input data. 

• Conduct sensitivity analysis. 

• Conduct comparative studies. 

• Conduct validation and calibration studies. 

• Modify input defaults and calibration coefficients as needed. 

While all five items listed above are necessary in the M-E implementation process, the 

last two address the adjustment of the M-E damage curves to match local experience. 

These processes were thoroughly explored with consideration given to the use of the 

models discussed in chapter 5 in accomplishing local calibration and validation. The 

following excerpts were taken directly from the NCHRP 1-37A Guide:

(2)
123 

• Calibrate to local conditions. 

The national calibration-validation process was successfully completed 

[as part of the NCHRP 1-37A project]. Although this effort was very 

comprehensive, further validation studies are highly recommended as a 

prudent step in implementing a new design procedure...A validation 

database should be developed to confirm that the national calibration 

factors or functions are adequate and appropriate for the construction, 

materials, climate, traffic, and other conditions that are encountered 

within the agencies’ highway system. 

Prepare a database of agency performance data and compare the new 

design procedure results with the performance of these “local” sections. 

This will require the selection of at least 20 flexible pavement sections 

around the state. If the state has very distinct climates this should be done 

in each climate. 

The goal of the calibration-validation process is to confirm that the 

performance models accurately predict pavement distress and ride quality 

on a national basis. For any specific geographic area, adjustments to the 

national models may be needed to obtain reliable pavement designs. 

• Modify the calibrations/inputs. 

If significant differences are found between the predicted and measured 

distresses and IRI for the agencies highways, appropriate adjustments 

must be made to the performance models. This study will also establish the 

level of accuracy desirable for key input parameters and default input 

values. Make modification to the new procedures as needed based on all 

of the above results and findings.

In light of these guidelines, a few SHAs are now in the process of refining the calibration 

factors for the performance models. The University of Washington is developing 

calibration factors for the WSDOT, which is described in the PCCP Models for 

Rehabilitation and Reconstruction Decision-Making report.(29) The steps outlined in the 

NCHRP 1-37A Guide were followed in the referenced work. The comparative analysis 

considered three major categories for rigid pavement performance: undoweled short 

jointed PCCP, undoweled short jointed PCCP mountain passes, and doweled bar 

retrofitted PCCP. Information from the SHA pavement management system, which 

contains more than 30 years of performance data for all pavements in the network, was 

used in steps four and five. Typical pavement sections consistent with those in place were 

used to estimate pavement performance for the three categories of pavement using the 1-

37A M-E design program. The resulting predictions were then compared to the average 

performance history for each category. Through several cycles of iterations, and a 

subsequent verification activity, a new set of C values were developed for the cracking 

and faulting models for the three categories of PCCP in Washington State. 

Not all SHAs have the advantage of having 30-plus years of network wide continuous 

pavement condition data. Those that do not may be able to fine-tune the NCHRP 1-37A 
124 

Guide output through the use of the models described in chapter 5. That would be 

particularly true for those agencies that have an environment significantly different than 

what would be represented by the national models developed for the M-E design 

procedure. The models developed in this project could be used to predict average rutting or 

fatigue cracking trends for a specific (regional or statewide) environment. These estimates 

could be used to then go through the same iteration and verification process described in 

the NCHRP 1-37A Guide, or the WSDOT research project, to determine if modified 

calibration factors are needed in the design program to accurately reflect performance in 

the SHA. An example of the NCHRP 1-37A Guide calibration methodology flowchart is 

shown in appendix G. 

To provide an example of the use of the models described in chapter 5, the calibration 

process outlined in the NCHRP 1-37A Guide was performed for a typical site in North 

Carolina. This sample calibration was performed for the basic roadway design parameters 

and pavement design section used in the economic analysis (chapter 9 of this report) for a 

rural primary highway. The representative climate for North Carolina presented in table 

22 was used for environmental inputs. 

In the NCHRP 1-37A pavement design program there are input procedures that allow the 

user to adjust the damage models used for fatigue cracking, rutting, and thermal cracking. 

For the AC fatigue cracking model there are three calibration factors (referred to as Bf1, 

Bf2, and Bf3) that adjust the K1, K2, and K3 correlation coefficients. In the current 

version of the NCHRP 1-37A pavement design software, these calibration settings can be 

found through the tools menu and the calibration settings for new flexible pavement 

under “Distress Model Calibration Settings–Flexible.” Four categories of calibration 

conditions are available for AC fatigue analysis: 

• Special analysis—Bf1 input only. 

• National calibration—No adjustments available. 

• State/regional calibration—Bf1, Bf2, and Bf3 inputs available. 

• Typical agency values—K1, K2, and K3 adjustments available. 

The same set of inputs is available for AC rutting, except that the adjustments for K1, K2, 

and K3 are termed Br1, Br2, and Br3, respectively. Conversely, only the State/Region 

Calibration category is available for AC thermal fracture with the adjustment inputs 

termed Bt1, Bt2, and Bt3 respectively. 

Similar adjustments are available for chemically stabilized material (CSM) fatigue and 

subgrade rutting. The correlation coefficients (C1 through C4 and C1 through C6) can be 

individually modified for the AC cracking, CSM cracking, and IRI categories. 

The roadway section used for this sample consisted of 150 mm (6 inches) of hot mix 

asphalt (HMA) concrete pavement over 205 mm (8 inches) of granular base with a finegrained, frost-susceptible subgrade soil, which was assumed to be A-6 soil as defined in 

the AASHTO classification system.(30) The values input into the NCHRP 1-37A 

pavement design software matched the mix properties and typical specifications provided 

by North Carolina. The traffic loading was input as load spectra, but it was very similar to 
125 

the 3 million ESAL loading over 20 years used in previous analysis described in this 

report. 

The calibration process consisted of running the NCHRP 1-37A pavement design 

software with the national calibration factors to estimate performance and compare 

output to the predictions of the models from this study. The NCHRP 1-37A pavement 

design software (using the nationally calibrated models) indicated that the pavement 

would experience rapid top-down cracking with an accumulation of more than 750 m/km 

(4,000 ft/mi), more than 13 mm (0.5 inch) of rutting, and ride values progressing from 1 

m/km (64 inch/mi) to more than 8.3 m/km (525 inch/mi) in 20 years. The design section 

used for this analysis was developed using the 1993 AASHTO Guide for the Design of 

Pavement Structures(1) with 85 percent reliability, so it did not seem reasonable for it to 

experience that much longitudinal cracking and ride deterioration. Looking at the 

pavement distresses recorded at the various LTPP sites around North Carolina, similar 

pavement sections have not experienced that level of distress accumulation over a 20-

year period. Dominate pavement distress recorded at the LTPP sites in North Carolina 

consisted of some fatigue cracking, isolated transverse cracking, and rutting, so regional 

calibration seemed appropriate. For this example, that calibration focused on the 

accumulation of fatigue cracking, rutting, and ride values because those were the 

predominant distresses indicated by the local LTPP sites. The calibration was achieved 

through an extensive iterative process of varying either the B values for the fatigue and 

rutting models or the C values for the ride model. 

Figure 76 provides the fatigue cracking predictions for the sample design. The NCHRP 1-

37A damage trends for fatigue cracking are generally continuous trends without the 

observation of a crack initiation point, which is included in the models developed under 

this study. The calibration was thus set to match the frost model at 20 years, but it could 

have been adjusted to fit the model at 10 or 15 years as well. The Bf1 term was set at 0.47 

to provide the NCHRP 1-37A Guide fatigue cracking prediction shown in figure 76 as 

local calibration. 
126 

0

5

10

15

20

25

30

35

0 2 4 6 8 10 12 14 16 18 20

Years 

Fatigue Cr. Area (%)

NCHRP 1-37A National Calibration

Frost Study Model 

NCHRP 1-37A w/ Local Calibration

Figure 76. Graph. Comparison of fatigue cracking trends 

before and after local calibration. 

The rutting prediction model developed in this study and the NCHRP 1-37A Guide

model are similar in form. Therefore, the NCHRP 1-37A rutting models could be 

calibrated to local conditions for the full range of the pavement ages as opposed to the 

fatigue models which had to be calibrated at a certain age. The Br1 factor was set to 0.1 

to obtain the rutting prediction for the local calibration shown in figure 77. This resulted 

in rut development predicted primarily in the base and subgrade soils by the NCHRP 1-

37A design software. Similar trends were also developed by increasing the Br1 factor to 

above 1 and setting the Bs1 terms to 0.05 or less, which moved the rutting prediction 

from occurring in the subgrade and aggregate base to occurring in the HMA based on the 

NCHRP 1-37A design software. Either approach provided the same basic rutting trend 

over time. 
127 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 10 12 14 16 18 20

Years 

Rutting (inches) 

NCHRP 1-37A National Calibration

Frost Study Model 

NCHRP 1-37A w/ Local Calibration

1 inch = 25 mm 

Figure 77. Graph. Comparison of rutting trends before and after local calibration. 

Similar to the rutting models, the form of models for ride deterioration in terms of IRI 

was also consistent over time; therefore, the NCHRP 1-37A model could be calibrated 

over the full range of pavement ages and not at a specific age. The C values were 

adjusted to bring the NCHRP 1-37A model more in line with the ride model developed 

for this study using the North Carolina conditions. As can be seen in figure 78, a very 

good fit was achieved after the local ride calibration. To get this fit, the C1 value was 

lowered to 0.10 of the value for the national calibration. The rest of the C values were not 

as sensitive as the C1 value in providing a better fit to the performance model from this 

study. 
128 

0

50

100

150

200

250

300

0 2 4 6 8 10 12 14 16 18 20

Years 

Ride (in/mile)

NCHRP 1-37A National Calibration

Frost Study Model 

NCHRP 1-37A w/ Local Calibration

 100 inches/mi = 1.5 m/km 

Figure 78. Graph. Comparison of ride trends before and after local calibration. 

This was one set of iterations for a pavement with 150 mm (6 inches) HMA over 205 mm 

(8 inches) of granular base. Similar iterations would need to be performed on a range of 

pavement sections with both higher and lower traffic loadings and the resulting average 

calibration factors would then be used to produce pavement design performance 

predictions that would be calibrated to the local environmental conditions. 

This type of calibration procedure should be used only if the local agency does not have 

its own performance data for developing local calibrations for use in the NCHRP 1-37A 

pavement design software. If an agency has limited pavement performance data, the 

models developed in this study could be used to estimate general pavement performance 

trends adjusted to fit the relative values of the local data in a process similar to that 

described in the next chapter on pavement management systems. 

The performance trends from this study were developed with the intent to represent a 

broad array of inservice pavements throughout North America. As such, the contributions 

of surface treatments such as chip seals were not incorporated in the models and cannot 

be used to calibrate the NCHRP 1-37A Guide performance curves for sealed HMA 

pavement. SHAs using these treatments must develop damage models that are specific to 

the performance of pavements with surface treatments. 
129 

11. APPLICATION TO PAVEMENT MANAGEMENT 

The models developed for this project can be used as a pavement management tool. Most 

PMSs in North America collect pavement condition data in a manner similar to that used 

in the LTPP program. The LTPP program collects pavement distress in more detail than 

most agencies do for pavement management purposes, but most agencies follow the same 

basic surface distress categories. Fatigue cracking, longitudinal cracking, and transverse 

cracking data are collected for flexible pavements and faulting, transverse, and 

longitudinal cracking are recorded for rigid pavements. The LTPP method also uses three 

severity levels that are similar to those used in PMSs. In some cases the extent 

conventions need to be modified, but that is fairly easily accomplished. For example, 

fatigue cracking is collected in square meters for LTPP purposes, but most SHAs 

measure the percentage of area or length of the fatigue cracking in the wheelpaths for use 

in their PMS. This is easily rectified because the dimensions of the LTPP test section are 

known, and fatigue cracking is assumed as occurring in the wheelpaths, so quantities can 

be converted to percentages in terms of wheelpath area. 

In consideration of PMS application, several of the models developed in this study were 

based on LTPP measurements that were converted to be consistent with PMS extant 

conventions. In turn, these values were extended to standard PMS deduct values so that 

the measured distress could be represented in terms of deduct values or a pavement 

condition index (i.e., 100 less the sum of deduct values for a given test section). This 

approach provided for the characterization of the various levels of distress severity that is 

consistent with common PMS practices. A complete discussion on the development of 

deduct value development can be found in chapter 3 of this report. 

One of the more difficult exercises in implementing PMS applications is the development 

of a family of curves that represent the standard deterioration trends unique to an 

agency’s roadways and environment. Few SHAs have been able to measure pavement 

conditions in a consistent manner. Relating one set of measurements over a specific area 

to another set of measurements over that same area from year to year (as was done at the 

LTPP sites) has proven to be a difficult task. To overcome this, some SHAs have relied 

on models developed based on test sites that could be monitored with time or expert 

opinion, while others have used models developed based on other SHA data. 

Pavement performance models are used to predict future deterioration of a highway 

network’s pavements based on the last set of pavement condition survey data. The 

models (or condition data) have a large effect on the information the PMS provides for 

future construction program decisions, and also influence the future pavement condition 

trends as well as the funding needs for that agency. 

SHAs with little historical pavement condition information or limited amounts of 

composite indices can use the models from this study to develop a family of curves to 

help implement a PMS or convert a PMS from one with only composite indices available. 

Not all agencies have maintained their raw pavement condition survey data and only have 

files with the computed index for prior years. Because the location referencing system 

can change from year to year or the posting of those locations can change, there is often 
130 

no attempt to correlate the pavement condition data taken at a specific location with 

future or past data taken at that same location. 

Unfortunately, most SHAs use different pavement distress indices based primarily on the 

specific pavement management system they have adopted, and there is a wide range of 

systems in use throughout the United States. Deduct curves developed for South 

Dakota,(10) which are now in use by several other SHAs, were used to develop pavement 

performance trends for this study. The extent of pavement distress (i.e., area or length) 

for each severity level was accumulated in terms of the sum of the pavement distress 

deduct values. As noted in the chapter on database development, the deduct values were 

determined using the expressions shown in equations 7 through 10 for each severity level. 

T L M H

H H

M M

L L

D D D D

D P

D P

D P

= + +

=

=

=

0.6956

0.6107

0.514

5.2064 *

4.4575*

3.4082 *

 

These equations are represented in figure 79. 

1

10

100

0.1 1 10 100

Percent Occurence

Deduct Value

Low Severity

Moderate Severity

High Severity

Figure 79. Graph. Individual distress deduct curves. 

(7) 

(8) 

(9) 

(10) 
131 

The individual distress indices were developed so those deduct values represented desired 

action times for most SHAs. For example, a fatigue distress deduct accumulation level of 

about 35 represents about 10 to 15 percent medium severity cracking over the length or 

area of the wheelpath assuming each wheelpath is 1 m (3 ft) wide. For transverse 

cracking, an accumulation level of about 50 represents a sufficient length of medium 

severity transverse cracking to indicate a crack spacing of approximately 9 m (30 ft) 

between transverse cracks. These values represent damage levels that affect or initiate 

repair or rehabilitation project timing for most SHAs.(10)

Figure 79 can be used by SHAs to gain an understanding for the distress magnitudes 

represented by deduct values. The medium severity deduct curve can be used to get the 

average magnitude required to produce that value. This process can also be used to make 

comparisons between SHA-specific damage indices and those used in this study. 

The pavement performance curves for any specific distress can be used to develop 

general deterioration trends to forecast future pavement conditions from current condition 

measurements. 

As an example, the fatigue deduct values shown in figure 80 (from the environmental 

sensitivity study) for the no-freeze wet region were converted to a pavement condition 

index by subtracting the accumulated deduct values from 100. The resulting pavement 

deterioration trend for fatigue cracking for a pavement with the characteristics outlined in 

table 19 can be found in figure 81. 

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30

AGE (years)

PREDICTED FWPC (deduct values)

Deep Freeze Wet Region

Moderate Freeze Wet Region

No Freeze Wet Region

Deep Freeze Dry Region

Moderate Freeze Dry Region

Figure 80. Graph. Example of fatigue cracking trends for different environments. 
132 

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30

Age

Fatigue Cracking Index

Figure 81. Chart. Fatigue cracking index trend 

for environmental case wet no-freeze. 

This trend line represents the average trend found for a specific set of conditions used in 

the sensitivity study for this project. SHAs can determine a set of input parameters that 

represents the regional-specific conditions (environment, traffic, and pavement structure) 

and develop a similar deterioration trend line using the performance models from this 

project. A series of these trend lines can be developed for the range of conditions 

expected across a region and used as a tool to predict future pavement conditions. As 

condition data are collected on pavement structures, the average deterioration line can be 

updated or shifted to match the most recently collected data. For example, a highway 

section that matches the criteria used to develop figure 81 surveyed at year 10 exhibits a 

fatigue cracking index of 75. The trend in figure 81 would be shifted to the right so that it 

predicts a value of 75 at year 10 without altering the slope. This would result in the life of 

the particular highway section being extended by approximately 2 years based on the 

condition data collected at year 10. This is shown in figure 82. 
133 

0

10

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30

Age

Fatigue Cracking Index

Figure 82. Chart. Example of shifting trend line to fit index for a given location. 

Where an agency used different pavement condition indices, it would need to develop the 

area of medium severity fatigue cracking using equations 7 through 10 for medium 

pavement deterioration trend lines. The amount of medium-severity fatigue-cracking 

values would then be used to convert to the pavement condition deduct values the agency 

uses. 

Where an agency uses a composite index that combines several of the distresses modeled 

individually in this study, the process would be more complicated. The agency would 

probably need to use each pavement deterioration curve to predict the future distress level 

for each distress and then combine the distress levels according to their combined 

equation into a composite index for set program dates rather than developing a trend line 

for the composite index. 

135 

12. KEY FINDINGS 

An overview of the findings from the performance comparisons between the five climatic 

scenarios established for the study can be found in table 36. The “X” symbol indicates 

differences exist, and they are statistically significant at a 95-percent confidence. 

Table 35. Summary of statistical comparisons. 

Performance 

Measure 

DeepFreeze 

Wet 

Region 

ModerateFreeze Wet 

Region 

NoFreeze 

Wet 

Region 

DeepFreeze 

Dry 

Region 

ModerateFreeze 

Dry 

Region 

Roughness 

(flexible) 

 

Fatigue/wheelpath 

cracking (flexible) X X X X X 

Transverse 

cracking (flexible) X X X X X 

Rut dept (flexible) X X X 

Roughness (rigid) X X X 

Longitudinal 

cracking (rigid) X X X X X 

Transverse 

cracking (rigid) X X 

Faulting (rigid) X X 

Following is a list of descriptions of the significant differences noted in table 36: 

• Differences in flexible pavement roughness between the various climates at 

20 years were not found to be significant. 

• The no-freeze wet region exhibits significantly lower rigid pavement roughness 

than the deep-freeze regions (both wet and dry) at 20 years. 

• Rutting accumulations in the moderate-freeze regions (both wet and dry) are 

significantly larger than the no-freeze wet region at 20 years. 

• The deep-freeze regions (both wet and dry) and no-freeze wet region accumulate 

significantly larger quantities of fatigue/wheelpath cracking at 20 years compared 

to the moderate-freeze regions (both wet and dry). 

• Predictions for flexible pavement transverse cracking at 20 years result in the 

deep-freeze regions (both wet and dry) having significantly larger quantities than 

the moderate-freeze regions (both wet and dry). In addition, the moderate-freeze 
136 

regions (both wet and dry) have significantly larger accumulations than the nofreeze wet region. 

• Accumulations of rigid pavement longitudinal cracking at 25 years were 

significantly lower in the no-freeze wet region as compared with all other regions. 

• At 25 years, rigid pavement transverse cracking was significantly larger for the 

no-freeze wet region as compared to the deep-freeze wet region. All other 

comparisons were found to be insignificant. 

• The magnitude of joint faulting at 20 years was found to be significantly larger in 

the deep-freeze dry region as compared with the moderate-freeze dry region. 

It should be noted that the data in this study do not support the notion that deep frost 

penetration and multiple FTCs are mutually exclusive. Areas do exist with high freezing 

indices and large quantities of annual FTCs. 

A review of information provided by participating SHAs and relevant literature regarding 

local adaptations to mitigate frost was performed. There was a large variation in typical 

cross sections for similar design situations, and no specific treatment was universally 

used to counter frost effects. Many States with frost penetration did required additional 

surfacing or the replacement of frost-susceptible soils with frost-free material. In 

addition, most of the States have adopted Superpave PG binder specifications and mix 

design procedures. This relatively recent development has to a large extent eliminated 

local adaptations to materials specifications and mix designs for HMA pavements. 

Life cycle cost analysis was conducted using two different approaches. The first approach 

used initial construction costs that were consistent for each of the five climatic scenarios 

and were based on a standard roadway design. This resulted in equivalent uniform annual 

costs that were not significantly different between the regions. The second approach used 

initial construction costs that varied with the climatic scenario. The typical section for the 

deep- and moderate-freeze regions included additional frost-free material to represent the 

mitigation found in the review of SHA information. This cost analysis resulted in the nofreeze region having equivalent uniform annual costs that were lower than the other 

regions. 

The models developed from this study can be used in pavement management system 

applications as well as to perform local calibration for the NCHRP 1-37A Guide. 
137 

13. SUMMARY AND CONCLUSIONS 

To meet the objectives of this study, the research was separated into three basic steps. 

The first step was to develop models to quantify the effect of the environment, 

particularly related to deep frost or multiple FTCs, on pavement performance in terms of 

pavement distress. The second measure was to look at pavement design and materials 

standards that compensate for or mitigate the effect of seasonal frost. The third step was 

to evaluate costs associated with pavement design elements considering frost-related 

effects. Supplementary to these efforts, the application to mechanistic-based pavement 

design was also addressed. 

Findings from phase one of this study were incorporated into the work performed in 

phase 2. As part of phase 1, a literature review was conducted to guide and supplement 

the analysis. It was also discovered in phase 1 that the dataset necessary to represent the 

number of variables required in the analysis needed to be expanded from only SMP sites 

to include other LTPP sections. GPS-1, GPS-2, GPS-6, SPS-1, and SPS-8 experiments 

were used in the dataset for flexible pavements, while the rigid pavement dataset 

consisted of GPS-3, SPS-2, and SPS-8 projects. Because frost penetration data are not 

directly measured at all of these sites (as is the case for SMP projects), surrogate factors 

were investigated. FI was found to represent relative frost conditions in the dataset, and 

that information was used in place of frost penetration. The analysis database developed 

for phase 2 included data to represent the following factors: 

• Pavement types (rigid, flexible). 

• Climatic data (rainfall, FI, and thawing index from temperature data). 

• Frost depth (temperature sensors and resistivity data). 

• Deflection data (stresses and strains calculated from layer material properties). 

• Performance data (distress and permanent deformation). 

• Soils and material properties. 

• Traffic data. 

The dataset included test sections located in the deep-freeze wet region, deep-freeze dry 

region, and the no-freeze wet region. The data were not separated by climatic setting, and 

they were analyzed independently to reduce the number of observations per grouping and 

because the information is largely dependent on the method used to group the data. 

