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Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Reclplent's Catalog No. 

FHWA-RD-98-127 I 

4. Tltle and Subtltle 

DESIGN AND CONSTRUCTION OF PCC PAVEMENTS 

l~olume 11: Desim Features and Practices That Influence 

5. Repoll Date 

0cto ber 1998 

6. Performlng Organlzatlon Code 

L. Titus-Glover, E. B. Owusu-Antwi, T. Hoener, and M. I. Darter 

9. Perfonnlng Organlzatlon Name and Address 

~erf<rmilnce of Pavements 

7. Author(s) 

ERES ~oisu6ants, Inc. 

505 W. University Avenue 

Champaign, IL 61820-3915 

8. Performlng Organlzatbn Report No. 

12. Sponoorlng Agency Name and Address 

Office of Engineering R&D 

Federal Highway Administration 

6300 Georgetown Pike 

McLean, Virginia 22101-2296 

15. Supplementary Notes 

10. Work UnW No. (TRAIS) 

11. Contract or Grant No. 

DTPH61-94C-00218 

13. Type of Repoll and Perlod Covered 

Final Report 

1994 to 1998 

14. Sponsoring Agency Code 

I FHWA Contracting Officer's Technical Representative (COTR): Cheryl Allen Richter,P.E., HNR-30 

16. Abstract 

A study has been conducted to evaluate and analyze portland cement concrete (PCC) pavements in order to 

develop recommendations for the design and construction of long-lived concrete pavements. It involved a 

detailed evaluation and analysis of the PCC pavement data in the Long-Term Pavement Performance (LTPP) 

database using statistical techniques to determine the design features and construction practices that have a 

beneficial effect on long-term performance. 

The study focused on the development of practical recommendations that can be easily implemented by highway 

agencies to increase pavement life. This volume describes and provides information on design features and 

construction practices that improve pavement performance. A key focus was to develop canonical discriminant 

functions that can be used to discriminate between groups of pavements in the sense of being able to tell them 

apart. The pavements were grouped according to their performance classification, namely, above expectation, as 

expected, and below expectation The canonical functions consist of linear combinations of the variables that 

describe and quantify the pavement design features, site conditions, and construction practices. 

I This report is the second in a series of three volumes on the study. The other volumes are as follows: 

FHWA No. Vol No. Short Title 

FHWA-RD-98-052 I Summary of Design Features and Construction Practices That Influence 

Performance of Pavements 

FHWA-RD-98-113 I11 Improved PCC Performance Models 

I 

19. Securlty Ctasslf. (of thls report) 20. Securlty Classlf. (of thls page) 21. No of Pages 22. Prlce 

17. Key Words 

Canonical discriminant analysis, analysis of 

variance, concrete pavements, performance 

classification, design, roughness, distress 

I Unclassified I Unclassified 1 231 1 

18. Dlstrlbutlon Statement 

No restrictions. This document is available 

to the public through the National 

Technical Information Service, Springfield, 

Virginia 22161. 

I . I I 

Fonn DOT F 1700.7 (8-721 Reproduction of completed page authorized 
LENGTH 

AREA 

square millimeters mm2 square millimeters 0.0016 

square meters square meters 

square meters 

square kilometers 0.386 

VOLUME VOLUME 

fluid ounces 

cubic meters cubic meters 

cubic meters cubic meters 

NOTE: Volumes greater than 1000 I shall be shown in m3. 

MASS 

kigrams - 

short tons (2000 Ib) T 

(or 'metric ton") 

TEMPERATURE (exact) TEMPERATURE (exact) 

temperature 

ILLUMINATION ILLUMINATION 

foot-hberts foot-lambem fl 

FORCE and PRESSURE or STRESS FORCE and PRESSURE or STRESS 

rounding should be made to comply with Section 4 of ASTM E380. 
TABLE OF CONTENTS 

Section Page 

1 . INTRODUCTION ................................................ 1 

Background . ....................... ..... ... ...............*... 1 

Project Scope .................................................... 1 

Scope of Report .................................................. 2 

2 . METHOD OF PERFORMANCE EVALUATION ...................... 3 

Introduction ..................................................... 3 

Preliminary Statistical Analysis (Univariate and Bivariate Analysis) ..... 5 

Canonical Discriminant Analysis ................................... 7 

Analysis of Variance ............................................. 12 

Summary of Methods of Performance Evaluation .................... 17 

3 . EVALUATION OF SITE CONDITIONS. DESIGN FEATURES. AND 

CONSTRUCTION PRACTICES THAT INFLUENCE JPCP FAULTING . 19 

Introduction ................................................ 19 

Preliminary Evaluation of Transverse Joint Faulting Data ............. 21 

Level 1 Investigations ............................................ 23 

Level 2 Investigations ............................................ 24 

Canonical Discriminant Analysis for Faulting ....................... 44 

Analysis of Variance for JPCP Faulting ............................. 51 

Summary ....................................................... 56 

4 . EVALUATION OF SITE. DESIGN. AND CONSTRUCTION 

VARIABLES THAT INFLUENCE TRANSVERSE CRACKING ........ 59 

Introduction .................................................... 59 

Preliminary Evaluation of Transverse Cracking Data ................. 59 

Transverse Cracking Level 1 Investigation .......................... 61 

Transverse Cracking Level 2 Investigation .......................... 61 

Canonical Discriminant Analysis for JPCP Transverse Cracking ....... 75 

Analysis of Variance for JPCP Transverse Cracking .................. 77 

Summary ....................................................... 83 

iii 
TABLE OF CONTENTS (continued) 

Section Pace 

5 . EVALUATION OF SITE CONDITIONS AND PCC PAVEMENT 

DESIGN FEATURES THAT INFLUENCE ROUGHNESS ............. 85 

Introduction .................................................... 85 

Preliminary Investigation of Roughness ............................ 85 

Roughness Level 1 Analysis ...................................... 86 

Roughness Level 2 Analysis ...................................... 87 

Canonical Discriminant Analysis for JPCP Roughness .............. 119 

Analysis of Variance for JPCP Roughness ......................... 121 

Canonical Discriminant Analysis for JRCP Roughness .............. 128 

Analysis of Variance for JRCP Roughness ......................... 130 

Canonical Discriminant Analysis for CRCP Roughness .............. 133 

Analysis of Variance for CRCP Roughness ......................... 136 

6 . CONCLUSIONS AND RECOMMENDATIONS .................... 139 

Conclusions ................................................... 139 

Recommendations . Design Features that Influence Performance ..... 139 

Recommendations . Construction Practices that Influence 

Performance ................................................... 143 

Recommendations - Site Conditions that Influence Performance ...... 144 

Recommendations for Future Research ............................ 144 

APPENDIXA ..................................................... 145 

APPENDIXB ..................................................... 197 

REFERENCES ................................................... 213 
LIST OF FIGURES 

Figure Page 

1 Normally distributed data ....................................... 6 

2 Data that are skewed or deviating from normal ..................... 6 

3 Graphical representation of pavements performing as 

expected, below, and above expectation .......................... 11 

4 Flow chart showing canonical discriminant analysis procedure ....... 13 

5 Typical plots of canonical variables .............................. 14 

6 Relation between regression and analysis of variance models ........ 16 

7 Plot of faulting versus cumulative ESAL's for doweled and undoweled 

PCC pavements ............................................... 22 

8 Plots showing the influence of the presence of dowels and climatic 

region on the faulting of JPCP and JRCP sections ................... 23 

9 Plots of JPCP and JRCP transverse joint faulting versus traffic for 

different freezing index categories (all climatic regions) ............. 26 

10 Plots of JPCP and JRCP transverse joint faulting versus traffic for 

different levels of freeze-thaw cycles (all climatic regions included) ... 27 

11 Plots of JPCP and JRCP transverse joint faulting versus traffic for 

different levels of average annual precipitation (all climatic regions 

included) .................................................... 27 

12 Plots of JPCP and JRCP transverse joint faulting versus traffic for 

different levels of average annual temperature range (all climatic 

regions included) ............................................. 29 

13 Plots of JPCP and JRCP transverse joint faulting versus traffic for 

different levels of average annual number of wet days (all climatic 

regions included) ............................................. 29 

14 Plots of JPCP and JRCP transverse joint faulting versus traffic for 

different subgrade type categories (all climatic regions included) ..... 30 

15 Plots showing the influence of subgrade type, dowels, and climate 

on faulting of JPCP and JRCP ................................... 31 

16 Plots showing the influence of k-value on the faulting of JPCP and 

JRCP ........................................................ 32 

17 Plots showing the influence of k-value, dowels, and climate on the 

faulting of JPCP and JRCP ...................................... 33 

18 Plots showing the influence of thickness and dowels on faulting 

of JPCP and JRCP ............................................. 35 

19 Mots showing the influence of thickness and climate on the faulting 

of undoweled JPCP and JRCP ................................... 35 
LIST OF FIGURES (continued) 