Rather, all data for one pavement type were combined. Climatic differences were 

accounted for through the use of explanatory variables in the regression analysis. Test 

sections with surface treatments were excluded from the analysis. This resulted in more 

than 520 and 280 test sections for flexible and rigid pavement structures, respectively. 

This study considered the following performance data: 

• Roughness—flexible and rigid. 

• Rut depth—flexible. 
138 

• Distress (fatigue, transverse, longitudinal, block, raveling)—flexible. 

• Distress (corner breaks, transverse, longitudinal, durability, pumping)—rigid. 

• Faulting—rigid. 

• Strain—flexible. 

Some of the distress types were excluded from further analysis because of the limited 

number of nonzero observations or because the variation at one site over time was 

relatively large. This included raveling and durability cracking. 

Models were developed using multivariate regression analysis through an iterative 

process. All explanatory variables were investigated through the use of P-values to 

determine if their contribution to the prediction was significant. Variables that were 

marginally significant were included in the model only if they improved the prediction 

capability of the regression. In addition, various transformations and relationships 

between the performance measure and pavement age were investigated. The models with 

the smallest deviation were selected for use. 

Some of the models resulted in predictions that were not logical based on general 

engineering experience. For example, the strain model estimated a reduction in strain as 

the pavement age increased. Models exhibiting these types of errors were excluded from 

the performance comparison evaluation. Table 35 provides an overview of the selected 

models including R-squared data. 

Table 36. Overview of developed performance models. 

Model Pavement Model Type Rsquared Observations

Roughness Flexible 

regression 

(shifted) 0.78 4,544 

Roughness Rigid regression 0.78 2,652 

Rut Depth Flexible regression 0.45 1,966 

Faulting Rigid regression 0.47 1,384 

Fatigue and logistic NA 1,977 

Wheelpath Cracking 

Flexiblededuct regression 0.63 1,486 

Fatigue and logistic NA 1,977 

Wheelpath Cracking 

Flexiblepercent regression 0.49 1,481 

logistic NA 1,920 Transverse Cracking Flexible regression 0.71 1,077 

Longitudinal logistic NA 475 

Cracking Rigid regression 0.38 240 

logistic NA 489 Transverse Cracking Rigid regression 0.54 228 
139 

Using these models, performance comparisons were made for the following five climatic 

scenarios. Performance curves, as a function of pavement age, were evaluated and mean 

comparisons were made using 95 percent confidence intervals at select pavement ages, 

generally between 20 and 25 years. These comparisons were used to identify statistically 

significant performance differences. 

• Deep-freeze, wet region. 

• Deep-freeze, dry region. 

• Moderate-freeze, wet region. 

• Moderate-freeze, dry region. 

• No-freeze, wet region. 

In addition, performance predictions in each of the PFS were evaluated using typical 

climates found in each SHA. It was also observed that regional environmental conditions 

can vary widely in one state. Climatic differences in each PFS were presented as well. 

The PFSs were asked to provide information on the pavement design they would use for 

a standard primary and interstate highway with set design parameters as well as the 

material specifications, test procedures, and costs associated with those designs. 

The questionnaire responses produced a wide variation in pavement design sections for 

essentially the same design parameters. The PFSs did not identify any particular 

treatment in their designs that addressed frost effects other than one reference to the frost 

heave design procedure contained in the AASHTO Guide for Design of Pavement 

Structures.(1) In a secondary query, it was found that several of the northern SHAs did 

require that frost-susceptible subgrade soils must be removed as part of their construction 

specifications. 

The project also called for the review of practices in adjacent States to see if any 

treatments could be of use; however, only limited contacts were provided. To augment 

this process, existing research reports or ongoing research for all SHA were reviewed 

from Web sites for anything available for this process. This query produces nothing to 

add to that already found for the PFS and the earlier literature review. 

Many of the northern SHAs add additional untreated frost-free surfacing as part of their 

pavement designs based on the maximum measured frost depth.(12) For some SHAs, frostsusceptible subgrade soils are removed and replaced with frost-free material for depths 

ranging from 0.61 to 1.22 m (2 to 4 ft) as part of their normal construction requirements 

to eliminate the need to consider frost depth in the design procedures. There is no real 

way to show the relative value of the extra depth of frost-free material other than to note 

its widely accepted use. The extra surfacing depth is probably already accounted for in 

the pavement performance models developed considering that many SHAs follow that 

practice, and GPS test sections represent standard SHA design procedures. It may be one 

of the reasons for the longer service lives observed in fatigue cracking on flexible 

pavements in the moderate-freeze environments, which would have the extra surfacing 

compared to the wet no-freeze environments that would not have the extra surfacing. 
140 

A possible consideration was noted relative to the use of a uniform surfacing section 

across the full roadway prism. For those SHAs that use frost-susceptible subgrade soils in 

part of their shoulder section or outside their shoulder surfacing materials, there is the 

potential for differential frost heaving. The extent to which this is a problem or the effect 

it has on pavement performance could not be quantified. Any SHA that has experienced 

differential frost heave may wish to look more closely at the capillary flow of moisture 

into the roadway section during the advancement of the freezing front and onset of frost. 

The possible use of surface seals was also noted in the northern tier States where highway 

volumes were low enough to allow their use. This treatment did not seem to be practical 

in SHAs where chip sealing is not usually done, particularly on higher volume roadways. 

Little was noted in the area of material specifications. Prior to Superpave, the researchers 

would have expected to find different (softer) binders used in the more Northern States as 

well as the use of a finer, lower-void mix compared to the Southern States. In the 

response provided by the PFSs, most SHAs have or are in the process of switching to 

Superpave. The use of the Superpave binder specifications should improve cold weather 

performance, a major consideration in the development of those specifications. The use 

of Superpave mix design procedures has to a large extent eliminated local adaptations in 

mix design procedures and specifications that might have provided improved 

performance in areas of deep frost penetration or numerous FTCs. 

Very little was found in the SHA specifications that indicated a trend in specifications 

that would have had an impact on mitigating frost effects. What variation there was 

seemed most likely due to the availability or type of aggregate found in the State. 

An economic evaluation was conducted, which consisted of computing equivalent 

uniform annual costs and present worth costs using deterministic and probabilistic 

LCCA. Standard cross sections were developed for interstate and primary highways using 

the 1993 AASHTO Pavement Design Guide.

(1) LCCA was performed using these 

standard sections for all of the five regions. However, because many northern SHAs use 

additional depths of frost-free material to mitigate frost effects, an additional LCCA was 

performed. In this analysis, the roadway sections for the deep- and moderate-freeze 

regions included additional unbound base course thickness while the no-freeze region 

remained unchanged from the standard AASHTO design. 

The differences between the costs using the standard sections were relatively small and 

well within one standard deviation when considering the distribution of the data. Using 

the mitigated section, which is more representative, resulted in the no-freeze region 

having life cycle costs that were less than the costs of the other regions and fell outside 

the range of one standard deviation. 

The application of the results of this study to M-E design procedures was also explored. 

Potential uses of the models in implementing the NCHRP 1-37A Guide design procedure 

were also discussed. The use of additional frost-free material incorporated either in the 

pavement design or as a specific subgrade treatment can be accommodated in 

mechanistic design procedures. In most mechanistic design procedures there is a process 
141 

that accounts for changing subgrade stiffness experienced throughout the year. In those 

areas with significant thaw weakening, the amount of time that the subgrade is weaker as 

well as the amount of weakening that occurs during the spring thaw is input into the 

program. In these cases, the added frost-free layers would be considered as either an 

improved subgrade or added subbase in the program. For instance, in the basic pavement 

design used in the economic analysis in the previous chapter, the AASHTO design 

procedures produced a pavement section consisting of 150 mm (6 inches) of ACP over 

205 mm (8 inches) of untreated base for the design considerations included in the PFS 

design questionnaire. 

In an area where there is 1 m (3 ft) or more of frost penetration during the winter, that 

subgrade would weaken significantly during spring thaw, possibly as much as 50 percent. 

That weakening is addressed in the 1993 AASHTO guide by using the effective modulus 

to account for the reduced springtime stiffness. In a mechanistic-based design procedure, 

the increased damage accrued during the spring weakening of the subgrade would be 

accounted for in the program by an increase in stresses incurred during that period as a 

result of the lower stiffness. If the subgrade soil was replaced with 0.61 m (2 ft) or more 

of frost-free material, as is done in several of the PFSs, then the stiffness of the subgrade 

can be increased because of the replaced layer, and the reduced spring weakening effects 

can be eliminated or significantly reduced. Combined, these inputs would reduce the 

tensile stresses on the bottom of the pavement and reduce the compressive stresses on the 

top of the subbase as well as the subgrade. The results should be demonstrated in longer 

service life for the pavement section being analyzed. 

Probably of greater significance is the potential use of the models from this study to help 

provide a regional calibration for the 1-37A mechanistic-based design procedure. The 

1-37A M-E design program uses nationally calibrated damage trends, but the NCHRP 1-

37A Guide recommends that the users consider a regional calibration- verification 

procedure. For those SHAs that do not have the regional data to support the activity, the 

potential use of the models developed in this project as well as the general procedures to 

develop regional calibration factors were described. 

There is also a potential for using the models developed in this study to augment SHAcollected data for the development of a family of curves for regional use in PMSs. Where 

an agency does not have sufficient regional data to develop project-specific pavement 

deterioration curves, the models from this study could be used to develop a family of 

curves that would fit the local environment. It is not envisioned that these models could 

be used in place of SHA-specific data but, in the beginning—during the implementation 

of a PMS—the models could be used to make an initial trial of developing the pavement 

deterioration trends used in the PMS. As with the implementation of any PMS, as data 

become available, the SHA should refine the performance trends based on its own actual 

experience, but these models would provide a very good starting point. 

143 

APPENDIX A. LITERATURE REVIEW 

This appendix details relevant literature reviewed for this study to gain an understanding 

of pavement response in various freezing conditions. Although the available literature 

directly related to the study was relatively limited, it provided insight that was used to 

formulate the activities conducted. The majority of the literature regarding frost effects 

dealt with quantifying the change in material properties and performance characterization 

on particular projects. In addition, some limited information was found on using LTPP 

data to model pavement performance in frost areas. 

THE EFFECTS OF FREEZE-THAW PERIODS ON A TEST PAVEMENT IN 

THE DANISH ROAD TESTING MACHINE 

In this literature example, Zhang and MacDonald(31) reported frost studies using the 

Danish road testing machine in their paper “The Effects of Freeze-Thaw Periods on a 

Test Pavement in the Danish Road Testing Machine” presented at the Ninth International 

Conference on Asphalt Pavements, International Society for Asphalt. 

Tests performed in the testing machine were subjected to freeze-thaw cycles to study the 

effects of frost on pavement performance. Initially, Road Test Machine 2 (RTM2) was 

loaded with more than 150,000 repetitions of a 60,000 newton (N) (13,488 lbf) wheel 

load. A freeze-thaw cycle was then simulated and the pavement structure was loaded with 

1,800 additional repetitions during the thawing period. This process was then repeated; 

however, the number of load repetitions during thawing was modified to 3,000. Rut levelup and overlay layers were then placed on the RTM2 pavement structure. The resulting 

structure was labeled as RTM3. It was subjected to one freeze-thaw cycle and loaded 

with 2,800 repetitions during the thawing period, followed by 50,000 repetitions after the 

completion of the thawing process. Soil suction, material response, profile, and 

temperature data were collected throughout the testing periods. The profile data were 

obtained using a profiler specially built for profile measurements in the RTM. To 

simulate freezing conditions, the chamber was maintained at –15 °Celsius (C) 

(5 °Fahrenheit (F)). During thawing, the temperature of the chamber was gradually raised 

to 10 °C (50 °F) and then to 25 °C (77 °F). The water table level in the RTM was 

maintained at 0.8 m (2.62 feet (ft)) below the level of the pavement surface. 

As a result of the testing, a new subgrade permanent strain model was developed as well 

as roughness models in terms of slope variance, international roughness index (IRI), and 

rut depth. Equation 11was developed for permanent strain in the subgrade. 
144 

PZ N Z p Z ε σ *ε 0.333 * ( / ) 0.333 = 0.087 * (11) 

 

, , constants

 verticalresilient (elastic)strain at depth Z in microstrain

referencestress(atmoshperic pressure0.1MegaPasch al)

 verticalstressat depth Z

number of loadrepetitions

plasticstrain at depth Z in microstrain

Where:

=

=

=

=

=

=

α β

ε

σ

ε

A

P

N

Z

Z

PZ

Plastic strain in the subgrade material was determined to increase 60 to 75 percent during 

the thaw-loading period, while the transient resilient response increased 40 percent. The 

increase in strain was attributed to movement, reorientation, and resettlement of the soil 

particle displaced by previous frost heave. This study provided good numerical 

information on the change in material properties from frost effects. 

A DETERIORATION MODEL FOR PAVEMENTS IN FROST CONDITIONS 

Doré, Konrad, and Roy(32) presented a paper, “A Deterioration Model for Pavements in 

Frost Conditions,” analyzing thermal cracking. Using data from test sections in Quebec, 

Finland, and Minnesota (the Minnesota Road Research Project (Mn/ROAD) as well as 

two LTPP SMP sites, Doré, Konrad, and Roy developed two empirical models. The first 

model provides curves for delimiting different levels of cracking extents based on the 

solicitation index. The second model estimates the equivalent annual evolution of 

cracking, which is defined as the total cracking developed over 1 year divided by the age 

(in years) of the pavement. 

A sensitivity analysis was performed by altering all contributing factors used in the 

model to predict the life span of the pavement. The results showed that the frost depth 

and width of snow removal contribute the most to pavement performance. The model is 

based on a number of assumptions, and it is valid only for pavements with substantial 

frost penetration. In addition, the data used in generating the model came from a small set 

of locations. Construction variability was not taken into account and would contribute 

significantly to crack development in areas of local weak spots. 

ANALYSIS OF SEASONAL PAVEMENT DETERIORATION 

Doré and Savard(33) reported additional studies of thermal cracking which in their paper 

“Analysis of Seasonal Pavement Deterioration.” Two test sites in Quebec, Canada, were 

monitored for 3 years to evaluate seasonal accumulation rates of distress. One site was 

constructed in 1994 specifically for this project. It consisted of five test sections (three of 

which were insulated). The other site was part of C-SHRP and consisted of four 
145 

noninsulated test sections. Each of these sections had the same structural design and 

differed only in the grade of asphalt used. Noninsulated sections were used to compare 

the accumulation of different distress types in each season. Similar analysis was 

performed on the insulated sections, thus evaluating the effectiveness of insulation in 

preventing frost-related distress. 

The noninsulated sections were found to experience 65 percent of transverse cracking 

damage in the winter followed by 25.5 percent in the spring. Transverse cracking is 

caused by the accumulation of tensile stress in the pavement due to thermal shrinkage. 

The relatively large amount of damage incurred during the spring is likely to be the 

propagation of existing transverse cracks originally developed in the winter. Similarly, 

55 percent of longitudinal cracking damage occurred in the winter compared with 

23 percent in the spring and 22 percent in the summer. The research team believed that 

differential heaving during winter was the main mechanism in longitudinal cracking; 

however, the data does not support this belief. Other factors may also contribute to the 

longitudinal cracking. Approximately one-half (49 percent) of the fatigue damage was 

accumulated during the winter, while 42 percent occurred in the spring. The large fatigue 

damage in the winter is believed to be due to partial thawing of the pavement base that 

occurred during the winter season. The ratio of winter-to-fall roughness was 

approximately 1.7 to 1. A residual roughness was found to remain after the spring thaw. 

The increase in roughness is believed to be caused by frost heave. Based on the data 

collected, seasonal effects are not significant in terms of rutting damage. Last, the 

comparison of distress accumulated on insulated and noninsulated sections revealed that 

insulation is effective in reducing overall distress by 40 percent. 

DEVELOPMENT OF PERFORMANCE PREDICTION MODELS FOR DRY NO 

FREEZE AND DRY FREEZE ZONES USING LTPP DATA 

More specific to this project was a report by Senn, et al.(34) on “Development of 

Performance Prediction Models for Dry No Freeze and Dry Freeze Zones Using LTPP 

Data.” Using data from approximately 50 GPS sites in the LTPP Western Region, 

performance trends were developed based on roughness, fatigue cracking, transverse 

cracking, and rut depth. For purposes of analysis, the sites were categorized by climatic 

region and structural number. Only two climates were considered in this study (dry freeze 

and dry no-freeze). Similarly, two structural number levels were used to group the sites 

using a structural number of four as the breakpoint between the two levels. This was done 

to distinguish between the relatively thick and thin pavement sections. Regression 

analysis was performed using ESALs as the independent variable for all parameters with 

the exception of transverse cracking, which incorporated pavement age as the 

independent variable. 

From the analysis, fatigue cracking and transverse cracking both increased with traffic or 

time: 

• In the dry no-freeze zone, larger amounts of fatigue cracking were present in 

thinner pavements compared to thicker pavements. 

• The opposite was true for sites located in the dry freeze zone. 
146 

• Thinner pavements in the dry no-freeze zone were more susceptible to transverse 

cracking than thicker pavements. 

• The increase in roughness of sites located in the dry freeze region was more rapid 

than the increase in the dry no-freeze zone. 

• Similar to the other distress types, opposing roughness trends were found for thick 

and thin pavements in each climate. 

• Thin pavements experience a greater increase in roughness in the dry freeze 

climate, while the opposite is true for the dry no-freeze environment. 

• Rutting in the dry no-freeze climate was much greater than in the dry freeze 

climate. 

Although the preceding conclusions could be drawn from the data, the correlations were 

very poor, and many factors made the analysis difficult. In some cases, monitoring 

terminated before the failures in the pavement were exhibited. Without data on pavement 

structures near the end of their service life, developing a prediction model is very 

difficult. Adding to the difficulties was the variability in the field measurements. In 

particular, the difference between longitudinal cracking in the wheelpath and fatigue 

cracking is very subjective, resulting in drastically different amounts of fatigue cracking 

on the same section. Last, there is a large difference in the traffic levels experienced in 

the Western Region’s dry freeze and dry no-freeze zones. 

DETERMINATION OF THE CRITICAL THAW-WEAKENED PERIOD IN 

ASPHALT PAVEMENT STRUCTURES 

A study(35) was undertaken to evaluate the spring thaw period on a forest highway in 

northwestern Montana. By developing a methodology to accurately and easily determine 

the thaw conditions of the highway at various locations, responsible entities could place 

load restrictions at optimum times throughout the year. Air and subsurface soil 

temperature thermistors were installed at 17 sites equating to a density of one site every 

5.63 km (3.5 mi). The locations of the instrumentation were selected based on divisions 

of the highway that could be easily closed without affecting the remaining segments. 

Data were collected throughout the year with increased frequency during the spring 

season. 

The research attempted to predict the thaw condition based solely on soil temperature. 

Because of the presence of dissolved minerals in soil moisture, the soil will freeze at 

temperatures below 0 °C (32 °F). Therefore, laboratory testing was performed on eight 

soil samples typical of the region to determine the actually freezing temperature. An 

average value of −0.17 °C (31.7 °F) was determined from this testing and used in the 

analysis. In addition, pavement strength was measured using Benkelman beam deflection 

testing. 

Through the combination of temperature and strength testing the following results were 

obtained. The thawing period did not begin until a temperature of −0.17 °C (31.7 °F) was 

achieved at the base of the asphalt. It is interesting to note that this condition was not met 
147 

until the average soil temperature rose to approximately 1.11 °C (30 °F). Pavement 

strength data were used to determine the pavement strain, and thus, damage factors 

throughout the year. The end of the critical thaw weakening period was determined by 

identifying the time when the damage factor returned to unity, indicating the pavement 

strength returned to prefreeze levels. From this analysis, a thaw depth of approximately 

1.22 m (4 ft) was indicated as the end of the critical thaw period. These results are based 

on limited data from a very specific region of Montana. 

CALCULATED MAXIMUM FROST DEPTHS AT MN/ROAD WINTERS 1993–

1994, 1994–1995, AND 1995–1996 

The Modified Berggren equation (MBE) was used to estimate the maximum frost 

penetration experienced at 40 Mn/ROAD test sections over three winters.(36) These 

predictions were compared with the measured maximum frost penetration values 

obtained from electrical resistivity probes installed at the site. Further, sensitivity analysis 

was conducted to study the contribution of many factors on estimated frost depth. The 

factors considered in the analysis were pavement material properties, moisture content, 

density, layer thickness, and mean soil temperature, as well as factors dealing with 

thermal properties of materials. 

The results from the comparison were inconsistent between the three winters. For the 

1993–1994 winter, the difference between calculated and measured frost depth was 

within ±10 percent for most of the sections. Generally, the calculated values 

underestimated the actual frost depths measured. Similar results were obtained for the 

1995–1996 winter. Conversely, only approximately 50 percent of the data from the 

1994–1995 winter were within ±20 percent. The rest of the data exhibited greater 

differences. Using the MBE resulted in an overestimation of frost depth in the majority of 

the test sections for that winter. For all three winters, test sections with granular subgrade 

experienced measured frost depths far less than the estimated values. In this case, it is 

believed that errors in the frost penetration measurement caused the discrepancy. 

Several conclusions were drawn from the sensitivity analysis. The effect of small 

variations in layer thickness on estimated frost depth can be considered negligible. 

Sizeable deviation in moisture content will result in a change in calculated frost depth of 

less than 10 percent. One of the variables in the MBE is the n-factor, which is used to 

convert air freezing index to surface freezing index. The most appropriate n-factors were 

found to be 0.90 for flexible pavements and 0.95 for rigid pavements based on the 

conditions at Mn/ROAD. Thermal conductivity values were estimated using equations 

developed by Kersten. Better results of frost penetration were observed when the thermal 

conductivity estimates were increased by 25 percent. Last, a reduction of the estimated 

mean annual soil temperature from 11.1 °C to 9.4 °C (51.98 to 48.92 °F) resulted in 

better agreement of frost penetration data. If the changes in n-factors, thermal 

conductivity, and mean annual soil temperature are implemented, the majority of the 

calculated depths fall within ±13.3 (excluding granular subgrade data). 
148 

PARKS HIGHWAY LOAD RESTRICTION FIELD DATA ANALYSIS: A CASE 

STUDY 

In an effort to evaluate the effect of thaw weakening on pavement structures, eight test 

sections were monitored in 1993, 1995, and multiple times in 1996.(37) FWD, distress, rut, 

roughness, and traffic data were collected and used in the analysis. To monitor thaw 

propagation, hourly subsurface temperatures were collected to a depth of 1.93 m (6.33 ft) 

below the surface. Regression analysis was performed on the temperature data to develop 

thaw propagation models, which were used to determine the limits of the thaw period in 

the spring of 1996. For this study, the end of the thaw period was set as the time at which 

the thaw depth reached 1 m (3.28 ft). 

Results from distress data revealed that rutting and water bleeding were the most 

common forms of distress accumulated during the thaw period. Rutting occurred very 

rapidly, and it first occurred when the thaw depth was at approximately 0.3 m (0.98 ft). 

The southern lane, which carries 70 percent less EALs, was found to exhibit more 

damage than the northern lane. Dynamic effects of lighter weight traffic are believed to 

be the cause of this phenomenon. Rut depth and roughness were both found to be larger 

in the early spring than in the late spring. The improvement in remaining life based on 

fatigue damage obtained when load restrictions were enforced was determined using 

FWD. 

COMMON CHARACTERISTICS OF GOOD AND POORLY PERFORMING 

PCC PAVEMENTS 

A study(38) was undertaken to evaluate the factors contributing to both good and poor 

performance of PCC pavements. To conduct this analysis, definitions of good and poor 

performance needed to be determined in terms of specific distress types. This was 

achieved through the collaboration of a panel of experts who set limits on performance 

considering roughness, faulting, cracking, and localized failures such as a function of 

pavement age. Data from the LTPP database were the sole source of information used for 

this study. 

Based on the analysis, jointed plain concrete pavements (JPCP) were found to exhibit 

increased roughness values when subjected to multiple freeze-thaw cycles. In addition, 

sections in colder climates were rougher, as were sections in wetter climates. Seventy-one 

percent of the poor performing JPCP (in terms of roughness) sections had fine-grained 

subgrade. Similarly, all of the poor performing JRCP sections (roughness) were on finegrained subgrade. Increased subdrainage was also found to decrease roughness in three 

surface types: JPCP, JRCP, and continuously reinforced concrete pavements. 

Faulting of nondoweled JPCP was found to be higher in wet climates. This trend did not 

hold true for doweled JPCP or JRCP. Fine-grained soils contributed to increased faulting 

of both JPCP and JRCP. Adequate subdrainage resulted in lower amounts of faulting for 

both types of jointed concrete but particularly for nondoweled JPCP. 
149 

Transverse cracking of JPCP was found to occur more often in the western part of North 

America. It is believed that the increase in solar radiation in the west contributes to a 

larger thermal gradient through the slab leading to increased warping and curling. 

Cold and wet climates increased the amount of localized failures experienced in 

continually reinforced concrete pavement (CRCP); However, more information is 

required to fully understand all factors contributing to these failures. 

DETERMINATION OF FROST PENETRATION IN LTPP SECTIONS, FINAL 

REPORT 

In this study(39) data obtained from LTPP SMP sites were used to determine the presence 

and extent of frost penetration. Three types of electrical resistance measurements (2-point 

resistance, 4-point resistivity, and voltage drop) were used in conjunction with subsurface 

temperature profile data to estimate frost penetration. A computer program (FROST) was 

developed to assist in evaluating the temperature and electrical resistance data. 

Overall, temperature profile data produced reasonable and expected trends. Based on the 

concept of latent heat of fusion, two conditions were defined to indicate a phase change 

in subsurface material. The temperature must be less than or equal to 0 °C (32 °F), and it 

must remain constant for 2 days. Reasonable frost predictions were obtained using these 

criteria and the temperature data; however, some limitations exist using the established 

method. The 2-day minimum time period for constant temperature prevents short freezethaw cycles from being captured. On the other hand, temperature data from lower in the 

pavement structure usually exhibit less than 1 °C (33.8 °F) daily variation throughout the 

year. Therefore, the 2-day time period erroneously identifies phase changes in some 

cases. Because of these limitations, temperature data alone cannot be used in predicting 

phase changes. 

There is a large increase in resistivity when frozen conditions exist, thereby allowing for 

the identification of phase changes; however, many factors influence the bulk resistivity 

of soils: the type of soil, moisture content, dissolved salt concentration, and temperature. 

Identifying an increase in resistance due to frozen soil is very difficult. When comparing 

the three resistance measurement techniques it was discovered that agreement between all 

three methods occurred 60 percent of the time. Coupling this with the fact that each 

method had unique advantages and disadvantages, the use of all three techniques 

simultaneously would produce the most reliable results. Concurrently using all resistance 

data still produced large nonwinter, seasonal, and diurnal variability that could not predict 

phase changes with accuracy. 

As a result of the analyses of temperature and electrical resistance data, algorithms were 

developed to incorporate data from all three resistance measurement techniques, 

temperature data and time of year to identify the extent of frost penetration. These 

algorithms were packaged into the FROST program. The FROST program will normalize 

all three resistance measurements. The user must then select an appropriate threshold 

based on these normalized values. All data points below the threshold are considered to 
150 

be in the nonfreeze state. Data points above the threshold are analyzed further based on 

the average temperature. If the temperature is greater than zero, the data point is 

considered to be in the nonfreeze state. Conversely, temperatures below zero yield a data 

point classified in the freeze state. Graphs are then produced that visually depict the 

extent of frost throughout the monitoring period. 

The output from FROST was compare with historical data and found to be in reasonable 

agreement. Time domain reflectometry (TDR) data, also collected at LTPP SMP sites, 

was compared with the FROST output. TDR data provide information about the moisture 

content of soil; however, this information does not reflect frozen water. Therefore, a 

sudden drop in moisture content recorded by the TDR is expected during freeze periods. 

In almost all cases, prediction of frost by the program corresponded to a drop in moisture 

content recorded by TDR. The overall reliability of the freeze-state determination is 93 

percent. These results have been added to the LTPP IMS and can be found in two tables: 

SMP_FREEZE_STATE and SMP_FROST_PENETRATION. 

DEVELOPMENT OF A PAVEMENT RUTTING MODEL FROM 

EXPERIMENTAL DATA 

An empirical rutting progression model was developed using data from the AASHO 

Road Test.(40) One of the major differences between this and other models is the inclusion 

of a thawing index to capture the effects of the environment on rutting. Considerable 

accumulation of permanent deformation was experienced during spring thaw periods; 

therefore, including a thawing index seems logical. Furthermore, it is well accepted that 

the strength of unbound layers reduces considerably with excess moisture. The largest 

increase in moisture content of subsurface material is experienced during thaw periods. 

To determine a thawing index, some measure of freezing is necessary. An accumulated 

freeze index was utilized to quantify freezing and was based on the mean minimum 

temperature of each two week period. This index was combined with the mean maximum 

temperature over each two week period to determine the thawing index. The thawing 

index was then incorporated in the permanent deformation model by increasing the rate 

of rutting as a function of thawing index. 

Overall, the model was found to estimate rut depth with a standard error of regression of 

3.3 mm (0.13 inch). The predicted rut depth values compare very well to the observed rut 

depth measurements for sections used in the model development. In addition, actual 

rutting measurements from other sections, not used in the model development, were 

compared with predicted values and found to be in agreement, further confirming the 

model. Note that this model is specific to the conditions of the AASHO road test. 
151 

ANALYSIS OF EXPERIMENTAL PAVEMENT FAILURE DATA USING 

DURATION MODELS 

A study(41) was undertaken to develop a new pavement performance equation using 

duration models based on data from the AASHO road test. Duration models incorporate 

the variable nature of pavement failure as well as accounting for censoring and truncation 

biases, which are introduced into models when failure events are not observed due to 

limited duration data collection. In some instances, pavement failure is reached before the 

monitoring period begins, while other failures occur after the completion of the 

monitoring period. Although these data points were not observed, they need to be 

accounted for in model development. An extension of the Weibull model can be used to 

eliminate this bias. 

From the Weibull model, the rate at which pavement failure will occur after a given time 

can be estimated using the hazard rate function. In addition, the survival function can be 

used to estimate the probability that a pavement will last longer than a given time period. 

In turn, the probability distribution can be determined using the previous two functions. 

Of the three models discussed above, only the hazard rate function was used in this study. 

For comparison, a model using the hazard function with the same variables as the original 

AASHO equation was developed to predict pavement life. Results from this duration 

model were compared with the predicted values of the original model using data from the 

AASHO road test. Overall, the life prediction from the new duration model estimated the 

life of the test section with more accuracy than the original model. The standard error of 

the new model was 0.42 compared with 0.65 from the original model. However, the 

duration model overestimated the life of pavement structures that failed relatively early in 

the testing period. Conversely, the model underestimated the pavement life of pavement 

structures failing relatively late. By the end of the testing phase, 237 test sections reached 

failure. The new equation predicted 253 failures with an error of 6.8 percent, while the 

original equation predicted 215, which equates to an error of –9.3 percent. 

PAVEMENT PERFORMANCE DURING THAW WEAKENING 

Test sections in the Frost Effects Research Facility at the Cold Regions Research and 

Engineering Laboratory were loaded with a 133-kN wheel load during a simulated 

thawing period.(42) Using in situ measurement equipment, base and subgrade responses 

such as stress, strain, resilient modulus, and permanent deformation were monitored 

before and throughout one freeze-thaw cycle to quantify the changes induced by this 

environmental process. In addition, the timing of these changes was of interest to 

researchers to define critical periods in the cycle. Results from this research were used to 

evaluate the validity of the U.S. Army Corps of Engineers Modulus Reduction Factors 

for Frost-Susceptible Soils. This table is used for design to adjust modulus values 

obtained during frost-free conditions to reflect values experienced during thawing 

periods. 
152 

From this testing, the base was found to experience a 67 percent maximum reduction in 

resilient modulus during the thaw-weakening period, which was approximately 2 weeks. 

Similarly, the subgrade experienced a reduction of 56 percent over a 3-week period. The 

vertical strain in the base reached a maximum level 15 days into the thawing phase, 

which was approximately 530 percent of the prefreeze value. The strain never fully 

recovered, remaining at 160 percent on completion of the cycle. The subgrade behaved in 

the same manner, reaching a maximum strain 1,100 percent higher than the prefreeze 

strains 15 days into the thaw process and recovering to only 241 percent. The stress 

values followed similar patterns. The minimum stress did occur at the same time as the 

maximum strain in the subgrade but not in the base layer. Permanent deformation was 

found to increase rapidly within the first 10 days. Based on these findings, the reduction 

factors used in the design process significantly overestimate the stiffness of both the base 

and subgrade material during the thawing period. 

EFFECTS OF FROST HEAVE ON THE LONGITUDINAL PROFILE OF 

ASPHALT CONCRETE PAVEMENTS IN COLD REGIONS 

The cyclic increase of pavement roughness in roadways subjected to frost penetration 

prompted a study of the change in IRI on a section of the Doto Expressway in east 

Hokkaido, Japan.(16) The section of road was divided into 31 consecutive sections each 

with a length of 1 km (0.62 mi). The IRI values were obtained from longitudinal profile 

data collected using an inertial profiler. Profiles were collected once in August 1999 and 

once in November 1999 to establish prefrost values. Between February and April 2000, 

longitudinal profile data were collected once a week to monitor the change in IRI during 

the propagation of frost penetration and subsequent thaw period. Weather data were also 

gathered from a weather station adjacent to the test sections. Using a modified Berggren’s 

formula with this data and knowledge of the subsurface material, the depth of frost 

penetration was estimated to reach a maximum in late March 2000. In addition, frost 

penetration was estimated to reach the subgrade layer on February 11, 2000. 

The IRI in the summer and fall were found to be similar and considerably less than the 

winter IRI values. The winter IRI values increased as the temperature decreased, and they 

reached a maximum value in early to mid-March just before the maximum depth of frost 

penetration was achieved. The spring IRI values appeared to have returned the level of 

the prefreeze values of the summer and fall. For further analysis, the test sections were 

divided into 100-m (328.1-ft) segments and categorized into following three groups based 

on the nature of material under the pavement structure: cut, embankment, and bridge. The 

average IRI values in each category were compared and the cut sections were found to 

exhibit the greatest increase in roughness during the frost penetration. Additional 

evaluation of the six sections demonstrating the largest increase in IRI established a 

linear relationship between IRI and freezing index. 

THERMAL ASPECT OF FROST-THAW PAVEMENT DIMENSIONING: IN 

SITU MEASUREMENT AND NUMERICAL MODELING 

Multiple models have been developed to forecast the propagation of frost and resultant 

heave of roadways both in Quebec and France. In order to validate these models, in this 
153 

study(43) four test sections were constructed and monitored for 3 years. The test sections 

were equipped with temperature sensors, TDRs, frost tubes, piezometers, and heaving 

sensors to monitor frost depth as well as the amount of heaving in the subsurface layers 

of the pavement structure. Weather data were obtained from a weather station in the 

nearby vicinity. Two pavement structures commonly constructed in France were selected 

for the monitoring sections. Two test sections conforming to each typical pavement 

structure were constructed; however, only one test section of each structure was insulated 

with extruded polystyrene. This allowed for the evaluation of the effect of frost on 

pavement performance while keeping all other variables (i.e., traffic, natural subgrade, 

climate, and pavement structure) constant. Furthermore, the efficiency of the insulation 

could also be assessed. 

Frost depth and soil heave estimates from the SSR, GEL1D, and CESAR-GELS models 

were compared with actual values obtained for the onsite monitoring equipment. The 

SSR model is based on thermal equilibrium at the frost front, and it estimates soil heave 

using water migration as a function of thermal gradient. The GEL1D and CESAR-GELS 

models are similar; they use finite element analysis as the foundation for frost-depth 

estimation. These two models do not incorporate heave estimates into their frost-depth 

calculations. 

All three models were found to estimate frost penetration within 10 percent of the values 

recorded onsite. Because the GEL1D and CESAR-GELS models are dependent on initial 

conditions and do not incorporate frost heave, substantial differences between the models 

were observed. The SSR model was found to predict frost heave within the same order of 

magnitude with the maximum difference between predicted and measured values found 

to be 7 mm (0.276 inch). The ability of the insulation layer to protect subsurface 

materials from frost penetration was confirmed. 

PROBABILISTIC ANALYSIS OF HIGHWAY PAVEMENT LIFE FOR 

ILLINOIS 

Survival analysis has been performed four times over the past 15 years on the majority of 

the freeway system in Illinois. This analysis(44) provides the probability of failure as a 

function of age or cumulative ESALs. Original pavement structures were categorized by 

pavement type and thickness. JRCP and CRCP were further separated with the presence 

of durability cracking. All pavement types were also classified by geographical location. 

Receiving rehabilitation treatment was defined as the failure criteria. Due to the length of 

the study, some pavement structures experienced failure multiple times, with some 

sections receiving three overlays; therefore, overlays were also included in the analysis. 

Durability cracking greatly reduced the life expectancy of both JRCP and CRCP sections 

in the southern region of Illinois. JRCP sections without durability cracking are expected 

to carry 30 percent more ESALs than sections with durability cracking before reaching 

the 50th percentile life expectancy. Similar results were found in CRCP sections with a 

reduction in cumulative ESALs ranging between 32 and 63 percent. Thick asphalt cement 

overlays placed over both JRCP and CRCP experienced a larger reduction in life 

expectancy due to durability cracking compared with thin overlays. 
154 

One major downfall of this study was the failure criteria selected. Due to budgetary and 

other issues, rehabilitation activities are not performed on roads with consistent 

conditions. One road may receive treatment 1 year after the pavement has reached a poor 

condition, while other roads may be overlaid within 1 month of deteriorating to a similar 

condition. Further, some sections receive rehabilitation before failure occurs, introducing 

censoring bias. 

EFFECTS OF ENVIRONMENTAL FACTORS ON PAVEMENT 

PERFORMANCE-THE INITIAL EVALUATION OF THE LTPP SPS-8 

EXPERIMENT 

Using data available from the LTPP database, a study was conducted to examine the 

effects of climate and subgrade on pavements subjected to limited loading.(45) All SPS-8 

projects, monitored under LTPP, were considered in this analysis. Each site was 

categorized based on climate (precipitation and temperature), subgrade type, pavement 

structure, and age. Distress and roughness data were the only parameters evaluated in the 

study. 

The research effort revealed that sections constructed on active subgrade soil in wetfreeze climates exhibited significantly more nonwheelpath longitudinal cracking. 

Statistical analysis of pavement roughness showed that subgrade type was the most 

influential factor for flexible pavements, while precipitation was most important in rigid 

pavements. It should be noted that these statements were not statistically significant at the 

95 percent confidence interval. Sections constructed on active subgrade (in any climate) 

had the highest average IRI. 

LTPP DATA ANALYSIS: INFLUENCE OF DESIGN AND CONSTRUCTION 

FEATURES ON THE RESPONSE AND PERFORMANCE OF NEW FLEXIBLE 

AND RIGID PAVEMENTS 

LTPP data from the SPS-1, SPS-2, and SPS-8 experiments were used to study the relative 

influence of structural design factors as well as site conditions on pavement 

performance.(46) The SPS-1 projects were used to investigate HMA layer thickness, base 

type, base thickness, and drainage. The SPS-2 experiments were evaluated to determine 

the effects of PCC slab thickness, flexural strength, base type, drainage, and slab width. 

The SPS-8 experiments were used to look into environmental effects without the 

contribution of heavy traffic. Performance comparisons were made for various surface 

distress types as well as roughness, rutting (HMA), and transverse joint faulting (PCC). 

The results from this study can be used to improve and implement design procedures that 

make better use of design options. Also, the contribution of environment and site 

conditions can be evaluated. Following are a few of the findings from the final report: 

• Based on the data evaluated, drainage improves rutting performance of pavements 

with dense graded aggregate base (DGAB) in wet no freeze climates. 

• Pavements with underlying asphalt treated base (ATB) exhibit the lowest 

accumulation of fatigue cracking. 
155 

• PCC pavements with fine-grained subgrade soils have higher accumulations of 

transverse cracking as compared to coarse-grained subgrades, although the 

difference is only marginally significant based on statistics. 

• Because SPS-8 projects have accumulated low amounts of distress to date, 

pavement with frost-susceptible subgrade have more longitudinal cracking, 

transverse cracking, and fatigue cracking compared with other soil types. 

157 

APPENDIX B. PERFORMANCE PREDICTION MODELS 

This appendix provides details on the prediction models for each of the performance 

measures evaluated in the study. In addition, the use of each model is illustrated through 

examples. Following is a list of the performance measures: 

IRI = Pavement roughness (m/km (ft/mi))—flexible and rigid. 

RUT = Rut depth (mm (inch))—flexible. 

FWPC = Fatigue and wheelpath cracking (deduct value)—flexible. 

fwpc = Fatigue and wheelpath cracking (percentage of wheelpath area)—flexible. 

TC = Transverse cracking (deduct value)—flexible. 

TC = Transverse cracking (percentage of total section area)—rigid. 

LC = Longitudinal cracking (percentage of total section area)—rigid. 

FLT = Transverse joint faulting (mm (inch))—rigid. 

Following is a list of the explanatory variables input into the equations: 

ACTHICK = Thickness of AC layer (mm (inch)). 

ADJ_AGE = Pavement age after distress initiation (years). 

AGE = Pavement age (years). 

BASE = Base type (DGAB, ATB, PATB, LCB, NONBIT, NONE). 

CI = Cooling index (degree-Celsius days). 

DEPTH = Thickness of PCC layer (mm (inch)). 

FC = Functional classification of roadway. 

FI = Freezing index (degree-Celsius days). 

FTC = Annual number of freeze-thaw cycles (each). 

LESN = Logarithm of annual ESAL divided by structural number. 

LEDT = Logarithm of annual ESAL divided by PCC layer thickness. 

MIRI = Initial IRI (m/km (ft/mi)). 

MIRI_AGE = Pavement age when MIRI was recorded (years). 

Pavement Structure = AC nonoverlay with unbound base, AC nonoverlay with bound 

base, AC overlay (either bound or unbound base), JPCC. 

PRECIP = Annual precipitation (mm (inch)). 

SG = Subgrade classification (FINE, COARSE, ROCK/STONE, OTHER). 
158 

ABSOLUTE IRI PREDICTION MODEL FOR FLEXIBLE PAVEMENTS 

Equation 12 shows the IRI regression equation (R-squared = 0.78, total observations = 

4,544) and equation 13 defines the delta equation. 

( )

( )

MIRI AGE AGE PRECIP AGE FTC

AGE AGE CI AGE FI MIRI

SN

ESAL

PRECIP FTC ACTHICK

AGE CI FI

SN

ESAL (IRI . ) α

2.50 10 * _ 2.40 10 * * 1.09 10 * *

* 1.77 10 * * 4.55 10 * * 0.643* log 5.39 10 *

5.90 10 * 2.21 10 * 8.59 10 *

3.29 10 * 4.33 10 * 2.28 10 * log ln 01 0.115*

2 6 5

3 6 6

5 4 5

2 5 6

− − −

− − −

− − −

− − −

− × − × − ×

⎟ + × + × + ⎠

⎞ ⎜

⎝

⎛ − ×

+ × + × + ×

⎟ + × − × + × ⎠

⎞ ⎜

⎝

⎛ + = −Δ+

(12) 

Where: 

( )

( )

MIRI AGE FTC (MIRI . )

MIRI AGE FI MIRI MIRI AGE PRECIP

MIRI AGE MIRI AGE CI

SN

ESAL

PRECIP FTC ACTHICK

MIRI AGE CI FI

SN

ESAL

α

1.09 10 * _ * ln 01

4.55 10 * _ * .643 * 2.40 10 * _ *

* _ 1.77 10 * _ * log 5.39 10 *

5.90 10 * 2.21 10 * 8.59 10 *

7.90 10 _ 4.33 10 * 2.28 10 * log 0.115 *

5

6 6

3 6

5 4 5

3 5 6

− × − +

+ × + − ×

⎟ + × ⎠

⎞ ⎜

⎝

⎛ − ×

+ × + × + ×

⎟ + × − × + × ⎠

⎞ ⎜

⎝

⎛ Δ = +

−

− −

− −

− − −

− − −

(13) 

Table 37 shows the coefficients for a flexible IRI model. 
159 

Table 37. Coefficients for flexible IRI model. 

α Values for FINE SUBGRADE 

BASE 

Nonoverlay, 

Unbound Base 

Nonoverlay, 

Bound Base 

Overlay, Bound 

or Unbound Base 

ATB NA −0.714 −0.759 

DGAB −0.713 NA −0.753 

LCB NA −0.658 −0.702 

NONBIT NA −0.674 −0.718 

NONE −0.658 NA −0.698 

PATB NA −0.734 −0.779 

α Values for COARSE SUBGRADE 

ATB NA −0.77 −0.815 

DGAB −0.769 NA −0.810 

LCB NA −0.714 −0.759 

NONBIT NA −0.730 −0.774 

NONE −0.714 NA −0.755 

PATB NA −0.790 −0.835 

α Values for ROCK/STONE SUBGRADE 

ATB NA −0.671 −0.716 

DGAB −0.670 NA −0.711 

LCB NA −0.615 −0.660 

NONBIT NA −0.631 −0.676 

NONE −0.615 NA −0.656 

PATB NA −0.691 −0.736 

Example of Absolute IRI Prediction Model for Flexible Pavements 

As discussed in the main body of the report, the flexible IRI prediction model 

incorporates a delta factor that shifts the model to correspond with the measured initial 

IRI value (MIRI). The first step in predicting IRI for flexible pavements is to calculate 

that delta factor. With this factor determined, the IRI can be estimated from the 

regression equation. A pseudopavement section was fabricated with the explanatory 

conditions in table 38 for use as an example. 
160 

Table 38. Example pavement section information. 