Figure 

20 Plots showing the influence of thickness and climate on the faulting 

PaPe 

of doweled JPCP and JRCP ..................................... 36 

21 Plots showing the influence of joint spacing on the faulting of 

doweled and undoweled JPCP .................................. 36 

22 Plots showing the influence of joint spacing and climate on faulting 

of doweled JPCP .............................................. 37 

23 Plots showing the influence of joint spacing and climate on faulting 

of undoweled JPCP ............................................ 38 

24 Plots showing the influence of edge drains on faulting of doweled 

and undoweled JPCP and JRCP sections .......................... 39 

25 Plots showing the influence of dowels and climate on JPCP and JRCP 

faulting in wet-freeze and wet-nofreeze regions .................... 40 

26 Plots showing the influence of base type and dowels on faulting of 

JPCPandJRCP ............................................... 41 

27 Plots showing the influence of base type and climatic region on 

faulting of doweled JPCP and JRCP sections ....................... 41 

28 Plots showing the influence of base type and climatic region on 

faulting of undoweled JPCP and JRCP sections .................... 42 

29 Plots showing the influence of PCC elastic modulus on faulting of 

doweled and undoweled JPCP and JRCP sections (all climatic 

regionsincluded) ............................................. 43 

30 Plots showing the influence of lane width on faulting of doweled 

and undoweled JPCP and JRCP sections ..*....................... 43 

31 Plots showing the influence of shoulder type on faulting of doweled 

and undoweled JPCP and JRCP ................................. 45 

32 Plots showing the influence of joint sealant on faulting of doweled 

and undoweled JPCP and JRCP ................................. 45 

33 Plots showing the influence of joint sealant type on faulting of 

doweled and undoweled JPCP and JRCP ......................... 46 

34 Plots showing the influence of joint sealant type and climatic region 

on the faulting of doweled and undoweled JPCP and JRCP LTPP 

sections ...................................................... $6 

35 Plots showing the influence of transverse joint forming method and 

dowels on the transverse joint faulting of JPCP and JRCP ............ 48 

36 Plot showing the influence of dowel placement method on transverse 

joint faulting of JPCP and JRCP .................................. 48 

37 A plot of CANFl versus CANF2 for each observation ............... 50 
Page 

LIST OF FIGURES (continued) 

Figure 

38 Plot showing the influence of slab thickness on the transverse 

fatigue cracking of JPCP LTPP sections (all climatic regions 

included) ................................ .. ........... 62 

39 Plots showing the influence of slab thickness and climatic region 

on the transverse fatigue cracking of JPCP sections ................. 63 

40 Plots of JPCP fatigue cracking versus traffic as influenced by slab 

thickness and freezing index (all climatic regions included) .......... 65 

41 Plots of JPCP fatigue cracking versus traffic as influenced by slab 

thickness and average annual precipitation (all climatic regions 

included) .................................................... 66 

42 Plots showing the influence of slab thickness and subgrade type on 

the cracking of JPCP LTPP sections (all climatic regions included) . . , . 66 

43 Plots showing the influence of slab thickness and k-value on the 

transverse fatigue cracking of JPCP LTPP sections (all climatic 

regionsincluded) ............................................. 68 

44 Plots showing the influence of k-value category and climatic region 

on the transverse fatigue cracking of JPCP sections with thickness 

2250mm .................................................... 68 

45 Plots showing the influence of k-value category and climatic region 

on the transverse fatigue cracking of JPCP LTPP sections with 

thickness < 250 mm ........................................... 69 

46 Plots showing the influence of slab thickness and average joint 

spacing on the transverse fatigue cracking of JPCP LTPP sections 

(all climatic regions included) ................................... 70 

47 Plots showing the influence of slab thickness and base type on the 

transverse fatigue cracking of JPCP LTPP sections (all climatic regions 

included) .................................................... 71 

48 Plots showing the influence of slab thickness and modulus of 

rupture on the transverse fatigue cracking of JPCP LTPP sections 

(all climatic regions included) ................................... 71 

49 Plots showing the influence of paver type and slab thickness on the 

transverse fatigue cracking of JPCP LTPP sections .................. 73 

50 Plots showing the influence of curing method and slab thickness on 

the transverse fatigue cracking of JPCP LTPP sections (all climatic 

regions included) .......,..................................... 73 

51 Plots showing the influence of transverse joint forming method and 

slab thickness on the transverse fatigue cracking of JPCP (all climatic 

regions included) ............................................. 74 
LIST OF FIGURES (continued) 

Fizure Page 

52 A plot of CANT1 versus CANT1 for each observation of the data ..... 76 

Plots showing the influence of slab thickness on the IRI of JPCP, JRCP, 

and CRCP sections (all climatic regions included) .................. 89 

Plots showing the influence of slab thickness and climatic region on 

the IRI of JPCP LTPP sections ................................... 90 

Plots showing the influence of slab thickness and climatic region on 

the IRI of JRCP LTPP sections ................................... 90 

Plots showing the influence of slab thickness and climatic region on 

the IRI of CRCP LTPP sections .................................. 92 

Plots of JPCP IRI versus traffic as influenced by slab thickness and 

freezing index (all climatic regions included) ...................... 92 

Plots of JRCP IRI versus traffic as influenced by slab thickness and 

freezing index (all climatic regions included) ...................... 93 

Plots of CRCP IRI versus traffic as influenced by slab thickness and 

freezing index (all climatic regions included) ...................... 93 

Plots of JPCP IRI versus traffic as influenced by slab thickness and 

freeze-thaw cycles (all climatic regions included) ................... 94 

Plots of JPCP IRI versus traffic as influenced by slab thickness and 

average annual precipitation (all climatic regions included) .......... 94 

Plots of JRCP IRI versus traffic as influenced by slab thickness and 

average annual precipitation (all climatic regions included) .......... 95 

Plots of CRCP IRI versus traffic as influenced by slab thickness and 

average annual precipitation (all climatic regions included) .........* 96 

Plots of JPCP IRI versus traffic as influenced by slab thickness and 

average annual temperature range (all climatic regions included) ..... 96 

Plots of JRCP IRI versus traffic as influenced by slab thickness and 

average annual temperature range (all climatic regions included) ..... 97 

Plots of CRCP IRI versus traffic as influenced by slab thickness and 

average annual temperature range (all climatic regions included) ..... 97 

Plots of JPCP IRI versus traffic as influenced by slab thickness and 

average annual number of wet days (all climatic regions included) .... 99 

Plots of JRCP IRI versus traffic as influenced by slab thickness and 

average annual number of wet days (all climatic regions included) .... 99 

Plots of CRCP IRI versus traffic as influenced by slab thickness and 

average annual number of wet days (all climatic regions included) ... 100 

Plots showing the influence of slab thickness and subgrade type on 

the IRI of JPCP sections (all climatic regions included) ............. 100 

viii 
LIST OF FIGURES (continued) 