Input Value 

Pavement Structure Nonoverlay, 

Unbound Base 

BASE DGAB 

SG FINE 

ESAL 126,000 

SN 5.0 

ACTHICK 6.5 

FTC 80 

FI 688 

CI 205 

PRECIP 1,140 

MIRI_AGE 1 

MIRI 1 

Age 2 

Substituting the inputs from table 38 into the delta equation and extracting the alpha 

value from table 37 based on pavement structure, base type, and subgrade type appears in 

equation 14. 

( )

( )

1.09 10 *1*80 ln(1 0.1)

4.55 10 *1*688 .643*1 2.40 10 *1*1140

*1 1.77 10 *1*205

5

log126000 5.39 10 *

5.90 10 *1140 2.21 10 *80 8.59 10 *6.5

7.90 10 *1 4.33 10 *205 2.28 10 *688

5

log126000 0.713 0.115*

5

6 6

3 6

5 4 5

3 5 6

− × − +

+ × + − ×

⎟ + ×

⎠

⎞ ⎜

⎝

⎛ − ×

+ × + × + ×

⎟ + × − × + ×

⎠

⎞ ⎜

⎝

⎛ Δ = − +

−

− −

− −

− − −

− − −

(14) 
161 

Therefore, the result is equation 15. 

Δ = 0.032 (15)

Using this delta and the regression equation, the result is equation 16. 

( )

( )

4.55 10 *2*688 0.643*1 2.50 10 *1 2.40 10 *2*1140 1.09 10 *2*80

*2 1.77 10 *2*205

5

log 126000 5.90 10 *1140 2.21 10 *80 8.59 10 *6.5 5.39 10 *

3.29 10 *2 4.33 10 *205 2.28 10 *688

5

log 126000 ln 01 0.713 0.032 0.115*

6 2 6 5

5 4 5 3 6

2 5 6

− − − −

− − − − −

− − −

+ × + − × − × − ×

⎟ + ×

⎠

⎞ ⎜

⎝

⎛ + × + × + × − ×

⎟ + × − × + ×

⎠

⎞ ⎜

⎝

⎛ (IRI + . ) = − − +

 (16) 

Therefore, the results appear in equations 17 and 18. 

 (17) 

 (18) 

ABSOLUTE IRI PREDICTION MODEL FOR RIGID PAVEMENTS 

Equation 19 shows the IRI regression equation (R-squared=0.78, total 

observations=2,652) for rigid pavements. 

( )

FI FTC CI FTC FI CI FI AGE

PRECIP FTC MIRI AGE AGE

FI CI

DEPTH

ESAL (IRI . ) α MIRI

2.30 10 * * 8.11 10 * * 3.57 10 * * 4.30 10 * *

4.17 10 * 7.76 10 * 1.64 10 * _ 1.68 10 *

3.21 10 * 1.84 10 * log ln 01 0.501* 9.85 10 *

6 8 7 6

5 4 2 2

2 4 5

− − − −

− − − −

− − −

− × + × − × + ×

+ × + × − × + ×

⎟ + × − × ⎠

⎞ ⎜

⎝

⎛ + = + + ×

 (19) 

Table 39 lists the coefficients for a rigid IRI model. 

0.1 1.03

ln 01 0.123

0.123 = − =

+ =

IRI e

(IRI . )
162 

Table 39. Coefficients for rigid IRI model. 

α Values for FINE SUBGRADE 

 ATB Base DGAB Base LCB Base NONBIT Base PATB Base 

FC 1 −0.478 −0.499 −0.497 −0.413 −0.539 

FC 2 −0.488 −0.510 −0.507 −0.423 −0.550 

FC 6 −0.529 −0.551 −0.548 −0.464 −0.591 

FC 7 −0.492 −0.514 −0.511 −0.427 −0.554 

FC 9 −0.402 −0.424 −0.421 −0.337 −0.464 

FC 11 −0.455 −0.476 −0.473 −0.389 −0.516 

FC 12 −0.469 −0.491 −0.488 −0.404 −0.531 

FC 14 −0.526 −0.547 −0.544 −0.460 −0.587 

FC 17 −0.408 −0.430 −0.427 −0.343 −0.470 

α Values for COARSE SUBGRADE 

FC 1 −0.475 −0.497 −0.494 −0.410 −0.537 

FC 2 −0.486 −0.507 −0.505 −0.420 −0.547 

FC 6 −0.527 −0.548 −0.545 −0.461 −0.588 

FC 7 −0.489 −0.511 −0.508 −0.424 −0.551 

FC 9 −0.400 −0.421 −0.419 −0.334 −0.461 

FC 11 −0.452 −0.473 −0.471 −0.387 −0.513 

FC 12 −0.467 −0.488 −0.485 −0.401 −0.528 

FC 14 −0.523 −0.544 −0.542 −0.458 −0.584 

FC 17 −0.406 −0.427 −0.424 −0.340 −0.467 

α Values for ROCK/STONE SUBGRADE 

FC 1 −0.540 −0.562 −0.559 −0.475 −0.602 

FC 2 −0.551 −0.573 −0.570 −0.486 −0.612 

FC 6 −0.592 −0.614 −0.611 −0.527 −0.653 

FC 7 −0.555 −0.576 −0.574 −0.490 −0.616 

FC 9 −0.465 −0.487 −0.484 −0.400 −0.526 

FC 11 −0.517 −0.539 −0.536 −0.452 −0.579 

FC 12 −0.532 −0.553 −0.551 −0.467 −0.593 

FC 14 −0.588 −0.610 −0.607 −0.523 −0.650 

FC 17 −0.471 −0.493 −0.490 −0.406 −0.532 
163 

Table 39. Coefficients for rigid IRI model (continued). 

α Values for OTHER SUBGRADE 

FC 1 −0.482 −0.504 −0.501 −0.417 −0.544 

FC 2 −0.493 −0.514 −0.512 −0.428 −0.554 

FC 6 −0.534 −0.555 −0.553 −0.468 −0.595 

FC 7 −0.497 −0.518 −0.515 −0.431 −0.558 

FC 9 −0.407 −0.428 −0.426 −0.341 −0.468 

FC 11 −0.459 −0.481 −0.478 −0.394 −0.520 

FC 12 −0.474 −0.495 −0.492 −0.408 −0.535 

FC 14 −0.530 −0.551 −0.549 −0.465 −0.591 

FC 17 −0.413 −0.434 −0.431 −0.347 −0.474 

Example of Absolute IRI Predictions Model for Rigid Pavements 

The rigid IRI model does not incorporate a delta factor; therefore, the regression equation 

can be used directly. The pavement structure shown in table 40 illustrates the use of the 

regression model. 

Table 40. Example pavement section information. 

Input Value 

Pavement Structure JPCC 

BASE DGAB 

SG FINE 

ESAL 410,000 

FC 2 

D 9.5 

FTC 80 

FI 688 

CI 205 

PRECIP 1,140 

MIRI_AGE 1 

MIRI 1 

AGE 2 

Substituting the inputs from table 40 into the regression equation and extracting the alpha 

value from the table 39 based on pavement structure, base type, and subgrade type leads 

to equation 20. 
164 

( )

2.30 10 *688*80 8.11 10 *205*80 3.57 10 *688*205 4.30 10 *688*2

4.17 10 *1140 7.76 10 *80 1.64 10 *1 1.68 10 *2

3.21 10 *688 1.84 10 *205

9.5

log 410000 ln 01 0.510 0.501*1 9.85 10 *

6 8 7 6

5 4 2 2

2 4 5

− − − −

− − − −

− − −

− × + × − × + ×

+ × + × − × + ×

⎟ + × − × ⎠

⎞ ⎜

⎝

⎛ (IRI + . ) = − + + ×

 (20) 

Equations 21 and 22 show the results. 

 (21) 

 (22) 

FWPC PREDICTION MODEL FOR FLEXIBLE PAVEMENTS (DEDUCT 

VALUE) 

Equation 23 shows the logistic FWPC prediction model for flexible pavements (total 

observations = 1977). 

( )

( ) AGE CI AGE FI

SN

ESAL AGE

CI PRECIP AGE

FTC FI

SN

ESAL

α

P

P

1.15 10 * * 1.44 10 * * log 6.10 10 * *

2.21 10 * 1.01 10 * 0.513 *

1.12 10 * 1.06 10 * log 0.917 *

1

ln

2 4 4

4 3

2 4

1

− − −

− −

− −

⎟ − × − ×

⎠

⎞ ⎜

⎝

⎛ − ×

− × − × +

⎟ − × + ×

⎠

⎞ ⎜

⎝

⎛ ⎟ = + ⎠

⎞ ⎜

⎝

⎛

−

(23) 

Table 41 lists the coefficients for the flexible FWPA logistic model. 

Table 41. Coefficients for flexible FWPC (deduct value) logistic model. 

α 1 Values 

Nonoverlay, Unbound Base −1.06 

Nonoverlay, Bound Base −0.635 

Overlay, Bound or Unbound Base 0.101 

Where crack initiation age is determined as the AGE at which P, cutoff probability, 

equals 0.7 (percent correct = 72.6). If the crack initiation age is determined to be less than 

zero from the logistic model, a value of zero is used. 

Equation 24 shows the regression equation (R-squared = 0.63, total observations = 

1,486). 

0.1 1.15

ln 01 0.223

0.223 = − =

+ =

IRI e

(IRI . )
165 

( ) ( )

( ) ADJ AGE PRECIP

CI FI PRECIP FTC

ADJ AGE

SN

ESAL (FWPC . ) α

2.14 10 * ln _ 0.1 *

8.77 10 * 3.59 10 * 1.71 10 * 1.12 10 *

0.875 * ln _ 0.1 log ln 01 3.65 10 *

4

4 4 4 2

3

2

+ × +

− × − × − × − ×

⎟ + + ⎠

⎞ ⎜

⎝

⎛ + = − ×

−

− − − −

−

(24) 

Table 42 lists the coefficients for the flexible FWPC regression model. 

Table 42. Coefficients for flexible FWPC (deduct value) 

regression model. 

α 2 Values for FINE SUBGRADE 

BASE Nonoverlay, Nonoverlay, Overlay, Bound 

ATB NA 3.13 2.88 

DGAB 3.25 NA 2.94 

LCB NA 2.79 2.54 

NONBIT NA 2.76 2.51 

NONE 2.91 NA 2.60 

PATB NA 3.06 2.81 

α 2 Values for COARSE SUBGRADE 

ATB NA 2.88 2.63 

DGAB 3.00 NA 2.68 

LCB NA 2.54 2.29 

NONBIT NA 2.51 2.25 

NONE 2.65 NA 2.34 

PATB NA 2.81 2.56 

α 2 Values for ROCK/STONE SUBGRADE 

ATB NA 3.17 2.91 

DGAB 3.28 NA 2.97 

LCB NA 2.82 2.57 

NONBIT NA 2.79 2.54 

NONE 2.94 NA 2.63 

PATB NA 3.09 2.84 

Where ADJ_AGE equals pavement age minus crack initiation age. If the pavement age is 

less than or equal to the crack initiation age, the predicted FWPC equals zero. 
166 

FWPC PREDICTION MODEL FOR FLEXIBLE PAVEMENTS (PERCENTAGE 

WHEELPATH AREA) 

Equation 25 gives the logistic model (total observations = 1977). 

( )

( ) AGE CI AGE FI

SN

ESAL AGE

CI PRECIP AGE

FTC FI

SN

ESAL

α

P

P

1.15 10 * * 1.44 10 * * log 6.10 10 * *

2.21 10 * 1.01 10 * 0.513 *

1.12 10 * 1.06 10 * log 0.917 *

1

ln

2 4 4

4 3

2 4

1

− − −

− −

− −

⎟ − × − ×

⎠

⎞ ⎜

⎝

⎛ − ×

− × − × +

⎟ − × + ×

⎠

⎞ ⎜

⎝

⎛ ⎟ = + ⎠

⎞ ⎜

⎝

⎛

−

(25) 

Table 43 lists the coefficients for the flexible FWPC logistic model. 

Table 43. Coefficients for flexible FWPC 

(percentage of wheelpath) logistic model. 

α 1 Values 

Nonoverlay, Unbound Base −1.06 

Nonoverlay, Bound Base −0.635 

Overlay, Bound or Unbound Base 0.101 

Where the crack initiation age is determined as the AGE at which P, cutoff probability, 

equals 0.7 (percent correct = 72.6). If the crack initiation age is determined to be less than 

zero from the logistic model, a value of zero is used. 

Equation 26 shows the regression equation (R-squared = 0.63, total observations = 1486). 

( ) ( )

( ) ADJ AGE PRECIP

CI FI PRECIP FTC

ADJ AGE

SN

ESAL (fwpc . ) α

2.07 10 * ln _ 0.1 *

1.04 10 * 4.12 10 * 2.03 10 * 1.12 10 *

0.737 * ln _ 0.1 log ln 01 7.63 10 *

4

3 4 5 2

2

2

+ × +

− × − × + × − ×

⎟ + + ⎠

⎞ ⎜

⎝

⎛ + = + ×

−

− − − −

−

(26) 

Table 44 lists the coefficients for the flexible FWPC regression model. 
167 

Table 44. Coefficients for flexible FWPC (percentage of wheelpath) 

regression model. 

α 2 Values for FINE SUBGRADE 

BASE Nonoverlay, 

Unbound Base 

Nonoverlay, 

Bound Base

Overlay, Bound, 

or Unbound Base 

ATB NA 2.43 1.97 

DGAB 2.51 NA 2.14 

LCB NA 2.80 2.33 

NONBIT NA 1.97 1.51 

NONE 2.15 NA 1.78 

PATB NA 2.45 1.98 

α 2 Values for COARSE SUBGRADE 

ATB NA 2.06 1.59 

DGAB 2.14 NA 1.77 

LCB NA 2.43 1.96 

NONBIT NA 1.60 1.13 

NONE 1.77 NA 1.40 

PATB NA 2.08 1.61 

α 2 Values for ROCK/STONE SUBGRADE 

ATB NA 2.11 1.64 

DGAB 2.19 NA 1.81 

LCB NA 2.47 2.01 

NONBIT NA 1.65 1.18 

NONE 1.82 NA 1.45 

PATB NA 2.12 1.66 

Where ADJ_AGE = pavement Age—crack initiation age. If the pavement age is less than 

or equal to the crack initiation age, the predicted FWPC equals zero. 

Example for FWPC Prediction Model for Flexible Pavements 

The prediction model for FWPC involves a two-step process. In the first step, the logistic 

equation in equation 25 is used to estimate the age of crack initiation. This is done 

solving for the AGE variable so that P is equivalent to the cutoff value (0.7 for this 

model). This AGE is denoted as the crack initiation age. In some cases, the resulting 

crack initiation age may be less than zero. A value of zero is used in place of the 

estimated negative crack initiation age. 

If the FWPC estimate is for a pavement age less than or equal to the crack initiation age, 

the predicted FWPC value is equal to zero. If the FWPC estimate is for a pavement age 
168 

greater than the crack initiation age, the ADJ_AGE is calculated as the pavement age of 

interest less the crack initiation age. Table 45 lists information for an example pavement 

section. 

Table 45. Example pavement section information. 

Equation 27 substitutes information from table 45 as well as a P value of 0.7 into the 

logistic equation and extracting the α 1 value from the appropriate table. 

( )

( )

1.44 10 * *688

1.15 10 * *205

5

log 126000 1.01 10 *1140 0.513* 6.10 10 * *

1.12 10 *80 1.06 10 *688 2.21 10 *205

5

log 126000 1.06 0.917*

1 0.7

0.7 ln

4

3 2 4

2 4 4

AGE

AGE AGE AGE

−

− − −

− − −

− ×

⎟ − ×

⎠

⎞ ⎜

⎝

⎛ − × + − ×

⎟ − × + × − ×

⎠

⎞ ⎜

⎝

⎛ ⎟ = − + ⎠⎞ ⎜

⎝

⎛

−

(27) 

Equation 28 is solving for AGE through an iterative process. 

AGE = 9.13 = crack initiation age (28) 

FWPC prediction of interest occurs at a pavement age of 12. Therefore, ADJ_AGE = 

12−9.13 = 2.87. Equation 29 substitutes this age and the information from table 45 into 

the regression equation. 

Input Value 

Pavement Structure Nonoverlay, Unbound Base 

BASE DGAB 

SG FINE 

ESAL 126,000 

SN 5.0 

ACTHICK 6.5 

FTC 80 

FI 688 

CI 205 

PRECIP 1,140 

MIRI_AGE 1 

MIRI 1 

AGE 12 
169 

( ) ( )

2.14 10 * ln( ) 2.87 0.1 *1140

8.77 10 * 205 3.59 10 * 688 1.71 10 *1140 1.12 10 * 80

0.875 * ln 2.87 0.1

5

log 126000 ln 01 3.25 3.65 10 *

4

4 4 4 2

3

+ × +

− × − × − × − ×

⎟ + + ⎠

⎞ ⎜

⎝

⎛ + = − ×

−

− − − −

− (FWPC . )

(29) 

Therefore, the results are shown in equations 30 and 31. 

 (30) 

 (31) 

TC PREDICTION MODEL FOR FLEXIBLE PAVEMENTS 

Equation 32 shows the logistic model (observations = 1,920). 

( )

( )

AGE FI AGE PRECIP AGE FTC

AGE CI

SN

ESAL PRECIP AGE AGE

FTC FI CI

SN

ESAL

α

P

P

1.92 10 * * 7.72 10 * * 1.56 10 * *

1.86 10 * * log 1.81 10 * 0.512* 2.88 10 * *

1.34 10 * 2.65 10 * 1.30 10 * log 0.723*

1

ln

4 5 3

4 2 4

2 3 3

1

− − −

− − −

− − −

− × − × + ×

⎟ − ×

⎠

⎞ ⎜

⎝

⎛ + × + − ×

⎟ − × + × + ×

⎠

⎞ ⎜

⎝

⎛ ⎟ = + ⎠⎞ ⎜

⎝

⎛

−

(32) 

Table 46 lists the coefficients for the flexible TC logistic model. 

Table 46. Coefficients for flexible TC logistic model. 

α 1 Values 

Nonoverlay, Unbound Base −3.75 

Nonoverlay, Bound Base −3.29 

Overlay, Bound or Unbound Base −1.97 

Where the crack initiation age is determined as the AGE at which P, cutoff probability, 

equals 0.7 (percent correct = 78.4). If the crack initiation age is determined to be less than 

zero from the logistic model, a value of zero is used. 

Equation 33 is the regression equation (R-squared = 0.71, total observations = 1,077). 

0.1 18.9

ln 01 2.95

2.95 = − =

+ =

FWPC e

(FWPC . )
170 

( ) ( )

( )

1.88 10 * * ln( ) 0.1

5.28 10 * 1.10 10 * 1.12 10 * ln 0.1 *

1.14 * ln 0.1 3.16 10 * log ln 01 0.175 *

4

4 4 4

4

2

− × +

+ × − × + × +

⎟ + + − × ⎠

⎞ ⎜

⎝

⎛ + = +

−

− − −

−

CI AGE

FI PRECIP AGE PRECIP

AGE CI

SN

ESAL (TC . ) α

 

(33) 

Table 47 lists the coefficients for the flexible TC regression model. 

Table 47. Coefficients for flexible TC regression model. 

α 2 Values for FINE SUBGRADE 

BASE 

Nonoverlay, 

Unbound Base 

Nonoverlay, Bound 

Base 

Overlay, Bound 

or Unbound Base 

ATB NA 1.48 1.10 

DGAB 1.03 NA 0.996 

LCB NA 1.58 1.20 

NONBIT NA 1.55 1.17 

NONE 1.41 NA 1.37 

PATB NA 1.82 1.44 

α 2 Values for COARSE SUBGRADE 

ATB NA 1.17 0.792 

DGAB 0.72 NA 0.687 

LCB NA 1.27 0.891 

NONBIT NA 1.24 0.859 

NONE 1.10 NA 1.06 

PATB NA 1.51 1.13 

α 2 Values for ROCK/STONE SUBGRADE 

ATB NA 0.976 0.599 

DGAB 0.527 NA 0.494 

LCB NA 1.08 0.698 

NONBIT NA 1.04 0.666 

NONE 0.905 NA 0.871 

PATB NA 1.32 0.939 

Where ADJ_AGE equals pavement age less the crack initiation age. If the pavement age 

is less than or equal to the crack initiation age, the predicted TC equals zero. 
171 

Example for TC Prediction Model for Flexible Pavements 

The prediction model for TC involves a two-step process. In the first step, the logistic 

model in equation 32 is used to estimate the age of crack initiation. This is done by 

solving for the AGE variable so that P is equivalent to the cutoff value (0.7 for this 

model). This AGE is denoted as the crack initiation age. In some cases, the resulting 

crack initiation age may be less than zero. A value of zero is used in place of the 

estimated negative crack initiation age. 

If the TC estimate is for a pavement age less than or equal to the crack initiation age, the 

predicted TC value is equal to zero. If the TC estimate is for a pavement age greater than 

the crack initiation age, the ADJ_AGE is calculated as the pavement age of interest less 

the crack initiation age. Table 48 lists information for an example pavement section. 

Table 48. Example pavement section information. 

Input Value 

Pavement Structure Nonoverlay, Unbound Base 

BASE DGAB 

SG FINE 

ESAL 126,000 

SN 5.0 

ACTHICK 6.5 

FTC 80 

FI 688 

CI 205 

PRECIP 1,140 

MIRI_AGE 1 

MIRI 1 

AGE 12 

Equation 34 substitutes information from table 48 as well as a P value of 0.7 into the 

logistic equation and extracts the alpha 1 value from the appropriate table. 

( )

( )

1.86 10 * * 205 1.92 10 * * 688 7.72 10 * *1140 1.56 10 * *80

5

log 126000 1.30 10 * 205 1.81 10 *1140 0.512 * 2.88 10 * *

1.34 10 *80 2.65 10 * 688

5

log 126000 1.03 0.723 *

1 0.7

0.7 ln

4 4 5 3

3 4 2

2 3

AGE AGE AGE AGE

AGE AGE

− − − −

− − −

− −

− × − × − × + ×

⎟

⎠

⎞ ⎜

⎝

⎛ + × + × + − ×

⎟ − × + × ⎠

⎞ ⎜

⎝

⎛ ⎟ = + ⎠⎞ ⎜

⎝

⎛

−

 (34) 

Solving for AGE through an iterative process results in equation 35. 
172 

AGE = 7.54 = crack initiation age (35) 

TC prediction of interest occurs at a pavement age of 12. Therefore, ADJ_AGE = 

12−7.54 = 4.46. Equation 36 substitutes this age and the information in table 48 into the 

regression equation. 