Figure Page 

71 Plots showing the influence of slab thickness and subgrade type on 

the IRI of JRCP sections (all climatic regions included) ............. 101 

Plots showing the influence of slab thickness and subgrade type on 

the IRI of CRCP sections (all climatic regions included) ............ 101 

Plots showing the influence of slab thickness and backcalculated 

static k-value on the IRI of JPCP sections (all climatic regions 

included) ................................................... 102 

Plots showing the influence of slab thickness and backcalculated static 

k-value on the IRI of JRCP sections (all climatic regions included) .... 102 

Plots showing the influence of slab thickness and average joint 

spacing on the IRI of JPCP sections (all climatic regions included) .... 104 

Plots showing the influence of slab thickness and average joint 

spacing on the IRI of JRCP sections (all climatic regions included) . , . 104 

Plots showing the influence of slab thickness and base type on the 

IRI of JPCP sections (all climatic regions included) ................ 105 

Plots showing the influence of slab thickness and base type on the 

IRI of JRCP sections (all climatic regions included) ................ 106 

Plots showing the influence of slab thickness and base type on the 

IRI of CRCP sections (all climatic regions included) ................ 106 

Mots showing the influence of slab thickness and base elastic 

modulus on the IRI of JPCP sections (all climatic regions included) ... 108 

Plots showing the influence of slab thickness and PCC elastic 

modulus on the IRI of JPCP LTPP (all climatic regions included) ..... 108 

Plots showing the influence of slab thickness and outer shoulder 

type on the IRI of JPCP sections (all climatic regions included) ...... 110 

Plots showing the influence of slab thickness and outer shoulder 

type on the IRI of JRCP sections (all climatic regions included) ...... 110 

Plots showing the influence of slab thickness and outer shoulder 

type on the IRI of CRCP sections (all climatic regions included) ...... 111 

Plots showing the influence of paver type and slab thickness on the 

IRI of JPCP LTPP sections (all climatic regions included) ........... 112 

Plots showing the influence of curing method and slab thickness 

on the IRI of JPCP LTPP sections (all climatic regions included) ..... 113 

Mots showing the influence of texture method and slab thickness 

on the IRI of JPCP LTPP sections (all climatic regions included) ..... 114 

Plots showing the influence of texture method and slab thickness 

on the IRI of JRCP LTPP sections (all climatic regions included) ..... 115 
LIST OF FIGURES (continued) 

Paae 

89 Plots showing the influence of texture method and slab thickness 

on the IRI of CRCP LTPP sections (all climatic regions included) ..... 115 

90 Plots showing the influence of transverse joint forming method 

and slab thickness on the IRI of JPCP LTPP sections (all climatic 

regions included) ............................................ 117 

91 Plots showing the influence of transverse joint forming method 

and slab thickness on the IRI of JRCP LTPP sections (all climatic 

regions included) ............................................ 117 

92 Plots showing the influence of steel placement method and slab 

thickness on the IRI of JRCP LTPP sections (all climatic regions 

included) ..................... ................... . 118 

93 Plots showing the influence of steel placement method and slab 

thickness on the IRI of CRCP LTPP sections (all climatic regions 

included) ................................................... 118 

94 Plots showing the influence of dowel placement method on the IRI 

of JPCP LTPP sections (all climatic regions included) .............. 120 

95 Plots showing the influence of dowel placement method on the IRI 

of JRCP LTPP sections (all climatic regions included) .............. 120 

96 A plot of CANJPC versus CANJPC for each observation of the data . . 122 

97 A plot of CANJRCl versus CANJRC2 for each observation of the 

data. ....................................................... 129 

98 A plot of CANCRCl versus CANCRC2 for each observation of the 

data ........................................................ 135 
LIST OF TABLES 

Table w 

1 Proposed variables to be investigated for each prediction model ....... 4 

2 Procedure of pavement performance evaluation ................... 10 

3 Key design features. site conditions. and construction practices 

available in LTPP database for transverse joint faulting ............. 20 

4 Level and 1 and 2 design features. site conditions. and construction 

practices used in the evaluation of transverse joint faulting data ...... 25 

5 Class means for canonical variables CANFl and CANF2 ............ 49 

6 Summary of analysis of variance results for faulting ................ 52 

7 Summary of analysis of variance results for variables that potentially 

influence faulting ............................................. 53 

8 Key design features. site conditions. and construction practices 

available in LTPP database for transverse cracking ................. 60 

9 Level 1 and Level 2 data elements used in the evaluation of 

transverse cracking ............................................ 64 

10 Class means for canonical variable CANT1 ........................ 77 

11 Summary of analysis of variance results for JPCP transverse cracking . 78 

12 Summary of analysis of variance results for variables that 

potentially influence transverse cracking .......................... 79 

13 Key design features. site conditions. and construction practices 

available in LTPP database for roughness ......................... 86 

14 Level 1 and 2 data elements used in the evaluation of roughness 

(IRI) data .................................................... 88 

15 Class means for canonical variable CANJPC ...................... 123 

16 Summary of analysis of variance results for roughness ............. 123 

17 Class means for canonical variable CANJRC1 and CANJRC2 ........ 130 

18 Summary of analysis of variance results for JRCP roughness ........ 131 

19 Class means for canonical variable CANCRCl and CANCRC2 ...... 136 

20 Summary of analysis of variance results for roughness ............. 137 

21 Design variables that influence pavement performance ............. 140 
xii 
AC 

ANOVA 

Cd 

CRCP 

Dowdia 

D32 

EPCC 

ESAL 

m 

Ftcyc 

FWD 

GPS 

~PCC 

IRI 

JPCP 

JRCP 

JTSP 

k 

LTPP 

PCC 

TEMP 

Wtdys 

LIST OF ACRONYMS AND ABBREVIATIONS 

asphalt concrete 

analysis of variance 

drainage coefficient 

continuousIy reinforced concrete pavement 

dowel diameter 

average annual number of days with temperature 

below 32 OC 

elastic modulus of PCC slab 

equivalent single axle load 

freezing index 

average annual number of air freeze-thaw cycles 

falling weight deflectometer 

general pavement studies 

PCC slab thickness 

international roughness index 

jointed plain concrete pavement 

jointed reinforced concrete pavement 

joint spacing or PCC slab length 

modulus of subgrade reaction 

Long-Term Pavement Performance 

portland cement concrete 

temperature 

average annual number of wet days 

xiii 

1. INTRODUCTION 

Background 

A major goal of the Long-Term Pavement Performance (LTPP) study is the 

development of recommendations for improving the design and construction of new 

and reconstructed pavements. This is emphasized in two of the six objectives that were 

established for the LTPP study in 1985 by the Pavement Advisory Committee of the 

Strategic Highway Research Program (SHRP). They are as follows: 

Determine the effects of loading, environment, material properties, construction, 

and maintenance on pavement distress and performance. 

* Determine the effects of design features on pavement performance. 

An attempt was made to develop such recommendations in the early analysis of the 

L,TPP data that were available in 1992.") Due to the limited amount of data that were 

available, a complete analysis to develop useful practical recommendations for 

pavements was not possible; however, the research provided several useful insights 

into the database and provided guidelines on future data analysis and research efforts. 

Since the 1992 study, there have been major improvements to the LTPP database. 

With these improvements have come some new opportunities for the evaluation and 

analysis of pavement data to meet some of the specific objectives of the LTPP study. 

More importantly, enough data are now available in the LTPP database to permit 

significant analyses to meet some specific regional and local needs. Such targeted 

research is perhaps the most appropriate way to harvest the best products out of the 

LTPP database. 

Project Scope 

This study was designed to investigate the evaluation of portland cement concrete 

(PCC) pavements in the LTPP database to meet some of the specific needs of the States 

that have been sponsoring and participating in the LTPP program. It involves an 

evaluation and analysis of the PCC pavement data in the LTPP database to determine 

the design features, climatic variables, and construction practices that influence 

performance of rigid pavements. The two specific objectives of the study include: 

a Examining and analyzing the PCC pavement LTPP data to determine design, 

climate, and construction variables that influence performance. 

m Developing specific recommendations for improving the design and 

construction of PCC pavements. 
The project scope was refined to exclude specific recommendations for the 

maintenance and rehabilitation of PCC pavements because of the lack of available data. 

Emphasis was placed on the development of national recommendations (as opposed to 

regional ones) because of constraints in the availability of data. 

Scope of Report 

This report is the second in a series of three volumes on this study. The other two 

reports are as follows: 

Volume I - Summary of Design Features and Construction Practices That 

Influence Performance of Pavements 

Volume 111 - Improved PCC Performance Models 

Volume I1 provides results of extensive data analysis and research work that was 

conducted in this study. Volume I1 addresses the preliminary work done in evaluating 

the influence of the site, specific design features, and construction practices on the longterm performance of concrete pavements using the LTPP database, and volume 111 

provides information on improved distress and roughness prediction models that were 

developed using the LTPP database. The results obtained from volumes I1 and I11 were 

used to develop a summary of design features and construction practices that influence 

performance of pavements, presented in volume I. 

After this introduction, the report provides a discussion of the approach used for 

performance evaluation in this study in chapter 2. Chapters 3'4, and 5 present an 

evaluation of the effects of site conditions, design features, and construction practices 

on PCC pavement performance with the use of transverse joint faulting, transverse 

cracking, and roughness, respectively, as a measure of distress and, hence, 

performance, and chapter 6 concludes the report. Transverse joint spalling and corner 

breaks were not considered for this kind of analysis (empirical statistical analysis) 

because there were not enough pavement sections with these distresses. A 

comprehensive overview of the LTPP PCC data used in this study is presented in 

appendix A of this report, and a discussion of past research findings on factors that 

influence pavement performance is presented in appendix B. 
2. METHOD OF PERFORMANCE EVALUATION 

Introduction 

Pavement performance is determined based on criteria related to the rate of 

occurrence of distress, the severity of the distress, and the detrimental effect of the 

given distress on the pavement's ability to serve its function. Several attempts have 

been made in the past to develop procedures and criteria for evaluating pavement 

performance and, in the process, determine the effects of design features, site 

conditions, and construction practices on perf~rmance.('*~*~) Most of these studies 

involved the use of simple univariate and bivariate plots of the data to establish trends 

in the severity of a given distress as a function of design features, site conditions, and 

construction variables. The development of performance prediction models and a 

comprehensive sensitivity analysis is also an effective procedure for identifying key 

factors affecting performance. 

Results from some of the past pavement performance evaluation studies were 

reviewed as part of this study to determine the following: 

Variables that have significant effect on distresses and, hence, performance. 

Interactions between quantitative variables that have a significant effect on 

distresses. 

* Mechanistic clusters that have an effect on the occurrence and rate of progression 

of distress. 

Procedures used to determine which pavements are performing better than 

expected, as expected, and worse than expected. 

Table 1 shows the site, design, and construction (explanatory) pavement variables that 

have been found in previous studies to have a significant effect on PCC distresses. The 

effect of these variables on pavement performance were investigated further by 

conducting a preliminary analysis of bivariate plots of distress versus traffic and 

consequently a more detailed and comprehensive canonical discriminant analysis and 

analysis of variance. The plots showed clearly the effects of the variables on the 

development and progression of distress. The procedure for pavement performance 

evaluation for this study is in five parts and can be summarized as follows: 

1. A literature review of past pavement evaluation studies that is presented in 

appendix B of this report. 

2. Preliminary evaluation of data using graphical techniques such as univariate 

(presented in appendix A) and bivariate plots. 

3. Classification of pavement sections within the database based on performance 

criteria. 
Table 1. Proposed variables to be investigated for each prediction model. 

Pavement age J J J 

Slab thickness J J J -- 

Joint spacing J J J 

Drainage J 

Base tv~e J J J 

Cumulative ESALJs* J J J 

Effective joint opening J 

Corner deflection J 

11 Sealant damage 1 J 1 II Load transfer type J ! ! 

11 / Fr z-h 

* ESAL = Equivalent single axle load. 
4. Developing a procedure for determining pavement performance on the basis of 

known quantitative design, climate, and construction variables (canonical 

discriminant analysis). 

5. An analysis of variance (ANOVA) to determine variables that have a significant 

influei~ce and effect on distress and, therefore, performance. 

This chapter reviews the approach used in this report for evaluating pavement sections 

according to performance and determining the significant design features, site 

conditions, and construction practices that influence pavement performance. 

Preliminary Statistical Analysis (Univariate and Bivariate Analysis) 

For both performance model development and performai~ce evaluation, an 

extensive univariate and bivariate analysis of the database was necessary. This served 

two purposes: to determine the condition of the database used in analysis and to 

confirm trends determined from past pavement performance evaluation studies, model 

development efforts, and mechanistic analysis. 

Univaria te Analvsis 

Univariate analysis is the determination of the basic statistics of the LTPP data 

elements such as the total number of sections, sections with missing data, and the 

mean, mode, median, and standard error estimate of the mean of the data element. 

Other tests include a test for the normality of the data, skewness, and kurtosis. Plots, in 

the form of a histogram or bar chart, can be used to show visually the distributioi~ of a 

given data element or variable. The ideal data element distribution for statistical 

analysis is a i~ormal distribution. However, the distribution of most of the data 

elements used in the analysis is skewed. Univariate analysis and plot are efficient 

techniques for identifying such data elements and correcting them where possible. 

Also, statistical techniques that are insensitive to skewness may be used for analysis. 

Figures 1 and 2 show examples of normally distributed data and skewed data. The 

results of the comprehensive univariate analysis performed as part of this study are 

presented in appendix A of this report. 

Bivariate Analvsis 

Bivariate statistics measure the degree of dependence between two variables 

(dependent and independent). They also show the trends and changes in the value of 

the dependent variable as the independent variable is varied. For this study, the 

dependent variable is a distress variable and the independent variable is a design, 

climate, construction, or any other kind of variable or cluster of variables that could 

influence pavement performance. 
Data element or variable 

Figure 1. Normally distributed data. 

Data element or variable 

Figure 2. Data that are skewed or deviating from normal. 
Results of the bivariate analysis were presented in the form of plots of the 

independent variables against distress and simple statistical regression curves (linear) 

showing trends and correlations. The plots showed visually the trends between the 

independent variable and distress. They were used to determine if the trends were 

reasonable and as expected from mechanistic analysis, engineering judgment, and past 

empirical analysis. The bivariate plots were also used to identify potential outliers. 

Plots of this kind can be confounded by the effects of other independent variables not 

considered. This could lead to obtaining contradictory and misleading results. 

Observations from bivariate plots are therefore preliminary in nature and therefore not 

concl-usive. For this study, more sophisticated statistical tools such as canonical 

discriminant analysis and analysis of variance were used to develop recommendations 

for pavement design and construction. 

Canonical Discriminant Analysis 

Canonical discriminant analysis begins with the desire to statistically distinguish 

between two or more groups of observations. These groups are defined by the 

particular research Some examples of groups of observations would be as 

follows: 

Group 1-Pavements with faulting less than 10 mm. 

Group 2-Pavements with faulting greater than or equal to 10 mm. 

With measurements on several quantitative variables, canonical discriminant analysis 

derives a linear combination of variables that has the highest possible multiple 

correlation with the groups. The coefficients of the linear combination are the canonical 

coefficients or canonical weights. The variable defined by the linear combination is the 

canonical variable. Several other canonical variables can be obtained by finding the 

linear combination uncorrelated with the first canonical variable that has the highest 

possible multiple correlation with the groups. 

To distinguish between the groups the researcher selects a collection of 

discriminating variables that measure the characteristics on which the groups are 

expected to differ. For instance, in the faulting example, data on the pavement design, 

site conditions, and construction techniques such as base or subgrade type, traffic, load 

transfer system, joint construction method, and precipitation that potentially could 

influence faulting may be useful for canonical discriminant analysis. The mathematical 

objective of canonical discriminant analysis is to weight and linearly combine the 

discriminating variables in some fashion so that the groups are forced to be statistically 

distinct as possible. In order words we want to be able to discriminate between the 

groups in the sense that we tell them apart. Normally, no single variable will perfectly 

discriminate between the two faulting levels given in the example; however, by taking 
several variables and mathematically combining them it is possible to obtain a single 

discriminant variable that is a linear combination of the pavement properties that 

clearly demarcates between the two levels of faulting. For pavement performance 

evaluation, canonical discriminant analysis can be an efficient tool for evaluating the 

performance of newly designed pavements given the pavement site, design, and 

construction features. 

Canonical discriminant analysis in this study involved the classification of existing 

pavement sections in the LTPP database according to performance and then using the 

data in the development of canonical functions based on the pavement's design, site, 

and construction properties. The methods for classifying the pavement and developing 

the canonical functions are discussed in the following sections. 

Performance Classification of Pavements 

To perform a more detailed statistical analysis on the data to determine variables 

affecting performance, the different pavement sections within the LTPP database had to 

be classified using a variable that defined them based on performance. The 

observations were classified as expected, below, and above expected performance. 