( ) ( )

( )

1.88 10 205 * ln( ) 4.46 0.1

5.28 10 * 688 1.10 10 *1140 1.12 10 * ln 4.46 0.1 *1140

1.14 * ln 4.46 0.1 3.16 10 * 205

5

log 126000 ln 01 1.03 0.175 *

4

4 4 4

4

− × +

+ × − × + × +

⎟ + + − × ⎠

⎞ ⎜

⎝

⎛ + = +

−

− − −

− (TC . )

(36) 

Therefore, the results are shown in equations 37 and 38. 

(37) 

(38) 

LC PREDICTION MODEL FOR RIGID PAVEMENTS 

Equation 39 shows the logistic model (total observations = 400). 

( )

AGE FI PRECIP AGE

PRECIP AGE AGE CI

FTC CI

DEPTH

ESAL

P

P

3.45 10 * * 1.03 10 * *

5.69 10 * 3.01 10 * 8.19 10 * *

1.09 10 * 1.39 10 * log 4.01 3.15 *

1

ln

5 4

4 2 5

2 3

− −

− − −

− −

+ × + ×

− × + × − ×

⎟ + × + × ⎠

⎞ ⎜

⎝

⎛ ⎟ = − + ⎠⎞ ⎜

⎝

⎛

−

(39) 

Where the crack initiation age is determined as the AGE at which P, cutoff probability, 

equals 0.55 (percent correct = 63.5). If the crack initiation age is determined to be less 

than zero from the logistic model, a value of zero is used. 

Equation 40 is the regression equation (R-squared = 0.38, total observations = 240). 

( )

( ) ADJ AGE FTC PRECIP

FTC PRECIP

DEPTH

ESAL (LC . ) α

0.354 * ln _ 0.01 7.66 10 * *

4.96 10 * 6.44 10 * log ln 01 0.68 *

6

3 4

1

−

− −

+ + − ×

⎟ + × + × ⎠

⎞ ⎜

⎝

⎛ + = +

(40) 

Table 49 lists the coefficients for the rigid LC regression model. 

0.1 25.6

ln 01 3.25

3.25 = − =

+ =

TC e

(TC . )
173 

Table 49. Coefficients for rigid 

LC regression model. 

BASE α 1

ATB −1.36 

DGAB −1.89 

LCB −1.03 

NONBIT −0.84 

PATB −2.14 

Where ADJ_AGE equals pavement age less the crack initiation age. If the pavement age 

is less than or equal to the crack initiation age, the predicted LC equals zero. If the 

predicted LC is less than zero for pavement age is greater than crack initiation age, the 

predicted LC was set to zero. 

Example for LC Prediction Model for Rigid Pavements 

The prediction model for LC involves a two-step process. In the first step, logistic 

equation 39 is used to estimate the age of crack initiation. This is done solving for the 

AGE variable so that P is equivalent to the cutoff value (0.55 for this model). This AGE 

is denoted as the crack initiation age. In some cases, the resulting crack initiation age may 

be less than zero. A value of zero is used in place of the estimated negative crack 

initiation age. 

If the LC estimate is for a pavement age less than or equal to the crack initiation age, the 

predicted TC value is equal to zero. If the LC estimate is for a pavement age greater than 

the crack initiation age, the ADJ_AGE is calculated as the pavement age of interest less 

the crack initiation age. 

Table 50 gives the information for an example pavement section. 
174 

Table 50. Example pavement section information. 

Input Value 

Pavement Structure JPCC 

BASE DGAB 

SG FINE 

ESAL 410,000 

FC 2 

D 9.5 

FTC 80 

FI 688 

CI 205 

PRECIP 1,140 

MIRI_AGE 1 

MIRI 1 

AGE 14 

Equation 41 substitutes information from the table 50 as well as a P value of 0.55 into the 

logistic equation and extracts the alpha 1 value from the appropriate table. 

( )

AGE AGE

AGE AGE

3.45 10 * * 688 1.03 10 *1140 *

5.69 10 *1140 3.01 10 * 8.19 10 * * 205

1.09 10 * 80 1.39 10 * 205

9.5

log 410000 4.01 3.15 *

1 0.55

0.55 ln

5 4

4 2 5

2 3

− −

− − −

− −

+ × + ×

− × − × − ×

⎟ + × + × ⎠

⎞ ⎜

⎝

⎛ ⎟ = − + ⎠⎞ ⎜

⎝

⎛

−

 (41) 

Equation 42 is solving for AGE through an iterative process. 

AGE =11.86 = crack initiation age (42) 

LC prediction of interest occurs at a pavement age of 14. Therefore, ADJ_AGE = 

14−11.86 = 2.14. Substituting this age and the information in table 50 into the regression 

equation results in equation 43. 

( )

0.354 * ln( ) 2.14 0.01 7.66 10 * 80 *1140

4.96 10 * 80 6.44 10 *1140

9.5

log 410000 ln 01 1.89 0.68 *

6

3 4

−

− −

+ + − ×

⎟ + × + × ⎠

⎞ ⎜

⎝

⎛ (LC + . ) = − +

(43) 

Therefore, the results are shown in equations 44 and 45. 
175 

 (44) 

 (45) 

TC PREDICTION MODEL FOR RIGID PAVEMENTS 

Equation 46 shows the logistic model (total observations = 414). 

( )

PRECIP AGE

PRECIP AGE CI AGE FI AGE

FTC CI

DEPTH

ESAL

P

P

1.31 10 * *

2.07 10 * 0.126 * 1.16 10 * * 7.47 10 * *

6.58 10 * 1.25 10 * log 2.20 3.06 *

1

ln

4

3 4 5

3 3

−

− − −

− −

+ ×

− × + − × − ×

⎟ + × + × ⎠

⎞ ⎜

⎝

⎛ ⎟ = − + ⎠⎞ ⎜

⎝

⎛

−

(46) 

Where The crack initiation age is determined as the AGE at which P, cutoff probability, 

equals 0.6 (percent correct = 63.5). If the crack initiation age is determined to be less than 

zero from the logistic model, a value of zero is used. 

Equation 47 shows the regression equation (R-squared = 0.54, total observations = 228). 

FTC PRECIP

PRECIP ADJ AGE FTC FI

(TC . ) α FTC FI

1.56 10 * *

2.23 10 * 0.388 * ln( _ 0.01) 1.53 10 * *

ln 01 1.02 10 * 1.20 10 *

5

3 5

2 3

1

−

− −

− −

− ×

+ × + + + ×

+ = + × − ×

(47) 

Table 51 lists the coefficients for the rigid TC regression model. 

Table 51. Coefficients for rigid TC regression model. 

BASE α 2

ATB −3.05 

DGAB −2.28 

LCB −1.76 

NONBIT −2.65 

PATB −2.55 

Where ADJ_AGE equals pavement age less the crack initiation age. If the pavement age 

is less than or equal to the crack initiation age, the predicted TC equals zero 

Example for TC Prediction Model for Rigid Pavements 

The prediction model for TC involves a two-step process. In the first step, the logistic 

model in equation 46 is used to estimate the age of crack initiation. This is done by 

solving for the AGE variable so that P is equivalent to the cutoff value (0.6 for this 

model). This AGE is denoted as the crack initiation age. In some cases, the resulting 

0.1 .358

ln 01 0.78

0.78 = − =

+ = −

− LC e

(LC . )
176 

crack initiation age may be less than zero. A value of zero is used in place of the 

estimated negative crack initiation age. 

If the TC estimate is for a pavement age less than or equal to the crack initiation age, the 

predicted TC value is equal to zero. If the TC estimate is for a pavement age greater than 

the crack initiation age, the ADJ_AGE is calculated as the pavement age of interest less 

the crack initiation age. 

Table 52 lists information for an example pavement section. 

Table 52. Example pavement section information. 

Input Value 

Pavement Structure JPCC 

BASE DGAB 

SG FINE 

ESAL 410,000 

FC 2 

D 9.5 

FTC 80 

FI 688 

CI 205 

PRECIP 1,140 

MIRI_AGE 1 

MIRI 1 

AGE 18 

Equation 48 substitutes information from table 52 as well as a P value of 0.6 into the 

logistic equation and extracts the alpha 1 value from the appropriate table. 

( )

AGE AGE

AGE AGE

7.47 10 * 688 * 1.31 10 *1140 *

2.07 10 *1140 0.126 * 1.16 10 * 205 *

6.58 10 * 80 1.25 10 * 205

9.5

log 410000 2.20 3.06 *

1 0.6

0.6 ln

5 4

3 4

3 3

− −

− −

− −

− × + ×

− × + − ×

⎟ + × + × ⎠

⎞ ⎜

⎝

⎛ ⎟ = − + ⎠⎞ ⎜

⎝

⎛

−

(48) 

Equation 49 is solving for AGE through an iterative process. 

AGE =11.86 = crack initiation age (49) 
177 

The TC prediction of interest occurs at a pavement age of 18. Therefore, ADJ_AGE = 

18−11.86 = 6.14. Equation 50 substitutes this age and the information in table 52 into the 

regression equation shown in equation 50. 

2.23 10 *1140 0.388 * ln(1.43 0.01) 1.53 10 * 80 * 688 1.56 10 * 80 *1140

ln 01 2.28 1.02 10 * 80 1.02 10 * 688

3 5 5

2 3

− − −

− −

+ × + + − × − ×

(TC + . ) = − + × − ×

(50) 

Therefore, the results are as shown in equations 51 and 52. 

 (51) 

 (52) 

RUT DEPTH PREDICTION MODEL FOR FLEXIBLE PAVEMENTS 

Equation 53 shows the regression equation (R-squared = 0.45, total observations = 

1,966). 

( )

( ) ( )

() () ( ) FI

SN

ESAL AGE CI AGE FI

AGE

SN

ESAL FTC FI PRECIP

(RUT . ) α AGE CI PRECIP

* log 1.42 10 * ln 0.1 * 1.38 10 ln 0.1 * *

* ln 0.1 log 3.48 10 * 2.98 10 * * 8.44 10 *

ln 01 0.503 * ln 0.1 3.37 10 * 1.22 10 *

4 5

3 7 2

4 5

β ⎟ + γ ⎠

⎞ ⎜

⎝

⎛ − × + − × + +

⎟ + ⎠

⎞ ⎜

⎝

⎛ + × + × − ×

+ = + + + × + ×

− −

− − −

− −

(53) 

Table 53 lists the coefficients for the flexible RUT model. 

0.1 0.92

ln 01 0.02

0.02 = − =

+ =

TC e

(TC . )
178 

Table 53. Coefficients for flexible RUT model. 

α Values for FINE SUBGRADE 

BASE Nonoverlay, 

Unbound Base 

Nonoverlay, Bound 

Base 

Overlay, Bound 

or Unbound Base 

ATB NA 0.136 0.124 

DGAB 0.367 NA 0.31 

LCB NA 0.624 0.612 

NONBIT NA 0.436 0.425 

NONE −0.539 NA −0.597 

PATB NA −0.309 −0.321 

α Values for COARSE SUBGRADE 

ATB NA 0.0958 0.084 

DGAB 0.327 NA 0.269 

LCB NA 0.583 0.572 

NONBIT NA 0.396 0.384 

NONE −0.580 NA −0.637 

PATB NA −0.350 −0.361 

α Values for ROCK/STONE SUBGRADE 

ATB NA −0.0199 −0.0317 

DGAB 0.211 NA 0.154 

LCB NA 0.468 0.456 

NONBIT NA 0.280 0.269 

NONE −0.695 NA −0.753 

PATB NA −0.465 −0.477 

β and γ values 

BASE β γ

ATB 0.331 -.000228 

DGAB 0.225 -.000266 

LCB 0.0236 -.00507 

NONBIT 0.108 .00120 

NONE 1.13 .000283 

PATB 0.769 .000150 
179 

Example for Rut Depth Prediction Model for Flexible Pavements 

The prediction model for rut depth on flexible pavement uses one regression equation. 

Table 54 lists information for the example pavement structure to illustrate the model. 

Table 54. Example pavement section information. 

Input Value 

Pavement Structure Nonoverlay, Unbound Base 

BASE DGAB 

SG FINE 

ESAL 126,000 

SN 5.0 

ACTHICK 6.5 

FTC 80 

FI 688 

CI 205 

PRECIP 1,140 

MIRI_AGE 1 

MIRI 1 

AGE 2 

Equation 54 substitutes the information from table 54 into the delta equation and extracts 

the alpha value from table 53 based on pavement structure, base type, and subgrade type. 

( )

( ) ( )

() ()

( ) 2.66 10 * 688

5

log 126000 0.225 *

1.42 10 * ln 2 0.1 * 205 1.38 10 ln 2 0.1 * 688

* ln 2 0.1

5

log 126000 3.48 10 * 80 2.98 10 * 688 *1140 8.44 10 *

ln 01 .367 0.503 * ln 2 0.1 3.37 10 * 205 1.22 10 *1140

4

4 5

3 7 2

4 5

−

− −

− − −

− −

⎟ − ×

⎠

⎞ ⎜

⎝

⎛ +

− × + − × +

⎟ + ⎠

⎞ ⎜

⎝

⎛ + × + × − ×

(RUT + . ) = + + + × + ×

(54) 

Therefore, the results are shown in equations 55 and 56. 

 (55) 

53 (56) 0.1 3.

ln 01 1.29

1.29 = − =

+ =

RUT e

(RUT . )
180 

TRANSVERSE JOINT FAULTING PREDICTION MODEL FOR RIGID 

PAVEMENTS 

Equation 57 shows the regression equation (R-squared = 0.47, total observations = 

1,384). 

( )

( )

( )

( ) AGE FI AGE

DEPTH

ESAL AGE

PRECIP CI PRECIP

DEPTH

ESAL PRECIP

CI FI CI

DEPTH

ESAL FTC FI CI

FTC FI

DEPTH

ESAL (FAULT ) α

* 9.78 10 * * log 6.68 10 * 5.09 10 *

* 7.44 10 * * log 8.42 10 * 8.28 10 *

* 9.28 10 * * log 3.15 10 * * 5.15 10 * 7.92 10 *

1.40 10 * 5.51 10 * log ln 1 0.337*

3 2 6

4 4 7

6 4 4 7

3 4

− − −

− − −

− − − −

− −

⎟ + × ⎠

⎞ ⎜

⎝

⎛ − × + ×

⎟ + × ⎠

⎞ ⎜

⎝

⎛ − × + ×

⎟ + × ⎠

⎞ ⎜

⎝

⎛ + × − × − ×

⎟ − × − × ⎠

⎞ ⎜

⎝

⎛ + = −

(57) 

Table 55 lists the coefficients for the rigid FLT model. 

Table 55. Coefficients for rigid FLT model. 

αValues 

BASE Fine 

 Subgrade 

Coarse 

Subgrade 

Other 

Subgrade 

Rock/Stone 

Subgrade 

ATB 0.755 0.810 1.20 0.515 

DGAB 0.732 0.786 1.18 0.492 

LCB 0.665 0.720 1.11 0.425 

NONBIT 0.834 0.889 1.28 0.595 

PATB 0.693 0.748 1.14 0.453 

Example for Fault Prediction Model for Rigid Pavements 

The prediction model for faulting on rigid pavement uses one regression equation. Table 

56 lists information for an example pavement structure to illustrate the use of the model. 
181 

Table 56. Example pavement section information. 

Input Value 

Pavement Structure JPCC 

BASE DGAB 

SG FINE 

ESAL 410,000 

FC 2 

D 9.5 

FTC 80 

FI 688 

CI 205 

PRECIP 1140 

AGE 15 

Equation 58 substitutes the information from table 56 into the delta equation and extracts 

the alpha value from table 55 based on base type and subgrade type. 

( )

( )

( )

( ) *15 9.78 10 *688*15

9.5

log 410000 6.68 10 *15 5.09 10 *

*1140 7.44 10 *205*1140

9.5

log 410000 8.42 10 *1140 8.28 10 *

*205 9.28 10 *688*205

9.5

log 410000 3.15 10 *80*688 5.15 10 *205 7.92 10 *

1.40 10 *80 5.51 10 *688

9.5

log 410000 ln 1 .732 0.337*

3 2 6

4 4 7

6 4 4 7

3 4

− − −

− − −

− − − −

− −

⎟ + × ⎠

⎞ ⎜

⎝

⎛ − × + ×

⎟ + × ⎠

⎞ ⎜

⎝

⎛ − × + ×

⎟ + ×

⎠

⎞ ⎜

⎝

⎛ + × − × − ×

⎟ − × − ×

⎠

⎞ ⎜

⎝

⎛ (FAULT + ) = −

(58) 

Therefore, equations 59 and 60 show the results. 

(59) 

(60) 1.0 0.443

ln 1 0.367

0.367 = − =

+ =

FAULT e

(FAULT )

183 

APPENDIX C. AGENCY CLIMATIC INFORMATION 

This appendix provides State maps containing the analysis test sections for each of the 

participating agencies. These maps and the tables link the geographic location of test 

sections to the climatic setting and aid in evaluating environmental variation within a 

State. This appendix also contains information on the pavement structure of each GPS 

test section. 

Figure 83 shows geographic locations of analysis test sections in Alaska, and table 57 

lists the environmental and pavement structure information for the Alaska test sections. 

Figure 83. Map. Alaska geographic location of analysis test sections. 

Table 57. Alaska environmental and pavement structure information 

for test sections. 

Site FTC FI CI PRECIP SN ACTHICK D LESN LEDT AC/PC

1002 112 794 0 2020 3.4 4.3 NA 1.4 NA AC 

1001 124 890 0 1116 2.2 3.0 NA 2.0 NA AC 

Average 118 841 0 1568 2.8 3.7 NA 1.7 NA NA 

1004 87 1110 2 420 4.1 4.5 NA 1.2 NA AC 

6010 92 1248 1 413 2.9 3.8 NA 1.7 NA AC 

9035 106 1513 3 729 2.7 3.0 NA 1.7 NA AC 

1008 74 2584 23 317 2.9 3.1 NA 1.6 NA AC 

Average 90 1614 7 470 3.2 3.6 NA 1.5 NA NA 
184 

Figure 84 shows the geographic locations for the analysis test sections in Idaho, and 

table 58 lists the environmental and pavement structure information for test sections in 

Idaho. 

Figure 84. Map. Idaho geographic location of analysis test sections. 

Table 58. Idaho Environment and pavement structure information for test sections. 

Site FTC FI CI PRECIP SN ACTHICK D LESN LEDT AC/PC

1001 113 217 158 693 2.2 3.7 NA 2.3 NA AC 

9032 111 259 121 718 5.8 6.1 NA 0.9 NA AC 

3023 107 278 400 295 NA NA NA NA 0.7 PC 

9034 117 316 89 807 6.5 9.2 NA NA NA AC 

1007 121 326 271 254 5.0 5.5 NA NA NA AC 

1020 128 328 331 280 4.0 3.8 NA 1.2 NA AC 

1009 137 351 221 262 5.9 10.6 NA 0.9 NA AC 

3017 125 356 344 342 NA NA 10.3 NA 0.6 PC 

1005 121 399 339 627 NA 3.8 NA 1.6 NA AC 

Average 120 314 253 475 NA 6.1 NA 1.2 0.6 NA 

1021 129 622 149 342 3.2 5.9 NA 1.6 NA AC 

1010 136 665 144 303 5.5 10.9 NA 1.0 NA AC 

6027 162 817 57 380 4.6 5.6 NA 1.2 NA AC 

Average 142 702 116 342 4.4 7.5 NA 1.2 NA NA 
185 

Figure 85 shows geographic locations for the analysis test sections in Illinois, and table 

59 lists environment and pavement structure information for the Illinois test sections. 

Figure 85. Map. Illinois geographic location of analysis test sections. 

Table 59. Illinois environment and pavement structure information for test sections. 

Site FTC FI CI PRECIP SN ACTHICK D LESN LEDT AC/PC

79 212 742 1052 5.9 12.10 NA 0.8 NA AC NA 

78 239.4 747 995 4.9 7.00 NA 1.0 NA AC NA 

79 225.7 745 1024 5.4 9.55 NA 0.9 NA NA NA 

96 651.8 383 871 5.8 13.20 NA 0.9 NA AC NA 
186 

Figure 86 shows geographic locations of analysis test sections in Indiana, and table 60 

lists environment and pavement structure information for the Indiana test sections. 

Figure 86. Map. Indiana geographic location of analysis test sections. 

Table 60. Indiana environment and pavement structure information 

for test sections. 

Site FTC FI CI PRECIP SN ACTHICK D LESN LEDT AC/PC

1037 73 152 821 1186 6.3 14.4 NA 0.88 NA AC 

6012 71 215 798 1165 8.6 20.2 NA 0.69 NA AC 

1028 80 217 696 1209 6.7 15.3 NA 0.84 NA AC 

3031 70 230 772 1165 NA NA 10.0 NA 0.53 PC 

3030 88 386 476 1017 NA NA 8.0 NA 0.72 PC 

2008 84 399 471 963 7.0 12.9 NA 0.76 NA AC 

Average 78 267 672 1118 7.2 15.7 9.1 0.79 0.63 NA 

3002 84 451 520 950 NA NA 9.5 NA 0.58 PC 

3003 88 454 453 1005 NA NA 10.0 NA 0.58 PC 

Average 86 453 487 978 NA NA 9.9 NA 0.58 NA 
187 

Figure 87 shows geographic locations of analysis test sections in Michigan, and table 61 

lists environment and pavement structure information for Michigan test sections. 

Figure 87. Map. Michigan geographic location of analysis test sections. 

Table 61. Michigan environment and pavement structure information 

for test sections. 

Site FTC FI CI PRECIP SN ACTHICK D LESN LEDT AC/PC

0200 86 382 443 866 NA NA NA NA NA PC

0100 105 510 324 870 NA NA NA NA NA AC

1010 91 532 309 825 3.7 2.2 NA 1.3 NA AC

1013 109 568 251 915 5.0 6.7 NA 1.1 NA AC

1012 115 612 230 931 4.9 6.1 NA 1.1 NA AC

3068 109 670 215 827 NA NA 9.0 0.6 0.6 PC

1001 109 759 174 790 2.2 2.2 2.3 NA AC

6016 100 787 173 751 4.5 4.6 10.3 1.1 0.6 AC

Average 105 634 239 844 4.1 4.4 9.7 1.3 0.6 NA

1004 77 960 131 878 2.5 4.2 NA 2.1 NA AC
188 

Figure 88 shows geographic locations for analysis test sections in New York. 

Figure 88. Map. New York geographic location of analysis test sections. 

Table 62 lists environment and pavement structure information for analysis test sections 

in New York. 

Table 62. New York environment and pavement structure information 

for test sections. 