Several studies in the past have used expert opinion to classify pavement 

performance. One such study used a graphical approach, which involved defining and 

plotting the boundaries between three levels of performance (good, normal, poor) for 

each distress type for a given pavement's age since construction. The criteria for 

differentiating between good, normal, and poor performance and defining the 

boundaries for the different classes of performance were recommended by pavement 

experts over a 20-year period. The objective was to identify design features that 

contribute to good and poor performance.'4' For this study, pavement performance was 

classified by considering the relevant design, climatic, and construction variables that 

influence the occurrence and severity of the distress. The classification procedure is best 

explained by the example below. 

Consider a pavement designed and constructed using appropriate design 

techniques and construction procedures to withstand 20 million ESAL applications over 

a 20-year period for the given site and climatic conditions. We expect the pavement to 

have a minimum useful life of 20 years; that is, we expect the pavement to perform 

adequately for at least 20 years, without any major occurrence of distress. We also 

expect damage to the pavement resulting in distress to accumulate both from the 

repeated axle loads applied to the pavement and repeated climate-related stresses, 

Within the 20-year useful design life of the pavement, we expect the severity of both 

damage and distress to approach but remain below critical levels. Classifying or 

quantifying performance for such a pavement is based on whether the pavement 
performs as expected. The most practical method for determining this is to compare the 

actual distress measured for a pavement with a given age and traffic load applications 

and expected distress. The expected distress is estimated from distress prediction 

models and expert opinion. Pavements can thus be categorized based on the 

comparison of actual and expected distress as follows: 

Pavements with measured distress equal to predicted distress or within a 90 

percent confidence interval are classified as "expected." 

Pavements with measured distress less than the lower limit of the 90 percent 

confidence interval of predicted distress are classified as "above expectation." 

Pavements with measured distress greater than the upper limit of the 90 percent 

confidence interval of predicted distress are classified as "below expectation." 

Table 2 presents this concept in another format. The most appropriate method for 

determining the expected distress values of pavement sections is the use of existing 

pavement performance models. Performance models are multi-dimensional, taking 

several design, climate, and construction variables into consideration for predicting the 

expected level of distress. Because of potential errors that are certain with every model, 

a confidence interval should be used to define a range for the expected level of distress. 

This will limit the potential for misclassification. It must be noted that the expected 

distress level of a pavement with similar features may vary according to age and traffic 

loading. A detailed procedure for pavement performance classification is presented in 

the next section. 

Pavement Performance Classification Procedure 

Pavement performance classification was determined using models developed as 

part of this study. The information required for classification is as follows: 

1. Determine the actual distress severity from the database. 

2. Use pavement performance models or expert opinion to determine the expected 

distress severity level for a pavement section with the given design, climate, and 

construction features. 

3. Determine the standard estimate of error (SEE) for the model being used in the 

analysis. 

4. Using the standard error estimate and expected distress value, determine the 90 

percent confidence interval (C.I.) for the expected distress (90% C.I. = 1.6455EE). 

Using information from the LTPP database, performance models, and engineering 

judgment, the pavement sections in the ETPP database used for this study were 

classified as follows: 
Table 2. Procedure of pavement performance evaluation. 

@ A pavement section with a measured distress severity greater than the expected 

confidence interval band (measured distress > expected distress + 1.645"SEE) is 

classified as performing below expectation. 

A pavement section with a measured distress severity within the confidence 

interval band is classified as expected (expected distress - 1.6455EE < measured 

distress < expected distress + 1.6455EE). 

A pavement section with a measured distress severity below the confidence level 

band (measured distress < expected distress) - 1.6455EE) is said to be 

performing better than expected or above expected. 

Age, 

Y* 

20 

20 

20 

The procedure outlined for pavement performance evaluation is shown graphically as 

figure 3. 

Canonical Functions 

Traffic, 

ESALfs 

10 million 

10 million 

10 million 

Canonical discriminant analysis is a dimension reduction technique related to 

principal component analysis and canonical ~orrelation.(~~~~~ Given a classification 

variable such as pavement performance and the quantitative variables such as the 

design, climate, and construction features of the pavement, canonical discriminant 

analysis derives canonical variables related to the classification variable for canonical 

functions (linear combination of the quantitative variables). The canonical functions 

summarize between class variation and discriminate between pavements based on the 

classification variable (in this case, pavement performance). The procedure for 

implementing this technique is summarized as follows and shown in figure 4.(5*6) 

0 Identify and define potential quantitative design, climatic, and construction 

variables. 

Identify missing data and potential data problems, such as normality, and 

implement remedies as required. 

Assemble the database and classify the pavement sections within the database 

into the expected, below expected, and above expected performing pavement 

sections (classification variables). 

Expected 

Faulting, mm 

2.5 

2.5 

2.5 

Actual Faulting, 

mm 

0.25 

2.5 

6 

Performance 

Classification 

Above expectation 

Expected 

Below expectation 

* Perform canonical discriminant analysis on the data to obtain canonical 

functions and variables. 

Check canonical functions and variables for significance, misclassification, and 

collinearity using diagnostic and other statistics. 

Determine the range of values of canonical variables for the different 

performance classes. 

The use of the canonical discriminant analysis technique to classify pavement 

performance is illustrated with the following example plot of two canonical functions, 

CANl and CAN2, shown as figure 5. The plot shows pavement sections classified as A, 

B, C, and D. Pavements classified as A are located in quadrant 1, B is in quadrant 2, C is 

in quadrant 3, and D is in quadrant 4 of the plot, 

Therefore, for a pavement section with given design features, site conditions, and 

construction practices, the quadrant in which the plot of CANl and CAN2 lies 

determines the performance class to which that pavement belongs. The performance 

class can be varied by varying the values of the input variables for the canonical 

functions and thus could be useful in determining the performance class of newly 

designed pavements. 

Analysis of Variance 

ANOVA models are versatile statistical tools for studying the relation between a 

dependent variable and one or more independent variables.@) They do not require 

making assumptions about the nature of the statistical relation nor do they require that 

the independent variables be quantitative. ANOVA models are used for applications 

where the effects of one or more independent variable on the dependent variable are of 

interest. Independent variables in the models for ANOVA are mostly called factors or 

treatments. This section of the report presents the basic ANOVA procedures used in 

this study for determining the effects of several factors or treatments (independent 

variables such as slab thickness and annual precipitation) on the dependent variable, 

distress. 

ANOVA Procedure 

The procedure for the ANOVA analysis is summarized as follows: 

1. Transform continuous variables within the LTPP database into classification 

variables. 

2. Develop ANOVA models for predicting distress. 

3. Perform test of hypothesis using the models developed. 
which variables have too little data to keep h analysis) 

Identlfy and define potential variables 

Dependent variable 

Independent or explamlory variables 

+ 

Explore dataset and clean data 

Select didress/llU model for predicting distress or performance by Statistics (mean, rnin, msg median) 

conductingliterature =view of previous models Identify types of relationships between variables 

Identify meous and potential problem data I 

=, Assemble database of LTPP sections 

(determine specific subset of data to use) - 

Classify pavement performance as above expectation, as expected, or below expectation using selected distresdroughness 

models . 

Identify missing data items for variables (decide 

Canonical discrimination functions development 

Select potential variables and tmnsformations for initial evaluation 

Conduct determinant analysis with all variables (observe significant levels, collinearity 

Identify potential outliers and redundant variables 

Select tentative canonical functions 

Sensitivity analysis 

Evaluate reasonableness of direction of variables in canonical 

functions 

Evaluate remableness of sensitivity of each canonical variable 

Judge adequacy of tentative canonical function 

Figure 4. Flow chart showing canonical discriminant analysis procedure. 

The procedure outlined is simple and suits the purposes of most simple analysis of 

variance. The different elements of the procedure are explained in the following 

sections. 

Data T~unsfmmation 

Most of the data elements in the LTPP database used as treatments or factors in 

ANOVA are continuous. Using them as presented in the database will result in the 

consideration of the effect of several factor levels on the given distress (e.g., PCC slab 

thickness = 200,210,220,230, ..., 260 mm). To avoid such a situation, and also to 

decrease the number of levels of a given factor such as slab thickness, the data elements 

were transformed into classification or indicator variables. An example is shown below: 

Class A or 0, PCC slab thickness < 220 mm, 

Class B or 1, PCC slab thickness 2 220 mm. 