Site FTC FI CI PRECIP SN ACTHICK D LESN LEDT AC/PC

0800 87 437 319 891 NA NA NA NA NA AC 

1011 90 505 298 1007 7.4 12.0 NA 0.7 NA AC 

1008 87 582 257 1133 5.6 1.8 NA 0.9 NA AC 

1643 99 618 270 1006 5.8 3.7 NA 1.0 NA AC 

Average 91 536 286 1009 6.3 5.8 NA 0.9 NA NA

1644 109 990 111 1110 3.7 3.0 NA 1.3 NA AC 

Figure 89 shows geographic location of analysis test sections in North Carolina. Table 63 

lists environment and pavement structure information for analysis test sections in North 

Carolina. 
189 

Figure 89. Map. North Carolina geographic location of analysis test sections. 

Table 63. North Carolina environment and pavement information 

for analysis test sections. 

Site FTC FI CI PRECIP SN ACTHICK D LESN LEDT AC/PC

0800 47 14 998 1343 NA NA NA NA NA AC 

1645 57 19 975 1260 5.2 8.9 NA 1.1 NA AC 

1030 50 24 951 1192 3.6 4.0 NA 1.4 NA AC 

2825 52 28 965 1093 3.9 4.6 NA 1.2 NA AC 

1352 69 32 876 1232 3.7 6.6 NA 1.4 NA AC 

1028 54 32 870 1186 4.6 2.9 NA 1.1 NA AC 

3008 67 33 815 1212 NA NA 7.9 NA 0.7 PC 

1006 60 35 887 1153 NA 10.1 NA 1.0 NA AC 

2819 69 43 799 1139 NA 7.0 NA 1.0 NA AC 

3816 71 44 830 1141 NA NA 9.3 NA 0.6 PC 

3011 71 45 867 1151 NA NA 10.0 NA 0.6 PC 

3807 77 46 772 1142 NA NA 9.4 NA 0.6 PC 

1817 74 46 779 1126 3.9 6.7 NA 1.4 NA AC 

0200 83 47 773 1151 NA NA NA NA NA PC 

3044 77 49 784 1161 NA NA 9.0 NA 0.7 PC 

Average 65 36 863 1179 4.5 6.4 9.1 1.2 0.6 NA 

1992 73 53 779 1221 4.7 2.4 NA 1.1 NA AC 

2824 73 54 780 1224 4.0 5.8 NA 1.3 NA AC 

1802 82 58 775 1110 3.3 4.9 NA 1.6 NA AC 

1024 93 59 475 1308 5.0 7.6 NA 1.0 NA AC 

1814 100 70 446 1477 4.2 7.1 NA 1.3 NA AC 

1803 107 86 407 1371 3.8 6.3 NA 1.3 NA AC 

1801 88 116 323 1205 4.6 8.5 NA 1.2 NA AC 

1040 109 141 218 1441 3.9 6.2 NA 1.3 NA AC 

Average 91 80 526 1245 4.2 6.1 NA 1.3 NA NA 
190 

Figure 90 shows the geographic locations of the analysis test sites in Ohio, and table 64 

shows the environment and pavement structure information for the analysis test sections 

in Ohio.. 

Figure 90. Map. Ohio geographic locations of analysis test sections. 

Table 64. Ohio environment and pavement structure information 

for analysis test sections. 

Site FTC FI CI PRECIP SN ACTHICK D LESN LEDT AC/PC

3801 89 249 480 1037 NA NA 9.2 NA 0.6 PC 

3013 89 250 533 1140 NA NA 8.3 NA 0.6 PC 

Average 89 250 507 1088 NA NA 8.8 NA 0.6 NA 

0100 96 375 414 972 NA NA NA NA NA AC 

0200 96 375 414 972 NA NA NA NA NA PC 

0800 96 375 414 972 NA NA NA NA NA AC 

Average 96 375 414 972 NA NA NA NA NA NA 
191 

Figure 91 shows the geographic locations for analysis test sections in Pennsylvania. 

Figure 91. Map. Pennsylvania geographic locations of analysis test sections. 

Table 65 lists environment and pavement structure information for analysis test sections 

in Pennsylvania. 

Table 65. Pennsylvania environment and pavement structure information 

for analysis tests sections. 

Site FTC FI CI PRECIP SN ACTHICK D LESN LEDT AC/PC

3044 99 269 433 1164 NA NA 12.7 NA 0.5 PC 

1623 95 309 415 1004 NA NA 9.3 NA 0.6 PC 

1608 107 311 334 944 3.7 5.0 NA 1.1 NA AC 

1605 107 331 339 1092 6.4 9.5 NA 0.9 NA AC 

1618 100 343 270 1028 3.6 5.1 NA 1.5 NA AC 

Average 102 313 358 1047 4.6 6.5 11.0 1.2 0.6 NA 

1597 111 514 196 865 5.6 8.7 NA 0.9 NA AC 

1599 124 546 141 1121 6.7 12.3 NA 0.8 NA AC 

Average 118 530 169 993 6.2 10.5 NA 0.9 NA NA

193 

APPENDIX D. QUESTIONNAIRE SENT TO POOLED FUND STATES 

This appendix provides a sample questionnaire sent to each of the participating PFS. The 

intent of this survey was to gain an understanding of standard design, specification, and 

testing procedures as well as unit bid prices and average performance lives. 
194 

POOLED FUND STATES QUESTIONNAIRE

Dear State Pooled Fund Panel Members: 

As you remember, one of the primary research objectives for this pooled fund study was 

to determine: 

“The extent to which local adaptations of materials standards and pavement 

thickness designs have compensated for and/or mitigated the effects of seasonal 

frost penetration,...” 

To accomplish that goal NCE proposed to look at this issue using a couple of standard 

pavement design sections. NCE anticipates that the roadway design sections as well as 

the materials and their related specifications may change between States to provide better 

pavement performance in their respective environment. 

To develop these standard section and related material information, we are asking the 

pooled fund states (PFS) to provide the following information. 

Standard Roadway Section

What is your standard pavement section for both flexible and rigid pavements that meets 

the following design criteria? If you don’t have standard sections then what would your 

designed section be? 

Rural Interstate (Four Lanes) Rigid and Flexible

− 30-year design. 

− 30,000,000 ESALs. 

− Frost-susceptible, fine-grained soil MR 68,947.6 kilopascals (kPa) (10,000 pounds 

per square inch (psi)). 

−

Rural Primary (Two Lanes) Rigid and Flexible

− 30-year design. 

− 5,000,000 ESALs. 

− Frost susceptible fine grained soil MR 68,947.6 kilopascals (kPa) (10,000 psi). 

Please provide layer unit names as well as dimensions for both the traveled lanes as well 

as the shoulder sections. For example, the first section of Rural Interstate Flexible might 

be shown as follows: 

Pavement Course Main Line Shoulders_________________

Wearing Course 7.62 cm (3-inch) Class A HMA 7.62 cm (3-inch) Class A HMA 

Leveling Course 7.62 cm (3-inch) Class B HMA 7.62 cm (3-inch) Class B HMA 

Base/Binder Course 12.7 cm (5-inch) Class E HMA 

Granular Base Course 15.24 cm (6-inch) Class 1 UTBC 27.94 cm (11-inch) Class 1 UTBC 

Granular Subbase Cr. 15.24 cm (6-inch) Class 3 UTBC 15.24 cm (6-inch) Class 3 UTBC

Total Depth 58.42 cm (23 inches) 58.42 cm (23 inches) 
195 

The item names noted above are entirely fictional, and are used only to show that the bid 

item names are important. Each State transportation agency has its own naming 

conventions. 

If special drainage features are included in the roadway section please note those as well. 

If possible provide a cross section of the roadway section which shows the configuration 

of the pavement layers as well as the typical ditch section and depth, subgrade slopes, 

drainage features, etc. 

Standard Specifications

We will also need copies of your standard specifications that apply to the bid items listed 

for the material properties as well as the placement procedures or in place properties. 

Where these are available at your agency’s Web site, please let us know and we will 

download the PDF files. If only paper hard copies are available, please provide copies of 

the applicable specifications or simply send us a specifications book that applies to the 

materials placed at the LTPP test sites and we will make copies and return the book. 

The ongoing adoption of SuperPave mixes will complicate this process. If your agency 

has adopted SuperPave mixes, please reference the materials that were used in your GPS 

and SPS test sites that represent the performance data included in the LTPP database. 

If your agency has adopted SuperPave, please provide copies of those specifications as 

well. Please note that many agencies have developed their own SuperPave mix 

specifications based to varying degrees on the national guidelines. This is the reason we 

are asking for your specific specifications rather than use the national guidelines. 

Test Procedures

Please review your specifications before you send them. If they reference standard 

AASHTO test procedures, we can access that information. If, however, they reference 

test procedures that are unique to your agency, please provide copies of those test 

procedures or provide a reference to the Web site where those test procedures are 

available. 

Average Unit Bid Prices

In addition to the specifications, we will also need the average unit bid prices or the 

prices you would prefer we use in this study for each of the bid items noted in your 

standard or design roadway section. 

Typical Service Life for Standard Section

We would also like your best estimate of the average service life of the pavement 

sections until major pavement repair, rehabilitation, or overlay as usually required. Please 

also provide a description of that treatment as well as the typical pavement condition 

(amount of fatigue cracking, ride values etc.), when treatment is applied. 
196 

Adjacent State Treatments

If there are any unique designs processes or treatments that are used by any adjacent 

States that seem to help mitigate frost effects, please describe that treatment and, if 

possible, indicate a contact person to check on that treatment. 

Timeline

If possible we would like to receive the typical or design roadway sections as well as the 

standard specifications and test procedures by June 21. We would like to receive the rest 

of the material (bid prices, service life estimate, and adjacent state treatments) by July 9. 

Sincerely, 

Newton Jackson, P.E. 

Project Manager 

Nichols Consulting Engineers Chtd. 
197 

APPENDIX E. RESPONSES RECEIVED FROM POOLED FUND STATES 

This appendix provides the information obtained from the PFS in response to the 

questionnaire in Appendix E. Some of the information provided by the PFS was excluded 

from this appendix because of publication limitations. 
198 

STATE OF ALASKA 

Department of Transportation and Public Facilities 

2301 Peger Road 

Fairbanks, Alaska 99709 

June 22, 2004 

Newton Jackson, P.E. 

Principal Engineer 

Nichols Consulting Engineers, Chtd. 

1885 S. Arlington, Suite 111 

Reno, NV 89509 

RE: Alaskan pavement section for “Effects of Multiple Freeze Cycles...” Pooled Fund 

Study 

Dear Newton: 

I am enclosing the Alaskan information you requested for the above project: 

Standard Roadway Section:

- In Alaska, only flexible pavements are used for roadways 

- In Rural Interstate (4 lanes): 30M ESALs is a high traffic level for us, so no 

pavement section is provided. 

- Rural Primary (2 lanes): Enclosed are a drawing and a table detailing the 

pavement section for this case. 

Standard Specifications:

Enclosed are hard copies of the relevant pages from the “Alaska Standard Specifications 

for Highway Construction – 2004.” 

Test Procedures:

We mainly use AASHTO specifications; however, two tests are done according to the 

Alaska Test Methods (ATM); see enclosed. 

Average Unit Bid Prices:

They are included in the table detailing the pavement section. 

Typical Service Life for Standard Section:

The average service life is 10 to 12 years. Typically, rutting and ride quality dictate the 

rehabilitation. “Shave and Pave” is typically used, unless distress is due to deep failures, 

where reclamation and paving become necessary. 

Adjacent State Treatments:

The Yukon Territory mainly uses high-float surface treatment. 
199 

If you have questions or need further clarifications, please call or e-mail me. 

Sincerely, 

Steve Sabaundjian, P.E. 

Research Engineer 

steve_saboundjian@dot.state.ak.us 

Ph: (907) 451-5322 

Typical flexible pavement for Alaska; Rural Primary (two lanes); 5M ESALs. 

See drawing. 

Pavement Course Mainline and Shoulder Item Price 

Wearing Course 50 mm (2 inch) HMA, Type 

II, Class B* 

401 (1) $27.56/metric ton 

($25/ton) 

Binder Course 75 mm (3 inch) HMA, Type 

II, Class B* 

401 (1) $27.56/metric ton 

($25/ton) 

Aggregate Base Course 180 mm (7 inch), Grading D1** 301 (1) $12.13/metric ton 

($11/ton) 

Subbase 305 mm (12 inch), Grading B 304 (1) $9.92/metric ton 

($9/ton) 

Borrow 915 mm (36 inch), Selected 

Material Type A 

203 (6) $4.41/metric ton 

($4/ton) 

Total Depth: 1525 mm (60 inch) NA NA 

Notes: 

* Asphalt, PG 58-28, $371.55/metric ton ($337/ton) 

** MC-30 liquid asphalt prime coat, $385.89/metric ton ($350/ton) 

See Section 703 for Aggregates Specs. 
200 

 1 inch = 2.54 cm; 1 ft = 0.3 m 

Figure 92. Diagram. Typical section for rural primary (2 lanes) in Alaska. 
201 

STATE OF IDAHO 

Transportation Department 

P.O. Box 7129 

Boise, ID 83707-1129 

December 20, 2004 

 1 inch = 2.54 cm; 1 ft = 0.3 m 

Figure 93. Diagram. Rigid pavement rural interstate typical section for Idaho. 

 1 inch = 2.54 cm; 1 ft = 0.3 m 

Figure 94. Diagram. Flexible pavement rural interstate typical section for Idaho. 
202 

 1 inch = 2.54 cm; 1 ft = 0.3 m 

Figure 95. Diagram. Rigid pavement rural primary typical section for Idaho. 

 1 inch = 2.54 cm; 1 ft = 0.3 m 

Figure 96. Flexible pavement rural primary typical section for Idaho. 
203 

 1 inch = 2.54 cm; 1 ft = 0.3 m 

Figure 97. Diagram. Rigid pavement at LTPP site 163023 in Idaho. 

 1 inch = 2.54 cm; 1 ft = 0.3 m 

Figure 98. Diagram. Flexible pavement at LTPP site 169032 in Idaho. 
204 

STATE OF ILLINOIS 

Department of Transportation 

126 East Ash Street 

Springfield, IL 62704-4766 

Standard Roadway Sections

 Rural Interstate Rigid

 Pavement Course Main Line Shoulder

 Jointed Plain Concrete: 4.6 m (15 ft) joints 265 mm (10.5 inch) 150 mm (6 inch) 

 Stabilized Subbase (CAM or BAM) 100 mm (4 inch) 

 Subbase Granular Material, Type C 215 mm (8.5 inch) 

 Pipe Underdrains: 100 mm (4 inch) 

 Aggregate Shoulder Wedge, Type B 

 Lime Modified Subgrade 305 mm (12 inch) 305 mm (12 inch)

 Total Depth 675 mm (26.5 inch) 675 mm (26.5 inch) 

Note: Tied Shoulder 

 Rural Interstate Flexible

 Pavement Course Main Line Shoulder

 SuperPave HMA Surface Course 50 mm (2 inch) 50 mm (2 inch) 

 SuperPave HMA Binder Course 465 mm (18.25 inch) 150 mm (6 inch) 

 Subbase Granular Material, Type C 310 mm (12.25 inch) 

 Pipe Underdrains: 100 mm (4 inch) 

 Aggregate Shoulder Wedge, Type B 

 Lime Modified Subgrade 305 mm (12 inch) 305 mm(12 inch)

 Total Depth 820 mm (32.25 inch) 820 mm (32.25 inch) 

 Rural Primary Rigid

 Pavement Course Main Line Shoulder

 Jointed Plain Concrete (15-ft Joints) 250 mm (9.75 inch) 

 Stabilized Subbase (CAM or BAM) 100 mm (4 inch) 

 SuperPave HMA Surface Course 50 mm (2 inch) 

 SuperPave HMA Binder Course 150 mm (6 inch) 

 Subbase Granular Material, Type C 145 mm (5.75 inch) 

 Aggregate Shoulder Wedge, Type B 

 Lime Modified Subgrade 305 mm (12 inch) 305 mm (12 inch)

 Total Depth 655 mm (25.75 inch) 655 mm (25.75 inch) 

 Note: Untied Shoulder 
205 

 Rural Primary Flexible

 Pavement Course Main Line Shoulder

 SuperPave HMA Surface Course 50 mm (2 inch) 50 mm (2 inch) 

 SuperPave HMA Binder Course 305 mm (12 inch) 150 mm (6 inch) 

 Subbase Granular Material, Type C 150 mm (6 inch) 

 Aggregate Shoulder Wedge, Type B 

 Lime Modified Subgrade 305 mm (12 inch) 305 mm (12 inch)

 Total Depth 660 mm (26 inch) 660 mm (26 inch) 

Standard drawings of the typical pavement cross sections may be found on the IDOT Internet site by 

following these directions. 

Go to www.dot.il.gov 

 Under IDOT Links, click on Doing Business

 Under Construction Guides, click on Specifications/Special Provisions/Highway 

Standards

 Next, click on Highway Standards

 Next, click on Index of Highway Standards

 Next, click on Section 400

 The appropriate drawings are: 420101, 482001, 482006, and 483001

Also, you will find two design drawings at: 

Go to www.dot.il.gov 

 Under IDOT Links, click on Doing Business

 Under Manuals – Memorandums – Rules, click on Bureau of Design & Environment 

Manual

 Next, click on Chapter 54—Pavement Design

 Select page 43 of 114 (Figure 54-4(9))

 Select page 65 of 114 (Figure 54-5(13))

Standard Specifications

Standard specifications that apply to rigid and flexible pavement construction may be found on 

the IDOT internet site by following these directions. 

Go to www.dot.il.gov 

 Under IDOT Links, click on Doing Business

 Under Construction Guides, click on Specifications/Special Provisions/Highway 

Standards

 Next, click on Standard Specifications for Road and Bridge Construction

 Next, click on Section 400

 The appropriate sections are: Section 406 (Starting on page 25 of 134)

 Section 407 (Starting on page 47 of 134) 

 Section 420 (Starting on page 57 of 134)

 Section 482 (Starting on page 129 of 134)

 Section 483 (Starting on page 132 of 134)

Test Procedures

References to test procedures other than AASHTO or ASTM standard test procedures may be 

found in the IDOT Manual of Test Procedures for Materials. This manual is not available on the 
206 

internet; however, the order form to receive a copy of the manual may be found on the IDOT 

internet site by following these directions. The cost of the manual is $50.00. 

Go to www.dot.il.gov 

 Under IDOT Links, click on Doing Business

 Under Manuals – Memorandums – Rules, click on Highways Manuals Order Form

 The item is number: ID 034 Manual of Test Procedures for Materials 

Average Unit Bid Prices

The following are average bid prices for the bid items listed under the Standard Roadway 

Sections. These prices assume large quantities. 

Table 66. Average unit prices for Illinois. 

Item Unit Unit Price 

Lime Metric ton (ton) $55.13 

($50.00) 

Processing Lime Modified Soils: 305 mm (12 inch) Square meter 

(square yard) 

$1.67 

($1.40) 

Subbase Granular Material, Type C Metric ton (ton) $16.54 

($15.00) 

Stabilized Subbase: 100 mm (4 inch) Square meter 

(square yard) 

$10.77 

($9.00) 

Pipe Underdrains: 100 mm (4 inch) Meter (foot) $9.84 

($3.00) 

Dowel Bars: 40 mm (1.5-inch diameter) Square meter 

(square yard) 

$14.35 

($12.00) 

PCC Pavement (Jointed): 250 mm (9.75 inch) Square meter 

(square yard) 

$35.89 

($30.00) 

PCC Pavement (Jointed): 265 mm (10.5 inch) Square meter 

(square yard) 

$38.88 

($32.50) 

Bituminous Concrete Pavement (Full-Depth): 

515 mm (20.25 inch) 

Square meter 

(square yard) 

$47.85 

($40.00) 

Bituminous Concrete Pavement (Full-Depth): 

355 mm (14.0 inch) 

Square meter 

(square yard) 

$39.48 

($33.00) 

Bituminous Shoulders: 205 mm (8 inch) Square meter 

(square yard) 

$21.53 

($18.00) 

PCC Shoulders: 150 mm (6 inch) Square meter 

(square yard) 

$17.94 

($15.00) 

Aggregate Shoulders, Type B Metric ton (ton) $14.33 

($13.00) 
207 

Typical Service Life for Standard Section

A description of the typical service life and the “planned” repair methods throughout the life of 

the pavement may be found on the IDOT internet site by following these directions. 

Go to www.dot.il.gov 

 Under IDOT Links, click on Doing Business

 Under Manuals—Memorandums—Rules, click on Bureau of Design & Environment 

Manual

 Next, click on Chapter 54—Pavement Design

 For jointed PCC pavement, select page 88 of 114 (Figure 54-7(A))

 For HMA pavement (TF = 5), select page 89 of 114 (Figure 54-7(B))

 For HMA pavement (TF = 30), select page 92 and 93 of 114 (Figure 54-7(D))
208 

STATE OF MICHIGAN 

Department of Transportation 

8885 Ricks Road 

P.O. Box 30049 

Michigan’s Typical Designs

Response for pooled fund study “Effects of Multiple Freeze Cycles and Deep Frost 

Penetration on Pavement Performance and Cost” 

Note: We use a design life of 20 years, and an MR of 69,000 kPa (10,000 psi) is much

higher than anything we would typically encounter in Michigan. 

Designs

Rural Interstate (four lanes) Rigid and Flexible

30-year design 

30,000,000 ESALs 

Frost susceptible fine grained soil MR 69,000 kPa (10,000 psi) 

 HMA Cross-Section 

Layer Mainline Shoulder

Wearing Course 40 mm (1.5 inch) Gap Graded Superpave 40 mm (1.5 inch) 4C 

Leveling Course 50 mm (2.0 inch) 4E50 50 mm (2.0 inch) 3C 

Base Course 95 mm (3.75 inch) 3E50 95 mm (3.75 inch) 2C 

Base 150 mm (6.0 inch) 21AA 150 mm (6.0 inch) 21AA 

Subbase 450 mm (18.0 inch) class IIA sand 450 mm(18.0 inch) class IIA 

 sand 

Drainage 150 mm (6.0 inch) subbase 

underdrains 

 Concrete Cross-Section 

Layer Mainline Shoulder

Concrete 290 mm (11.5 inch) JPCP 230 mm (9.0 inch) JPCP 

Base 150 mm (6.0 inch) 4G modified 215 mm (8.5 inch) 4G modified 

 Geotextile Separator 

Subbase 255 mm (10 inch) class IIA sand 255 mm (10.0 inch) class IIA 

 sand 

Drainage 150 mm (6.0 inch) base 

 underdrains 

Rural Primary (Two Lanes) Rigid and Flexible

30-year design 

5,000,000 ESALs 

Frost susceptible fine grained soil MR 69,000 kPa (10,000 psi) 
209 

 HMA Cross-Section 

Layer Mainline Shoulder

Wearing Course 40 mm (1.5 inch) 5E10 40 mm (1.5 inch) 4C 

Leveling Course 50 mm (2.0 inch) 4E10 50 mm (2.0 inch) 3C 

Base Course 75 mm (3.0 inch) 3E10 75 mm (3.0 inch) 2C 

Base 150 mm (6.0 inch) 21AA 150 mm (6.0 inch) 21AA 

Subbase 450 mm (18.0 inch) class II sand 450 mm (18.0 inch) class II 

 sand 

Drainage 150 mm (6.0 inch) subbase underdrains 

 Concrete Cross-Section 

Layer Mainline Shoulder

Concrete 215 mm (8.5 inch) JPCP 100 mm (4.0 inch) HMA (4C,3C) 

Base 150 mm (6.0 inch) 4G modified 150 mm (6.0 inch) 4G modified 

 Geotextile Separator 

Subbase 255 mm (10.0 inch) class II sand 255 mm (10.0 inch) class II sand 

Drainage 150 mm (6.0 in) base underdrains 

If this were a freeway, it would be 230 mm (9.0 in), which is our minimum concrete 

thickness for freeways. 

For equivalent designs (same traffic makeup) the ESALs would be different for asphalt 

and concrete. So, a 30 million ESAL design for HMA is not equivalent (structurally) to a 

30 million ESAL design for concrete. 

Specifications

Gap Graded Superpave—see attached special provision 

4E50, 3E50, 5E10, 4E10, 3E10—Superpave mainline mixes. Covered by Division 5 of 

Standard Specifications for Construction which can be found at: 

http://www.mdot.state.mi.us/specbook/ 

Materials for the mixes are covered in Division 9 of the Standard Specifications. 

Also see the special provision for superpave mixes (03SP501F) and the HMA Mixture 

Selection Guidelines which are attached. 

4C, 3C, 2C—Marshall mixes used for shoulders. Covered by Division 5 and Division 9 

of the Standard Specifications. Also see the special provision for Marshall mixes 

(03SP501H) and HMA Mixture Selection Guidelines which are attached. 

Jointed Plain Concrete Pavement—Covered by Division 6 

21AA—Base layer under HMA pavements. Covered by Sections 302 and 902 of the 

Standard Specifications. 
210 

4G modified—Base layer under concrete pavements. Covered by Sections 303 and 902 

of the Standard Specifications. The special provision for open graded drainage course, 

modified (03SP303A) covers the modifications to the Standard Specifications. 

Class II—Subbase layer. Covered by Sections 301 and 902 of the Standard 

Specifications. 

Geotextile Separator—Used between open graded bases and sand subbases. Covered by 

Section 910 of the Standard Specifications. 

Underdrains – Covered by Section 404 and 909 of the Standard Specifications. 

State Specific Test Methods

Any test methods specific to Michigan will be listed as MTM-xxx where ‘xxx’ will be a 

number. The Manual for the Michigan Test Methods (MTM’s) can be found at: 

http://www.michigan.gov/documents/mdot_MTM_CombinedManual_83501_7.pdf 

Also, years ago Michigan developed its own “One Point” tests for determining maximum 

density for granular and cohesive soils. (“One Point” refers to compacting the density 

mold at only one moisture content, instead of doing an optimum moisture content curve.) 

T-99 molds are used for cohesive soils and an inverted cone shaped mold is used for 

granular soils and unbound base courses. Detailed procedures are outlined in the Density 

Control Handbook, which can be accessed at: 

http://www.michigan.gov/mdot/0,1607,7-151-9622_11044_11367---,00.html 

Average Unit Bid Prices

These are prices we use in life-cycle costing: 

Item Average Price

Gap Graded Superpave $53.77/metric ton ($48.77/ton) 

4E50 $44.71/metric ton ($40.55/ton) 

3E50 $44.71/metric ton ($40.55/ton) 

5E10 $40.62/metric ton ($36.84/ton)* 

4E10 $39.25/metric ton ($35.60/ton)* 

3E10 $38.04/metric ton ($34.50/ton)* 

4C $37.85/metric ton ($34.33/ton) 

3C $41.07/metric ton ($37.25/ton)* 

2C $38.26/metric ton ($34.70/ton)* 

JPCP, 29.21 cm (11.5 inch) $23.99/square meter ($20.06/square yd)* 

21AA $5.13/square meter ($4.29/square yd)* 

4G, modified $4.70/square meter ($3.93/square yd)* 

Geotextile Separator $0.93/square meter ($0.78/square yd)* 

Class II sand $5.63/cubic meter ($4.31/cubic yd)* 

15.24-cm (6-inch) base underdrains $10.73/meter ($3.27/ft)* 

15.24-cm (6-inch) subbase underdrains $11.75/meter ($3.58/ft)* 
211 

We divide the State into three zones and obtain average unit prices for each of the zones. 

Prices with an * are chosen from one of the zones (zone 2); otherwise it is a statewide 

average price. 

Typical Service Life

In Michigan, we use a 20-year design life. Our service life for design 1 above (rural 

interstate) would be 26 years for both HMA and JPCP. Both would typically receive two 

cycles of preventive maintenance during that time. For the HMA, it would be a crack seal 

that averages about year 10 and a Distress Index of 29 and a mill and resurface that 

averages about year 13 and a Distress Index of 18. For the JPCP, it would be a joint 

reseal that averages about year 9 and a Distress Index of 6 and a CPR (full-depth repairs, 

spall repair, etc.) that averages about year 15 and a Distress Index of 18. 

The service life for design 2 (rural two-lane) would be 30 years. Again, two cycles of the 

same preventive maintenance fixes for both pavement types. For HMA, the first cycle 

would be at an average age of 11 and Distress Index of 27 and the second cycle would be 

at an average age of 15 and Distress Index of 20. For JPCP, the first cycle would average 

age 8 and Distress Index of 6 for cycle one and age 16 and Distress Index of 20 for cycle 

two. 

The Distress Index is Michigan’s measure of the pavement condition. The pavement is 

visually surveyed and distresses are logged. Each distress has a point value assigned to it 

depending on its severity. The points are accumulated into 0.16 km (0.1 mile) segments 

that are called the Distress Index. When a pavement reaches a Distress Index of 50, it is 

considered to have 0 life left and needs to be rehabilitated/reconstructed. The amount and 

type of distresses can vary significantly for the same Distress Index. For that reason, 

typical distress values at which preventive maintenance fixes are applied, are not given 

here. 

Michael Eacker 

Pavement Design Engineer 

eackerm@michigan.gov 

517-322-3474 

3-1-05 
212 

STATE OF NEW YORK 

Department of Transportation 

50 Wolf Road 

Albany, NY 12232 

MEMO via e-mail 

Subject: Response to Pooled Fund Study 

Date: June 15, 2004 

From: RMORGAN@dot.state.ny.us 

To: newt@nce.reno.nv.us 

CC: WYANG@dot.state.ny.us 

Newton, 

In response to your questionnaire for the Pooled Fund Study, “Effect of Multiple Freeze 

Cycles & Deep Frost Penetration on Pavement, Phase 2,” attached are NYSDOT standard 

pavement sections from our 2000 Comprehensive Design Manual. 

For your information, NYS does not use rigid pavement for highways with less than 

10,000,000 ESALs. Also MR (MPa in NYS) is used for HMA pavement only, and our 

typical allowed maximum is 62 MPa (9,000 psi). For rigid pavement, NYSDOT uses the 

Modulus of Subgrade Reaction (k-value). 

You may obtain copies of NYSDOT’s 2002 Standard Specifications and our Weighted 

Average Bid Price Book on NYDOT’s website at: www.dot.state.ny.us/pubs/publist.html. 

If you need additional information or have questions concerning this information, please 

feel free to contact me or Wes Yang. 
213 

 1 inch = 2.54 cm; 1 ft = 0.3 m 

Figure 99. Diagram. Typical portland cement concrete pavement section 

for New York. 

 1 inch = 2.54 cm; 1 ft = 0.3 m 

Figure 100. Diagram. Typical hot-mix asphalt pavement section for New York. 
214 

Table 67. PCC thickness table for New York. 

80-kN 

ESALs 

PCC Slab Thickness 

4.2 m driving lane 

slab width 

PCC Slab Thickness 

3.6 m driving lane 

slab width 

millions mm mm 

≤ 22 225 (9 inch) 225 (9 inch) 

> 22 ≤ 36 225 (9 inch) 250 (10 inch) 

> 36 ≤ 65 225 (9 inch) 275 (11 inch) 

> 65 – 100 250 (10 inch) 300 (12 inch) 

> 100 – 165 275 (11 inch) 325 (12.75 inch) 

> 165 – 250 300 (12 inch) 3251 (12.75 inch) 

> 250 – 400 325 (12.75 inch) 3251 (12.75 inch) 1 For ESALs over 165 million, 3.6 million, 3.6 m untied slabs may not be used for the right hand 

driving lane. Use either 3.6 m tied slabs, 4.2 m untied slabs, or 4.2 m tied slabs. 

Table 68. HMA thickness table for New York (Mr=28 MPa). 

Mr = 28 MPa 

80 kN ESALs Over 

Design Life 

Total HMA 

Thickness 

Select Granular 

Subgrade Thickness 

millions mm mm 

< 2 155 (6 inches) 0 

2 – 4 175 (7 inches) 0 

> 4 – 8 200 (8 inches) 0 

> 8 – 13 225 (9 inches 0 

> 13 – 23 250 (10 inches) 0 

> 23 – 45 250 (10 inches) 150 (6 inches) 

> 45 – 80 250 (10 inches) 300 (12 inches) 

> 80 – 140 250 (10 inches) 450 (18 inches) 

> 140 – 300 250 (10 inches 600 (24 inches) 
215 

Table 69. HMA thickness table for New York (Mr=34 MPa). 

Mr = 34 MPa 

80 kN ESALs Over Design 

Life 

Total HMA 

Thickness 

Select Granular Subgrade 

Thickness 

millions mm mm 

< 4 155 (6 inches) 0 

4 – 7 175 (7 inches) 0 

> 7 – 13 200 (8 inches) 0 

> 13 – 23 225 (9 inches) 0 

> 23 – 40 250 (10 inches) 0 

> 40 – 70 250 (10 inches) 150 (6 inches) 

> 70 – 130 250 (10 inches) 300 (12 inches) 

> 130 – 235 250 (10 inches) 450 (18 inches) 

> 235 – 300 250 (10 inches) 600 (24 inches) 

Table 70. HMA thickness table for New York (Mr=41 MPa). 

Mr = 41 MPa 

80 kN ESALs Over Design 

Life 

Total HMA 

Thickness 

Select Granular Subgrade 

Thickness 

millions mm mm 

< 6 155 (6 inches) 0 

6 – 11 175 (7 inches) 0 

> 11 – 20 200 (8 inches) 0 

> 20 – 35 225 (9 inches) 0 

> 35 – 60 250 (10 inches) 0 

> 60 – 110 250 (10 inches) 150 (6 inches) 

> 110 – 200 250 (10 inches) 300 (12 inches) 

> 200 – 300 250 (10 inches) 450 (18 inches) 
216 

Table 71. HMA thickness table for New York (Mr=48 MPa). 

Mr = 48 MPa 

80 kN ESALs Over Design 

Life 

Total HMA 

Thickness 

Select Granular Subgrade 

Thickness 

millions mm mm 

< 8 155 (6 inches) 0 

8 – 16 175 (7 inches) 0 

> 16 – 30 200 (8 inches) 0 

> 30 – 50 225 (9 inches) 0 

> 50 – 85 250 (10 inches) 0 

> 85 – 160 250 (10 inches) 150 (6 inches) 

> 160 – 300 250 (10 inches) 300 (12 inches) 

Table 72. HMA thickness table for New York (Mr=55 MPa). 

Mr = 55 MPa 

80 kN ESALs Over Design 

Life 

Total HMA 

Thickness 

Select Granular Subgrade 

Thickness 

millions mm mm 

< 12 155 (6 inches) 0 

12 – 20 175 (7 inches) 0 

> 20 – 40 200 (8 inches) 0 

> 40 – 65 225 (9 inches) 0 

> 65 – 115 250 (10 inches) 0 

> 115 – 215 250 (10 inches) 150 (6 inches) 

> 215 – 300 250 (10 inches) 300 (12 inches) 
217 

Table 73. HMA thickness table for New York (Mr=62 MPa). 

Mr = 62 MPa 

80 kN ESALs Over Design 

Life 

Total HMA 

Thickness 

Select Granular Subgrade 

Thickness 

millions mm mm 

< 15 155 (6 inches) 0 

15 – 30 175 (7 inches) 0 

> 30 – 50 200 (8 inches) 0 

> 50 – 90 225 (9 inches) 0 

> 90 – 150 250 (10 inches) 0 

> 150 – 300 250 (10 inches) 150 (6 inches) 
218 

STATE OF NORTH CAROLINA 

Department of Transportation 

P.O. Box 25201 

Raleigh, NC 27611 

July 20, 2004 

MEMO TO: Newton Jackson, P.E. 

Principal Engineer 

Nichols Consulting Engineers, Chtd. 

1885 S. Arlington, Suite 111 

Reno, NV 89509 

FROM: Clark S. Morrison, P.E. 

 State Pavement Design Engineer 

 Pavement Management Unit, NCDOT 

RE: Frost Penetration Pooled Fund Study Survey 

Attached are the pavement designs you requested. Our Standard Specifications and 

Average Unit Bid Prices are available online at: 

http://www.doh.state.nc.us/preconstruct/highway/dsn_srvc/contracts/ 

Please let me know if you have any difficulty with this site. This part of NCDOT is 

reorganizing, and their website address and the content may be changing in the near 

future. If you need help, please call me at 919-250-4094. 

RURAL INTERSTATE (four lanes)

30-year design 

30,000,000 ESALs 

Frost-susceptible fine grained soil, MR 69,000 kPa (10,000 PSI) 

Use S = 3.0, R = 1.5 (Typical values used in North Carolina Western Divisions) 

Recommended flexible pavements: 

 <1A> <1B> Shoulder 

Surface Course 100 mm (4 inches) 

S12.5 C 

100 mm (4 inches) 

S12.5 C 

100 mm (4 inches) 

S12.5 C 

Intermediate Course 75 mm (3 inches) I19.0 

C 

75 mm (3 inches) 

I19.0 C NA 

Base Course 230 mm (9 inches) 

B25.0 C 

140 mm (5.5 inches) 

B25.0 C NA 
219 

Granular Base 

Course 

250 mm (10 inches) 

ABC 

250 mm (10 inches) 

ABC 

Variable Depth 

Stabilized Subgrade 

NA 

200 mm (8 inches) 

Lime or 175 mm (7 

inches) Cement 

NA 

Total Depth 660 mm (26 inches) 570 mm (22.5 inches) Variable 

Recommended rigid pavement: 

 Shoulders 

280 mm (11 inches) Jointed Concrete 280 mm (11 inches) 

adjacent to mainline 

75 mm (3 inches) B25.0 B 115 mm (4.5 inches) ABC 

40 mm (1.5 inches) S9.5 B NA 

Lime/Cement Stabilization NA 

Total Depth 395 mm (15.5 inches) Variable 

RURAL PRIMARY (two lanes)

30-year design 

5,000,000 ESALs 

Frost-susceptible fine grained soil, MR 69,000 kPa (10,000 psi) 

Use S = 3.0, R = 1.5 (Typical values used in North Carolina Western Divisions) 

Recommended flexible pavements: 

 <1A> <1B> Shoulder 

Surface Course 75 mm (3 inches) 

S9.5 C 

75 mm (3 inches) S9.5 

C 

75 mm (3 inches) S9.5 

C 

Intermediate Course 75 mm (3 inches) 

I19.0 C 

100 mm (4 inches) 

I19.0 C NA 

Base Course 105 mm (4.5 inches) 

B25.0 C NA NA 

Granular Base Course 255 mm (10 inches) 

ABC 

255 mm (10 inches) 

ABC Variable Depth 

Stabilized Subgrade 

NA

205 mm (8 inches) 

Lime or 180 mm (7 

inches) Cement 

NA 

Total Depth NA 430 mm (17 inches) Variable 
220 

Recommended rigid pavement: 

 

 205 mm (8 inches) Jointed Concrete 

 75 mm (3 inches) B25.0 B 

 40 mm (1.5 inches) S9.5 B 

 Lime/Cement Stabilization 

Total Depth 320 mm (12.5 inches) 
221 

STATE OF OHIO 

Department of Transportation 

P.O. Box 899 

Columbus, OH 43216-0899 

ODOT on line is found at: 

http://www.dot.state.oh.us/default.htm 

In general all ODOT standards, specifications and design policies are found at: 

http://www.dot.state.oh.us/drrc/ 

ODOT specifications can be found at: 

http://www.dot.state.oh.us/construction/OCA/Specs/2002CMS/Specbook2002/Specbook

2002.htm 

Supplemental Specifications and approved plan notes can be found at: 

http://www.dot.state.oh.us/construction/OCA/Specs/SSandPN2002/default.htm 

Warranty Policy can be found at: 

http://www.dot.state.oh.us/construction/OCA/Warranty/WarrantyDocs/warpolicy.htm 

Warranty Guidelines can be found at: 

http://www.dot.state.oh.us/construction/OCA/Warranty/WarrantyDocs/Warranty%20App

%20Guidelines%20Rev3.pdf 

Under drain policy can be found at: 

http://www.dot.state.oh.us/se/hy/LD2/Sec1000/sec1000bookmarked.pdf 

ODOT Pavement Design & Rehabilitation Manual can be found at: 

http://www.dot.state.oh.us/pavement/publications.htm 

Regarding a pavement buildup, our design standards do not reference a standard 

pavement section. ODOT does not have a catalogue type design approach. Instead we put 

together a unique design buildup for every project. In general, all rigid pavements use 

tied concrete shoulders, and all pavement types require pipe underdrains. 

Rural Interstate—Flexible (93 AASHTO) 

30,000,000 flexible ESAL 

Mr = 69,000 kPa (10,000 psi) 

R= 90%; Overall Deviation = 0.49; Initial Serviceability = 4.50; 

Terminal Serviceability = 2.5 

SN=5.12 
222 

Final Design—Rural Interstate—Flexible: 

290 mm (11.5 inches) Item 880 Asphalt Concrete with Warranty 

150 mm (6 inches) Item 304 Aggregate Base 

 Item 605 Underdrains 

Rural Interstate—Rigid (93 AASHTO) 

30,000,000 rigid ESAL 

Mr = 69,000 kPa (10,000 psi) 

R=90%; Overall Deviation = 0.39; Modulus of rupture = 4800 kPa (700 psi); Modulus of 

Elasticity = 34,500 MPa (5,000,000 psi); J=2.8; k = 38.5 N/cubic cm (142 pci) { Mr 

(subgrade) = 69,000 kPa (10,000 psi), Mr (subbase) = 207,000 kPa (30,000 psi), subbase 

= 150 mm (6 in) LOS=1}; Cd=1.0; Initial Serviceability = 4.2; Terminal Serviceability = 

2.5 

PCC thickness = 287 mm (11.3 inches) round to 290 mm (11.5 inches) 

Final Design—Rural Interstate—Rigid: 

290 mm (11.5 inches) Item 884 Concrete Pavement With Warranty 

 150 mm (6 inches) Item 304 Aggregate Base 

 Item 605 Underdrains 

Rural Primary—Flexible (93 AASHTO) 

5,000,000 flexible Esal 

Mr = 69,000 kPa (10,000 psi) 

R= 85%; Overall Deviation = 0.49; Initial Serviceability = 4.50; Terminal Serviceability 

= 2.5 

SN= 3.8 

Final Design—Rural Interstate—Flexible: 

205 mm (8 inches) Item 880 Asphalt Concrete with Warranty 

150 mm (6 inches) Item 304 Aggregate Base 

 Item 605 Underdrains 

Rural Primary—Rigid (93 AASHTO) 

5,000,000 rigid Esal 

Mr = 69,000 kPa (10,000 psi) 

R=85%; Overall Deviation = 0.39; Modulus of rupture = 4800 kPa (700 psi); Modulus of 

Elasticity = 34,500 MPa (5,000,000 psi); J=2.8; k = 38.5 N/cubic cm (142 pci) { Mr 

(subgrade) = 69,000 kPa (10,000 psi), Mr (subbase) = 207,000 kPa (30,000 psi), subbase 

= 150 mm (6 in) LOS=1}; Cd=1.0; Initial Serviceability = 4.2; Terminal Serviceability = 

2.5 

PCC thickness = 208 mm (8.2 in) round to 205 mm (8 in) 

Final Design—Rural Interstate—Rigid: 

205 mm (8 inches) Item 884 Concrete Pavement with Warranty 

 150 mm (6 inches) Item 304 Aggregate Base 

 Item 605 Underdrains 
223 

The research objective mentions frost penetration. In Ohio, all A-4b (silts>50% of 

number 200 sieve) must be removed by specification. Soils work is done to make 

recommendations concerning both lime and cement stabilization, but current design 

policy does not figure this strength into the pavement design. This is going to change 

within the next few years. Furthermore, typical subgrade strengths used in design for 

ODOT projects are closer to 49,600 kPa (7,200 psi). 

If you need any further information, please do not hesitate to contact myself or 

Aric Morse @ 614-995-5994. 
224 

STATE OF PENNSYLVANIA 

Department of Transportation 

400 North Street, 6th Floor 

Harrisburg, Pa. 17120 

Standard Roadway Section-Designed Section

Base drains are required on all interstate projects. For non-interstates, base drains should 

be installed on all projects where subsurface water is a problem. 

Rural Interstate (four lanes) Rigid and Flexible

30-year design 

30,000,000 ESALs 

Frost susceptible fine grained soil MR 69,000 kPa (10,000 psi) 

Table 74. Pavement structure information for rural interstate in Pennsylvania. 

PAVEMENT COURSE 

MAIN 

LINE 

mm 

(inches) 

SHLDRS 

mm 

(inches) 

Flexible (minimum section): NA NA 

Superpave Asphalt Mixture Design, HMA Wearing Course, RPS, pg 

76-22, >/= 30 Million ESALs, 9.5 mm mix, 40-mm (1.5-inch) depth, 

SRL-E 

40 (1.5) 20 

(0.75) 

Superpave asphalt mixture design, HMA binder course, RPS, pg 76-22, 

>/= 30 million ESALs, 19.0 mm mix, 50-mm (2-inch) depth 

50 (2) NA 

Superpave asphalt mixture design, HMA base course, pg 64-22, 10 to < 

30 million ESALs, 25.0 mm mix 

330 (13) 100 

(4) 

Subbase 255-mm (10-inch) depth (number 2a) 255 (10) NA 

Total Depth: 675 

(26.5) 

120 

(4.75) 

Rigid (minimum section): NA NA 

Plain Cement Concrete Pavement, 330-mm (13-inch) depth 330 (13) Same as 

mainline 

Asphalt Treated Permeable Base Course, 100-mm (4-inch) depth 

(CTPBC allowed, but rarely chosen by contractors) 

100 (4) Same as 

mainline 

Subbase 100-mm (4-inch) depth (number 2a) 100 (4) Same as 

mainline 

Total Depth: 530 

(21) 

530 

(21) 
225 

Rural Primary (two lanes) Rigid and Flexible

30-year design 

5,000,000 ESALs 

Frost susceptible fine grained soil MR 69,000 kPa (10,000 psi) 

Table 75. Pavement structure information for rural primary in Pennsylvania. 