The qualitative variable defining the ranges of pavement PCC slab thickness was then 

used in the ANOVA model to determine the effect of slab thickness on pavement 

performance. 

ANOVA Models 

ANOVA models are a basic type I statistical model.(8) They are concerned, like 

regression models, with the statistical relation between one or more independent 

variables and a dependent variable. Like regression models, ANOVA models are 

appropriate for both observational data and data based on formal experiments. Further, 

like the usual regression models, the dependent variable for ANOVA models is a 

quantitative variable. However, they differ from ordinary regression models in two key 

respects. First, the independent variables in the ANOVA model can be qualitative 

(geographic location, wet, dry, freeze, nofreeze). Second, if the independent variables 

are quantitative, no assumption is made in the ANOVA models about the nature of the 

statistical relation between them.@) 

The difference between ANOVA models and regression models is illustrated by the 

plots shown in figure 6. Shown in figure 6a is the regression model for a pricing study 

involving three different price levels, X = $50, $60, and $70. For each level of the 

independent variable, there is a probability distribution of sales volume. The means of 

these probability distributions fall on the regression curve, which describes the 

statistical relation between price level and mean sales volume. 

The ANOVA model for the same study is shown in figure 6b. The three price levels 

are shown as separate distributions, each leading to a probability 
(a) Regression Model 

Sales volume 

(b) Analysis of Variance Model 

$50 $60 

Price Price 

$70 

Price 

p2 

Sales volume 

Figure 6. Relation between regression and analysis of variance models.'@ 
distribution of sales volumes. The quantitative differences in the three price levels and 

their statistical relation to expected sales volume are not considered. 

Hypothesis Testing 

The goal of the ANOVA is to compare means of the response variable (distress) for 

various combinations of the classification variables (design, site, and construction 

properties). The effect and significance of the variables on performance can be 

confirmed or verified by comparing the level of significance of the variables to a 

predetermined level of significance, called p-value. ANOVA determines if there is a 

statistical difference in the mean values of the distress for the different classes of the 

independent variables in the model,(8) The mean level of the distress for the different 

classes gives an indication of whether the independent variable has a positive or 

negative effect on the distress. The following example illustrates the ANOVA 

technique. 

Pavement PCC thickness: class A (less than 220 mm) 

class B (greater than 220 mm) 

Distress level : class A = y, 

class B = pB 

The significance of the effect of PCC thickness on the given distress is determined by 

%he following test of hypothesis: 

Null hypothesis, HO: PA = PB 

Alternative hypothesis, HA: pA + pB 

Based on a significance level (p-value) of 5 percent (0.05), of less than 0.05 rejects the 

null hypothesis, whereas a result greater than 0.05 confirms the null hypothesis. A 

comparison of the magnitude of the means determines the nature of the effect of the 

pavement property (in this case, PCC thickness). 

Summary of Methods of Performance Evaluation 

Bivariate plots, canonical discrimiiiant analysis, and analysis of variance are 

statistical tools useful in determining, preliminarily, trends between dependent and 

independent variables, and the effect of various design, site, and construction features 

on pavement performance. These statistical tools will be applied throughout this report 

to determine the effects of site conditions, design features, and construction practices on 

distress formation and progression in PCC pavements and, thus, PCC pavement 

performance. 

3. EVALUATION OF SITE CONDITIONS, DESIGN FEATURES, AND 

CONSTRUCTION PRACTICES THAT INFLUENCE JPCP FAULTING 

Introduction 

Many of the primary design features, site conditions, and construction practices that 

influence PCC pavement performance are identified in appendix B. They include the 

site conditions of traffic, climate, and subgrade support, as well as the specific design 

features that are incorporated into the pavements to improve performance, such as PCC 

slab thickness, the presence of dowels, and drainage facilities. 

A prioritized list of the design features, site conditions, and construction practices 

(relevant to each distress type) proposed for investigation with LTPP data was 

presented in table 1 of chapter 2 of this report. Some of these data elements identified 

during the literature review are not available in the LTPP database; however, those 

available will be investigated to determine their effect on performance, The list of key 

site conditions, design features, and construction practices available in the LTPP 

database, and identified as having the potential to influence the development and 

progression of faulting, is presented in table 3. This list was not meant to be exhaustive, 

and any other variables that were found to significantly influence faulting at the 

preliminary stage of faulting were investigated. 

This chapter presents the results of a preliminary bivariate analysis and canonical 

discriminant analysis used to identify the data elements that influence PCC pavement 

faulting. A comprehensive univariate analysis, which was the first step in analyzing the 

data, is presented in appendix A of this report For the bivariate analysis, the effects of 

individual design features and site conditions were investigated separately. The data 

were further divided, when possible, to observe the effect of climate. The design of 

experiments to be analyzed was based on engineering judgment in order to keep the 

individual evaluation data sets a minimum size. This reduced the possibility of 

insufficient data for analysis. Each experiment was specific to the distress data type 

being investigated and is described in detail in later sections of this chapter. 

Also, an analysis of variance was performed to confirm where possible the trend 

observed in the preliminary stages of the analysis. It must be noted therefore that, with 

the exception of the results from the analysis of variance, results and discussions 

presented from the bivariate analysis and canonical discriminant analysis can be 

misleading because of the influence of other variables not considered in this kind of 

preliminary analysis. 
Table 3. Key design features, site conditions, and construction practices 

available in LTPP database for transverse joint faulting. 

* Data elements were calculated from other original data elements in the LTPP 

database. 
Preliminary Evaluation of Transverse Joint Faulting Data 

As indicated in appendix B, the development of transverse joint faulting in both 

jointed plain concrete pavements (JPCP) and jointed reinforced concrete pavements 

(JRCP) pavements is greatly influenced by the presence of dowels and the amount of 

heavy traffic load applications. Based on the assumption that the presence of dowels 

has more of an influence on the development of faulting than pavement type, the JPCP 

and JRCP LTPP data were analyzed together. 

Level 1 Investigations 

The data were divided into subsets of doweled and undoweled sections, defined as 

the Level 1 analysis. The analysis at this stage was to determine the influence of dowels 

on faulting of PCC jointed pavements. The results are presented in the next section of 

this chapter. 

Influence of Presence of Dowels 

Figure 7 shows a plot of average edge faulting versus cumulative ESAL's for all 

doweled and undoweled JPCP and JRCP sections in the LTPP database. Linear trends 

were fit through the data in order to more clearly observe the trends. The available 

LTPP data indicate that, on average, undoweled JPCP and JRCP sections experience 

more faulting than doweled sections. A visual inspection of the data appears to 

indicate that the presence of dowels is much more important in minimizing faulting 

than dowel bar diameter. 

The effect of the presence of dowels was further investigated by determining the 

effect of pavement location and dowels on performance. This involved investigating the 

influence of dowels in the various climatic regions in the United States. Figure 8 shows 

plots of faulting versus traffic in ESAL's for the climatic regions. Only the wets-freeze 

and wet-nofreeze climatic regions contained enough sections to compare doweled and 

undoweled sections. For both of these regions, the undoweled sections clearly 

exhibited more faulting than the doweled sections. 

No trends were distinguishable for the dry-freeze and dry-nofreeze regions. An 

investigation of undoweled sections by climatic region showed about the same amount 

of faulting for those sections in the dry-freeze, wet-freeze, and wet-nofreeze regions. 

The undoweled sections in the dry-nofreeze region, however, exhibited much smaller 

faulting values than the undoweled sections in the other three regions. Doweled 

sections in the wet-freeze and wet-nofreeze regions appeared to develop about the 

same amount of faulting. 

Dry-Fre~e Climatic Region Wet-Frxze Uima tic Region 

Dry-N& Cliitic Region Wet-Nofmze Climatic Region 

ESAL's (thousands) 

Figure 8. Plots showing the influence of the presence of dowels and climatic region on 

the faulting of JPCP and JRCP sections. 
Level 2 Investigations 

Level 2 investigations of the faulting data consisted of further dividing the Level 1 

subsets into smaller data sets based on many of the possible influential site and design 

features identified in table 3. A summary of the Level I and Level 2 variables chosen 

for the faulting investigation is presented in table 4. The trends discussed and 

presented are those of the raw LTPP data. Each Level 2 variable is investigated by 

plotting the observed joint faulting data (expressed as the average transverse joint 

faulting for the section in millimeters) versus the cumulative ESAL's as determined 

from regression equations of the LTPP annual traffic data. The preliminary analysis 

consisted of creating plots showing the influence of each Level 2 variable at each Level 

1 definition (doweled or undoweled) for all of the data observations, regardless of 

climatic region. Lines were fit through the data to more easily identify the trends. To 

observe any differences in the influence of the Level 2 variables between climatic 

regions, these plots were then recreated by using only the data in each climatic region 

(when data were available). 

The conclusions and inferences drawn at the bivariate analysis stage are 

preliminary. There is a great possibility at this stage of the analysis for some of the 

results to be misleading because of confounding effects and interactions between the 

data elements. The results of these analyses are summarized and presented below. 

Influence of Site Conditions 

The Level 2 investigation involved analyzing the effect of several site-related 

variables on faulting of doweled and undoweled jointed PCC pavements, namely, 

freezing index, freeze-thaw cycles, average annual precipitation, average annual 

temperature range, average annual number of wet days, subgrade type, and the 

modulus of subgrade reaction. The variables were divided into the following categories 

for investigation: 

Freezing index: < 270 and 2 270 "C days. 

Annual freeze-thaw cycles: < 70 and 2 70. 

0 Average annual precipitation: c 1 and 2 1 m/yr. 

Average annual temperature range: < 11 and 2 11 "C. 

e Average annual number of wet days: < 125 and 2 125 days. 

Subgrade type: fine or coarse-grained. 

Modulus of subgrade reaction: < 40.7 and 2 40.7 kPa/mm. 

Efect of Freezing Index 

For pavements with dowels, freezing index appeared to have no significant effect 
Table 4. Level 1 and 2 design features, site conditions, and construction practices used 

in the evaluation of transverse joint faulting data. 
Sections WITH bmls Sections~Dowels 

0 >rnde&Cdays 

Linear (e 277 dq C days) 

Figure 9. Plots of JPCP and JRCP transverse joint faulting versus traffic for 

different freezing index categories (all climatic regions). 

on the level of faulting. However, undoweled pavements subjected to a freezing index 

2 270 "C days exhibited a larger amount of faulting than those sections subjected to a 

freezing index < 270 "C days. The plots of faulting versus traffic showing the influence 

of freezing index are presented figure 9. 

Efect of Average Annual Freeze-Thaw Cycles 

The level of faulting in doweled sections again did not appear to be influenced by 

the level of change in average annual freeze-thaw cycles. However, undoweled 

sections subjected to freeze-thaw cycles 2 70 per year exhibited a significantly larger 

amount of faulting than those sections subjected to < 70 per year. Figure 10 presents 

plots of faulting versus traffic showing the influence of freeze-thaw cycles. 

Efect of Average Annual Precipitation 

For those sections with dowels, no significant change in faulting was observed for 

the two different annual precipitation categories. However, the same did not hold true 

for the undoweled sections. The undoweled sections subjected to greater than 1 m of 

average annual precipitation exhibited much more faulting (on average) than those 

sections subjected to less than 1 m. Figure 11 presents plots of faulting versus traffic 

showing the influence of average annual precipitation. 
Sedions WITH Dowels Sections WITHOUT Dowels 

- - - Lit-ear (r 70) 

- 

ESAL's (thousands) ESAL's (thousands) 

Figure 10. Plots of JPCP and JRCP transverse joint faulting versus traffic for different 

levels of freeze-thaw cycles (all climatic regions included). 

Sections WITH Dowels Sections WITHOUT Dowels 

ESAL's (thousands) 

L'mear (< 1.0 rn) - - - Linear (zl 1 .O m) 

" 

ESAL's (thousands) 

Figure 11. Plots of JPCP and JRCP transverse joint faulting versus traffic for different 

levels of average annual precipitation (all climatic regions included). 
Efiect of Average Annual Temperature Range 

Changes in the average annual temperature range appeared to have little effect on 

the faulting of doweled sections. However, undoweled LTPP sections subjected to an 

annual temperature range < 11 "C showed more faulting (on average) than those 

undoweled sections subjected to a range of 2 11 "C. Figure 12 presents plots of faulting 

versus traffic showing this influence of average annual temperature range. 

Effect of Average Annual Number of Wet Days 

An investigation of the influence of the average annual number of wet days on 

transverse joint faulting showed that it significantly influenced faulting. Doweled and 

undoweled JPCP and JRCP subjected to 2 125 wet days per year consistently developed 

more faulting (on average) than those subjected to c 125 wet days per year. Figure 13 

presents plots of faulting versus traffic showing this influence of average annual 

number of wet days. 

Effect of Subgrade Type 

A Level 2 analysis of subgrade type involved further separating the JPCP and JRCP 

doweled and undoweled sections by subgrade type (coarse-grained and fine-grained). 

The results showed that doweled sections with fine-grained subgrades had more 

faulting than those with coarse-grained subgrades. The undoweled sections appeared 

to have the opposite trend, with the sections with coarse-grained subgrade soils 

experiencing more faulting. This may be due to the fact that close to 90 percent of the 

undoweled JPCP pavements were located in wet or freeze regions, which could 

influence the magnitude of observed faulting. A visual inspection of the data showed 

that the highest individual faulting values were observed for those sections with finegrained soils, Figure 14 contains plots showing the influence of subgrade type category 

on the faulting of JPCP and JRCP doweled and undoweled sections. 

An analysis of the sensitivity of JPCP and JRCP transverse joint faulting data to 

subgrade type and climatic region was limited to the wet-freeze and wet-nofreeze 

climatic regions because of the lack of data in the dry-freeze and dry-nofreeze climatic 

regions. Both doweled and undoweled sections in the wet-freeze climatic region 

showed more faulting for those sections with fine subgrade soils. The regressions fit 

through the data in the wet-nofreeze region showed no sensitivity to subgrade type for 

the doweled sections, whereas for the undoweled sections, pavements with coarsegrained subgrade soils exhibited more faulting than those sections with fine-grained 

subgrade soils. Figure 15 contains plots showing the influence of subgrade type on the 

faulting of JPCP and JRCP doweled and undoweled sections in the wet-freeze and wetnofreeze climatic regions. 
Sections WITH Dowels Sections WITHOUT Dowels 

r- 11 dog. C 

Linear (c 11 dog. C) 

= = - Linear (w 11 dog. C) 

08 

-8 -8 

ESAL's (thousands) 

Linwr (< 11 dog. CJ 

- "..c.' ............................... 

,O 

30,000 40,100 

0 

HSAL1s (thousands) 

Figure 12. Plots of JPCP and JRCP transverse joint faulting versus traffic for different 

levels of average annual temperature range (all climatic regions included). 

Sections WITH Dowels Sections WITHOUT Dowels 

r 125days 

Llnear (< 125 days) 

' ' ' Linear (2.125 days) 

2 08 

ESAL's (thousands) 

Linear (< 125 days) 

' ' ' Linear (t= 125 days) 

-8 

ESAL's (thousands) 

Figure 13. Plots of JPCP and JRCP transverse joint faulting versus traffic for different 

levels of average annual number of wet days (all climatic regions included). 
Sections WITH Dowels 

10 

I Coarse 1 

Linear (Coarse) - - - Linear (Fine) 

2 10 e8 

Sections WITHOW Dowels 

10 

I 6 Coarse 1 I 

Linear (Fine) 

ESAL's (thousands) ESAL's (thousands) 

Figure 14. Plots of JPCP and JRCP transverse joint faulting versus traffic for different 

subgrade type categories (all climatic regions included). 

Effect of Modulus of Subgrade Reaction (Backcalculated Static k-value) 

The influence of two different static k-value levels (k-value c 40.75 kPa/mm and 2 

40.75 kPa/mm) on JPCP and JRCP transverse joint faulting data was investigated for 

both doweled and undoweled LTPP sections. The results showed that doweled 

sections with k-values in the c 40.75 kPa/rnm category had slightly more faulting than 

those with larger k-values. The undoweled sections appeared to have the opposite 

trend, with the sections with k-values 2 40.75 kPa/mm experiencing more faulting. 