PAVEMENT COURSE 

MAIN 

LINE 

mm 

(inches) 

SHLDRS

mm 

(inches) 

Flexible (minimum section): NA NA 

Superpave asphalt mixture design, HMA wearing course, pg 64-22, 3 

to < 10 million ESALs, 9.5 mm mix, 40-mm (1.5-inch) depth, SRL-H 40 (1.5) 20 (0.75) 

Superpave asphalt mixture design, HMA wearing course, pg 64-22, 3 

to < 10 million ESALs, 9.5 mm mix, 40-mm (1.5-inch) depth, SRL-G 50 (2) NA 

Superpave asphalt mixture design, HMA binder course, pg 64-22, 3 to 

< 10 million ESALs, 19.0 mm mix 130 (5) 100 (4) 

Superpave asphalt mixture design, HMA base course, pg 64-22, 0.3 to 

< 3 million ESALs, 25.0 mm mix, 130-mm (5-inch) depth 230 (9) NA 

Total Depth: 445 (17.5) NA 

Rigid (minimum section): NA NA

Plain Cement Concrete Pavement, 205-mm (8-inch) depth 205 (8) Same as 

mainline 

Subbase 100-mm (4-inch) depth (no OGS) 100 (4) Same as 

mainline 

Subbase 100-mm (4-inch) depth (number 2a) 100 (4) Same as 

mainline 

Total Depth: 405 (16) 535 (21) 

Standard Specifications

http://www.dot2.state.pa.us/ 

Follow links for: References, Highway Related Pubs, Publication 408 (on page 4 of the 

list). 

GPS and SPS test sites (hat represent the performance data included in the LTPP 

database) were built using ID-2 and ID-3 mixes. 

Test Procedures

AASHTO test procedures 

Average Unit Bid Prices
226 

In addition to the specifications we will also need the average unit bid prices or the prices 

you would prefer we use in this study, for each of the bid items noted in your standard or 

design roadway section. 

Table 76. Average unit prices for Pennsylvania. 

Item Average Bid Price 

Superpave asphalt mixture design, HMA wearing course, RPS, pg 76-22, 

>/= 30 million ESALs, 9.5 mm mix, 40-mm (1.5-inch) depth, SRL-E 

$5.04/square meter 

($4.21/square yard) 

Superpave asphalt mixture design, HMA binder course, RPS, pg 76-22, >/= 

30 million ESALs, 19.0 mm mix, 50-mm (2-inch) depth 

$5.56/square meter 

($4.65/square yard) 

Superpave asphalt mixture design, HMA base course, pg 64-22, 10 to < 30 

million ESALs, 25.0 mm mix 

$46.00/metric ton 

($41.73/ton) 

Subbase 255-mm (10-inch) depth (number 2a) $14.39/square meter 

($12.03/square yard) 

Superpave asphalt mixture design, HMA wearing course, pg 64-22, 3 to < 

10 million ESALs, 9.5 mm mix, 40-mm (1.5-inch) depth, SRL-H 

$5.63/square meter 

($4.71/square yard) 

Superpave asphalt mixture design, HMA wearing course, pg 64-22, 3 to < 

10 million ESALs, 9.5 mm mix, 40-mm (1.5-inch) depth, SRL-G 

$5.98/square meter 

($5.00/square yard) 

Superpave asphalt mixture design, HMA binder course, pg 64-22, 3 to < 10 

million ESALs, 19.0 mm mix 

$5.55/square meter 

($4.64/square yard) 

Superpave asphalt mixture design, HMA base course, pg 64-22, 0.3 to < 3 

million ESALs, 25.0 mm mix, 130-mm (5-inch) depth 

$15.16/square meter 

($12.67/square yard) 

Subbase 230-mm (9-inch) depth (number 2a) $20.28/square meter 

($16.95/square yard) 

Plain Cement Concrete Pavement, 330-mm (13-inch) depth $82.06/square meter 

($68.60/square yard) 

Asphalt Treated Permeable Base Course, 100-mm (4-inch) depth $9.44/square meter 

($7.89/square yard) 

Plain Cement Concrete Pavement, 255-mm (8-inch) depth $61.05/square meter 

($51.04/square yard) 

Subbase 100 mm (4-inch) depth (No OGS) $6.38/square meter 

($5.33/square yard) 

Subbase 100-mm (4-inch) depth (number 2a) $6.09/square meter 

($5.09/square yard) 
227 

Typical Service Life for Standard Section

The amount of fatigue cracking, ride value, etc. when treatment is applied varies by 

district depending upon variables such as funding availability. Applicable sections of 

typical pavement maintenance timeline from the Publication 242, Pavement Policy 

Manual are as follows: 

New Bituminous, Bituminous Reconstruction and Bituminous Overlay 

5 years Seal coat shoulders if Type 1, 1S, 3, 4, 6 or 6S 

shoulders 

Do nothing if Type 1F, 1I, 6F, 6I, or 7 shoulders 

10 years 40 or 50 mm (1.5 or 2.0 inch) cold milling (recycling) 

 Full depth patch, 2% of pavement area 

40 or 50 mm (1.5 or 2.0 inch) bituminous inlay 

Saw and seal joints, as necessary 

Seal coat shoulders 

Maintenance and protection of traffic 

User delay 

15 years Seal coat shoulders 

20 years Full depth patch, 2% of pavement area 

60-psy leveling course 

 40 or 50 mm (1.5 or 2.0 inch) bituminous overlay 

Saw and seal joints, as necessary 

Type 7 paved shoulders 

Adjust guide rail and drainage structures, if necessary 

Maintenance and protection of traffic 

 User delay 

30 years Same as 10 years 

 35 years Seal coat shoulders 

New Concrete, Concrete Reconstruction and Unbonded Concrete Overlay 

10 years Clean and seal 25% of longitudinal joints 

Clean and seal 5% of transverse joints, 0% if neoprene seals are used 

Seal coat shoulders, if Type 1 paved shoulders 

20 years Concrete patch 2% of pavement area 

Diamond grind 50% of pavement area 

Clean and seal all longitudinal joints, including shoulders 

Clean and seal all transverse joints, 7% if neoprene seals are used 

Maintenance and protection of traffic 

User delay 
228 

30 years Concrete patch 5% of pavement area 

Clean and seal all joints 

600-psy leveling course 

90 or 100 mm (3.5 or 4.0 inch) bituminous overlay 

Saw and seal joints in overlay 

Type 7 paved shoulders 

Adjust all guide rail and drainage structures 

Maintenance and protection of traffic 

User delay 

35 years Seal coat shoulders 

Adjacent State Treatments

PennDOT is not aware of any treatments utilized by adjacent States. PA overdesigns for 

frost heave. Publication 242, PennDOT’s Pavement Policy Manual is available on line at 

ftp://ftp.dot.state.pa.us/public/pdf/pricelist.pdf. The link is towards the bottom of the third 

page. See the following section from Publication 242, Pavement Policy Manual for 

PennDOT design guidelines. 
229 

APPENDIX F. SPECIFICATION AND PAVEMENT DESIGN SUMMARIES 

Appendix F provides overviews of specifications and standard pavement designs for 

principal and rural interstate highways. The information was obtained from surveying the 

participating PFS as well as reviewing literature available on each SHA Web site. 
230

Table 77. AC wearing course specification summary. 

Pooled 

Fund State 

HMAC 

Reference 

AC 

Grade 

Mix 

Design Compaction Mix Voids (%) VFA (%) 

AK HMA, Type II, Class B PG 58 - 28 M 50 blow 3 to 5 

65 to 

78 

ID Plant Mix Pavement PG Superpave NA NA NA IL SP HMA Surface Course PG 58/64 - 22 Superpave/M 75 blow 3.5 to 4.5 

68 to 

78 

MI Gap Graded SP PG 70-22P Superpave NA NA NA NY 12.5-mm Superpave HMA PG 64-28 PG 70-22 Superpave NA 3 to 5 

65 to 

80 

NC S-12.5C PG 70 - 22 Superpave NA 3 to 5 

65 to 

75 

OH Item 880 (7-yr warranty) PG 64-22 PG 7022M Superpave/M 75/50/35blow 3.5 NA PA Superpave HMA Wearing Cr PG 64-22 PG 58-22 Superpave/M 75/50 blow 3 to 5 NA 

Table 78. AC wearing course specification summary (continued). 

Pooled 

Fund State HMAC Reference % Pass Number 4 % Pass Number 200 Fracture (%) La Wear Deg SS Loss (%) Antistri

(%) 

AK HMA, Type II, Class B 33 to 70 3 to 7 80 min 54 max 30 min 9 max NA ID Plant Mix Pavement NA 2 to 10 90 30 Max NA 12 max NA IL SP HMA Surface Course 24 to 65 2 to 6 NA 40 max NA 15 max <75 TSR MI Gap Graded SP NA NA NA NA NA NA NA NY 12.5-mm Superpave HMA NA 2 to 10 75 to 85 35 Max NA 18 max <80 TSR NC S-12.5C NA 4 to 8 95/90 55 Max NA 15 max 

<85 TSR 

(no F/T

OH Item 880 (7-yr warranty) 38 to 50 2 to 6 75/70 40 Max NA 15 max <70/80 TPA Superpave HMA Wearing Cr 

40 to 

80/M 3 to 6 85/80 40 max NA NA NA 
231

Table 79. AC base course specification summary. 

Pooled 

Fund 

State 

HMAC Reference AC Grade Mix Design Compaction Voids (%) VFA (%) 

VM

AK HMA, Type II, Class B PG 58 - 28 M 50 blow 3 to 5 65 to 78 ID 

Plant Mix Leveling Course PG Superpave NA NA NA 

IL 

SP HMA Binder Course PG 58/64 - 22 Superpave/M 75 blow 3.5 to 4.5 68 to 78 1

MI 4E50 PG 64 - 28 Superpave NA NA NA NY Binder and Base Course PG 64-28 PG 70-22 Superpave NA 3 to 5 65 to 80 NC B-25.0C PG 64 - 22 Superpave NA 3.0 to 5.0 65 to 75 OH 

Item 880 (7-yr warranty) PG 64-22 PG 64-28 Superpave/M 75/50/35blow 4 to 3.5 NA 1

PA 

Superpave HMA Base Cr PG 64-22 PG 58-22 Superpave/M 50 blow 3 to 5 NA 

Table 80. AC base course specification summary (continued). 

Pooled 

Fund 

State 

HMAC Reference % Pass Number 4 % Pass Number 200 Fracture (%) La Wear Deg SS Loss (%) Antistrip 

AK HMA, Type II, Class B 33 to 70 3 to 7 80 min 54 max 30 min 9 max NA ID Plant Mix Leveling Course NA 2 to 10 90 30 Max NA 12 max NA IL SP HMA Binder Course 24 to 65 2 to 6 NA 40 max NA 15 max 75% TSR MI 4E50 NA 4 to 8 95 50 Max NA NA NA NY Binder and Base Course NA 2 to 8 75 to 85 35 Max NA 18 max <80% TSR NC B-25.0C NA 3 to 7 95/90 55 max NA 15 max 

<85% TSR 

(no F/T) 

OH Item 880 (7-yr warranty) 38 to 50 2 to 6 75/70 40 Max NA 15 max <70/80 TSR PA Superpave HMA Base Cr 

40 to 

80/M 3 to 6 85/80 40 max NA NA NA 
232

Table 81. Asphalt-treated permeable base course specification summary. 

Pooled Fund 

State 

HMAC 

Reference AC Grade Max Agg. Size % Pass Number 4 % Pass Number 200 

AK NA NA NA NA NA ID NA NA NA NA NA IL NA NA NA NA NA MI NA NA NA NA NA NY ATPB Type1/2 

PG 64-28 PG 70-

22 

38 mm 

(1.5 inches) 3 TO 15 2 TO 4 

NC NA NA NA NA NA OH NA NA NA NA NA PA NA NA NA NA NA 

Table 82. Unbound base course specification summary. 

Pooled 

Fund 

State 

Reference Max Agg. Size 

% Pass 

number 

4 

% Pass 

number 

200 

Fracture La Wear Deg 

AK Grading D - 1 25 mm (1inch) 35 to 65 0 to 6 70% 50 max 

45 

min 

ID Rock Cap 75 mm (3 inches) 0 to 5 (0 to 5) Quarry 40 NA IL NA NA NA NA NA NA NA MI 21AA 25 mm (1 inch) 35 to 50 4 to 8 95% 50 max NA NY See Sub-base NA NA NA NA NA NA NC Aggregate Base Course 

38 mm (1.5 

inches) 35 to 55 4 to 10 none 

55 

Max NA 

OH Aggregate Base 50 mm (2 inches) 30 to 60 0 to 15 NA 

40 

Max NA 

PA 2A 50 mm (2 inches) 24 to 50 0 to 10 NA 

45 

Max NA 
233

Table 83. Subbase course specification summary. 

Pooled Fund 

State Reference 

Max Agg. 

Size 

mm (inches) 

% Pass 

Number 4 

% Pass 

Number200 

La 

Wear Deg LL PI 

AK Grading B 50 (2) 20 to 55 0 to 6 50 40 25 6 ID 

Granular Subbase 100(4) 30 to 75 0 to 15 40 NA NA NA 

IL CAM / BAM NA NA NA NA NA NA NA MI Class II Sand NA NA NA NA NA NA NA NY Subbase NA NA NA NA NA NA NA NC NA NA NA NA NA NA NA NA OH NA NA NA NA NA NA NA NA PA NA NA NA NA NA NA NA NA 

Table 84. Select subgrade specification summary. 

Pooled Fund 

State Reference 

Max Agg. 

Size 

mm (inches) 

% Pass 

1" 

% Pass 

#4 

% Pass 

#200 PI 

AK SM Type A NA NA 

20 to 

55% 0 to 6 6 

ID NA NA NA NA NA NAIL NA NA NA NA NA NAMI 

Granular Material Type II 75 (3) 

60 to 

100 NA 0 to 7 NA

NY NA NA NA NA NA NANC Lime stabilized NA NA NA NA NAOH NA NA NA NA NA NAPA NA NA NA NA NA NA
234

Table 85. Overview of rural interstate flexible pavement design. 

AK ID IL MI NY NC 

Layer 

Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Shou

HMA Surface Course 6 in. 6 in. 2.0 in. SP 2 in. SP 1.5 in. 1.5 in. 2 in. 2 in. 4 in. S12.5C 4 in. S12.5C 4 in. S12.5C 3.0 

It 8

HMA Binder Course NA NA 18.25 in. SP 6 in. SP 2 in. 2 in. 2 in. 2 in. 3 in. I19.0C 3 in. I19.0C NA 8.5 

88

HMA Base Course 1.8 in. 1.8 in. NA NA 3.75 in. 3.75 in. 3 in. 3 in. 9 in. B25.0C 5.5 in. B25.0C NA NA ATPB NA NA NA NA NA NA 4 in. 4 in. NA NA NA NA Total HMA 7.8 in. 7.8 in. 20.25 in. 8 in. 7.25 in. 7.25 in. 11 in. 11 in. 16.0 in. 12.5 in. 4.0 in. 11.5 UT Base Course 

12 in. 

Rock 

Cap 

12 in. 

Rock 

Cap NA 

12.25 in. 

to C 

6 in. 

21AA 

6 in. 

21AA NA NA 

10 in. 

ABC 

10 

in.ABC Variable 6.0 

It 3

UT Subbase Course 12 in. 12 in. NA NA 18 in. sand 18 in. sand 12 in. 12 in. NA NA NA NA Total UTBC 24 in. 24 in. NA NA 24 in. 24 in. 12 in. 12 in. 10 in. 10 in. Variable 6 inLime Treated Subgrade NA NA 12 in. NA NA NA NA NA NA 8 in. NA NA Other Subgrade Treatment 

Separation Geotextile NA NA NA NA NA NA NA NA NA NA 36 i

(not

Total Roadway Depth 31.8 in. 31.8 in. 32.25 in. 32.25 in. 31.25 in. 31.25 in. 23 in. 23 in. 26.0 in. 30.5 in. Variable 54.5 Underdrains 

 No pavements 

at 30 M-ESAL 

yes NA yes 4 in. yes 6 in. 

Yes or 

Daylight 

ATPB 

NA NA NA NA ye

Note: Grading specifications require replacement of A-4b silt within 0.9 m (3 ft) of SG. 

Measurements in inches (in.) (1 inch = 25.4 mm) 
235

Table 86. Overview of rural interstate rigid pavement design. 

AK ID IL MI NY NC 

Layer 

Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainlin

PCCP Depth 12 in. NA 10.5 in. 6 in. 11.5 in. 9 in. 10 in. 10 in. 11 11 11.5 inTreated Base Course 

2 in. 

ATPLC 

2 in. 

ATPLC 4 in. NA NA NA 4 in. ATPB 4 in. ATPB 3 in. B25.0B NA NA 

UT Base Course 

12 in. 

Rock Cap 

12 in. 

Rock 

Cap 

NA 8.5 in. 6 in. 4GMod 8.5 in. 4GMod NA NA 1.5 in. S9.5B 4.5 in. ABC 6 in. 

UT Subbase Course NA NA NA NA 10 in. IIA Sand 10 in. IIA Sand 12 in. 12 in. NA NA NA Total BC 14 in. 14 in. 4 in. 8.5 in. 16 in. 18.5 in. 16 in. 16 in. 4.5 in. 4.5 in. 6 in. Lime Treated Subgrade NA NA 12 in. 12 in. NA NA NA NA 8 in. 8 in. NA Other Subgrade Treatment NA NA NA NA Geotech fab.4G/sand NA NA NA NA NA 36 in. (note) Total Roadway Depth 26 in. 26 in. 26.5 in. 26.5 in. 27.5 in. 27.5 in. 26 in. 26 in. 23.5 in. 23.5 in. 53.5 inUnderdrains yes NA yes 4 in. yes 6 in. yes NA NA NA yes Other Subgrade Treatment 

No Pavements 

at 30 M ESAL 

Separation Geotextile NA NA NA NA NA NA NA NA NA NA 

Note: Grading specifications require replacement of A-4b silt within 0.9 m (3 ft) of SG. 

Measurements in inches (in.) (1 inch = 25.4 mm) 
236

Table 87. Overview of principal flexible pavement design. 

AK ID IL MI NY NC 

Layer 

Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Mainline Alt Shoulder Main

HMA Surface Course 

2 in. 2 in. 4.8 in. 4.8' 2 in. SP SC 2 in. SP SC 1.5 in. 1.5 in. 2 in. 2 in. 3 in. S12.5C 3 in. S12.5C 3 in. S12.5C 3 in

HMA Binder Course 

3 in. 3 in. 1.8 in. 1.8 in. 12 in. SP BC 6 in SP BC 2.0 in. 2.0 in. 2 in. 2 in. 3 in. I19.0C 4 in. I19.0C NA NA 

HMA Base Course NA NA NA NA NA NA 3.0 in. 3.0 in. 2 in. 2 in. 4.5 in. B25.0C NA NA 5 inATPB NA NA NA NA NA NA NA NA 4 in. 4 in. NA NA NA NA Total HMA 5 in. 5 in. 6.6 in. 6.6 in. 14 in. 8 in. 6.5 in. 6.5 in. 10 in. 10 in. 10.5 7 in. 3.0 in. 8 inUT Base Course 7 in. 7 in. 12 in. RC 12 in. RC NA 6 in. 6 in. 4Gmod 6 in. 4Gmod NA NA 10 in. ABC 10 in. ACB Variable 6 inUT Subbase Course 

12 in. 12 in. NA NA NA NA 

18 in. 

IIA 

Sand 

18 in. IIA 

Sand 12 in. 12 in. NA NA NA NA 

Total UBC 19 in. 19 in. 12' 12' 0 6 in. 24 in. 24 in. 12 in. 12 in. 10 in. 10 in. Variable 6 inLime Treated Subgrade 

NA NA NA NA 12 in. 12 in. NA NA NA NA NA 8 in. NA NA 

Other Subgrade Treatment 

36 in. 

Select 

36 in. 

Select NA NA NA NA NA NA NA NA NA NA NA 36 i

(not

Total Roadway Depth 

60 in. 60 in. 18.6 in. 18.6 in. 26 in. 26 in. 30.5 in. 30.5 in. 22 in. 22 in. 20.5 in. 25 in. Variable 50 i

Underdrain 

NA NA no NA no NA yes 6 in. u- drain yes or daylight ATPB NA NA NA NA yes 

Note: Grading specifications require replacement of A-4b silt within 0.9 m (3 ft) of SG. 

Measurements in inches (in.) (1 inch = 25.4 mm) 
237

Table 88. Overview of principal rigid pavement design. 

AK ID IL MI NY NC OH 

Layer 

Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline Shoulder Mainline S

PCCP Depth 10.5 10.5 9.75 in. 2 in. SP SC 8.5 4 in. HMA 9 in. 9 in. 8 in. 8 in. 8 in. T Base Course 

2 in. 

ATPLC 

2 in. 

ATPLC 

4 in. 

CAM/ 

BAM 

6 in. 

SP BC NA NA 4 in. ATPB 4 in. ATPB 

3 in. 

B25.0B/ 

1.5 in. 

S9.5B 

3 in. 

B25.0B/ 

1.5 in. 

S9.5B 

NA 

UT Base Course 

12 in. 

Rock 

Cap 

12 in. 

Rock 

Cap 

NA 5.75 Type C 6 in. 4Gmod 6 in. 4Gmod NA NA NA NA 6 in. 

UT Subbase Course NA NA NA NA 10 in. 10 in. 12 in. 12 in. NA NA NA Total BC 14 in. 14 in. 4 in. 5.75 in. 16 in. 16 in. 16 in. 16 in. 4.5 in. 4.5 in. 6 in. Lime Treated Subgrade NA NA 12 in. 12 in. NA NA NA NA 8 in. 8 in. NA Other Subgrade Treatment 

NA NA NA NA 

Geotech 

fab. 

4G/sand 

NA NA NA NA NA 36 in. (note) 

Total Roadway Depth 

24.5 in. 24.5 in. 25.75 in. 25.75 in. 26.5 in. 20 in. 26 in. 26 in. 20.5 in. 20.5 in. 50 in. 

Underdrains 

No PCC 

Pavement 

no NA no NA yes 6 in. yes yes NA NA yes 

Note: Grading specifications require replacement of A-4b silt within 0.9 m (3 ft) of SG. 

Measurements in inches (in.) (1 inch = 25.4 mm) 

239 

APPENDIX G: NCHRP 1-37A CALIBRATION FLOWCHART SAMPLE 

This appendix provides an example of a calibration methodology flowchart for the 

NCHRP 1-37A pavement design procedures. 

Source: J. Li et al., “PCCP Models for Rehabilitation/Reconstruction Decision Making,” Washington 

State Department of Transportation, Report No. WA-RD 588.2, Olympia, WA, August 2005. 

Figure 101. Flowchart. Example of NCHRP 1-37A calibration methodology 

flowchart. 

241 

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