This, however, does not appear to be significant and may require further statistical 

analysis. Figure 16 contains plots showing the influence of k-value category on the 

faulting of JPCP and JRCP sections (all climatic regions are included). An analysis of 

the sensitivity of JPCP and JRCP transverse joint faulting data to subgrade k-value and 

climatic region was again limited by the lack of data in the dry-freeze and dry-nofreeze 

regions. However, both doweled and undoweled sections in the wet-freeze climatic 

region showed more faulting for those sections with k-values less than 40.75 kPa/mm. 

Figure 17 contains plots showing the influence of subgrade k-value category and 

climatic region on the faulting of doweled and undoweled sections in the wet-freeze 

and wet-nofreeze climatic regions. 
Sections WITH Dowels 

(wet nofreeze) 

10 -, 

- - Unes (Come) 

ESAL's (thousands) 

Sections WITH Dowels 

(wet freeze) 

ESAL's (thousands) 

Sections WITHOUT Dowels 

(wet nofreeze) 

ESAL's (thousands) 

Sections WITHOUT Dowels 

(wet freeze) 

I 

ESALis (thousands) 

Figure 15. Plots showing the influence of subgrade type, dowels, and climate on 

faulting of JPCP and JRCP. 
Sections WITH Dowels Sections WITHOUT Domls 

FSAL's (thousands) ESAL's (thousands) 

Figure 16. Plots showing the influence of k-value on the faulting of JPCP and JRCP. 

The Level 2 investigation involved analyzing the effect of several design features on 

faulting of doweled and nondoweled jointed PCC pavements, namely, slab thickness, 

joint spacing, presence of edge drains, base type, PCC elastic modulus, presence of 

widened lanes, outside shoulder type, and joint sealant type. The variables were 

divided into the following categories for investigation: 

Slab thickness. 

Joint spacing. 

Presence of edge drains. 

Base type. 

PCC elas tic modulus. 

Presence of widened lanes. 

Outside shoulder type. 

Joint sealant type. 

Effect of Slab Thickness 

A Level 2 analysis of the effects of slab thickness on transverse joint faulting 

involved separating the JPCP and JRCP doweled and undoweled sections into two 

different slab thickness categories (thickness < 250 mm and 2 250 mm). 
SecUons WITH Dowls (wet-nofreere) Sections WITHOUT hmls (wet-nofreeze) 

ESAL's (thousands) ESAL's (thousands) 

Sections WITH Dowels (wet-freeze) Sections WITHOUT Dowels (wet-freeze) 

0 >540.7 kPm 

Linesr (< 40.7 kPa'mfn) 

Linear (h; 40 7 k~m) 

" 

ESAL's (thousands) ESAL's (thousands) 

Figure 17. Plots showing the influence of k-value, dowels, and climate on the faulting 

of JPCP and JRCP. 
The data showed that faulting was not influenced greatly by slab thickness. The 

doweled sections with slab thickness 2 250 mm showed slightly more faulting than the 

thinner sections, while the linear regressions fit through the undoweled sections 

showed no difference in the faulting between the sections in each thickness category. 

Figure 18 contains plots showing the influence of slab thickness category and presence 

of dowels on the faulting of JPCP and JRCP LTPP sections (all climatic regions are 

included). 

An analysis of the sensitivity of doweled and undoweled transverse joint faulting 

data to thickness category and climatic region was limited by the lack of data in some 

regions. There were not enough data to make any statements about trends in the drynofreeze region (doweled and undoweled sections) or the doweled sections in the dryfreeze region. The data that were available showed that thickness level did not have a 

significant influence on the faulting of undoweled pavements in the dry-freeze region 

or doweled and undoweled pavements in the wet-nofreeze region. 

Doweled pavements in the wet-freeze region showed more faulting for thicker 

pavements, and the undoweled pavements in this region showed slightly more faulting 

for the thinner sections. Figure 19 contains plots showing the influence of thickness 

category and climatic region on the faulting of undoweled JPCP and JRCP LTPP 

sections. Figure 20 contains plots showing the influence of thickness category and 

climatic region on the faulting of doweled JPCP and JRCP LTPP sections. 

Average Transverse Joint Spacing 

The influence of two different average transverse joint spacing levels (joint spacing 

- < 4.6 rn and > 4.6 rn) on JPCP transverse joint faulting was investigated for both 

doweled and undoweled LTPP sections. The data showed that faulting was generally 

higher for both doweled and undoweled sections in the category where joint spacing 

was < 4.6 m. Figure 21 contains plots showing the influence of joint spacing category 

on the faulting of doweled and undoweled JPCF LTPP sections (all climatic regions are 

included). 

Available data showed that doweled sections with joint spacing 5 4.6 m generally 

exhibited more faulting than those sections with longer joint spacing in the wet-freeze 

and wet-nofreeze regions. No consistent trends were identified between climatic 

regions for the available undoweled JPCP LTPP sections, These data showed that 

faulting was greater for the JPCP sections with joint spacing 5 4.6 m in the wet-freeze 

region, but the opposite was true in the dry-freeze and wet-nofreeze regions, where the 

sections with longer joint spacing exhibited more faulting. Figures 22 and 23 contain 

plots showing the influence of joint spacing category and climatic region on the faulting 

of doweled and undoweled JPCP LTPP sections, respectively. 
Sections WITH Dowels 

- Linear (r 250 mn7) *- 

Sections WITHOUT Dowels 

ESAL's (thousands) 

-" - 

ESAL1s (thousands) 

Figure 18. Plots showing the influence of thickness and dowels on faulting of JPCP 

and JRCP. 

Sections WITHOUT Dowels Sections WITHOUT Dowels 

(Wet-nofreeze) (Wet-freeze) 

10 10 

-hear (c 250 rnrn) 

ESAL's (thousands) 

-8 

ESAL's (thousands) 

c 250 mm 

0 r-250mm 

Linear (< 250 mm) 

- ' ' ' Linear (>- 250 m) 

1 10,000 20,000 30,000 40,100 

Figure 19. Plots showing the influence of thickness and climate on the faulting of 

undoweled JPCP and JRCP. 
Sections WITH Dowels 

(wet-f reeze) 

Sections WlTH Dowels 

(wt-nofreeze) 

Linear (< 250 mm) 

-3 

ESALts (thousands) 

0 >-2SOmrn 

Linear (c 260 mm) 

0 

0 10,000 20,000 30,000 40; 100 

ESAL's (thousands) 

Figure 20. Plots showing the influence of thickness and climate on the faulting of 

doweled JPCP and JRCP. 

Sections WITH Dowds Sections WITHOUT Wls 

ESAL's (thousands) ESAL's (thousands) 

Figure 21. Plots showing the influence of joint spacing on the faulting of doweled and 

undoweled JPCP. 

36 
Sections WlTH Dowels 

(wet-freeze) 

Sectlons WlTH Dowels 

(wet-nof reeze) 

ESAL's (thousands) 

Linser (<- 4.6 in) 

- - - Linear (a4.B in) 

0 .. . . . . . . . . . . . . .. . .. . .. . . . .. .... . . ... .. . .. . .. . . . .. . .. . - . . . . . . . . . @--; - 0- 

I 

1) 10,000 20,000 30,000 40, 

i 

Figure 22. Plots showing the influence of joint spacing and climate on faulting 

of doweled JPCP. 

XJO 

Effect of Presence of Edge Drains 

The presence of edge drains had no effect on the development of transverse joint 

faulting for doweled or undoweled JPCP and JRCP LTPP sections. However, a visual 

inspection of the data showed that the majority of high faulting data points came from 

sections without edge drains, as expected (see figure 24). An analysis of the sensitivity 

of transverse joint faulting data to the presence of edge drains and climatic region was 

conducted. No analysis could be made about the influence of edge drains in the dry 

regions because they lacked enough data. Edge drains had virtually no effect on the 

doweled sections in the wet-freeze or wet-nofreeze regions. However, a visual 

inspection of the data showed that the undoweled sections without edge drains in these 

regions exhibited more faulting than those sections constructed with edge drains. 

Figure 25 contains plots showing the influence of edge drains and presence of dowels 

on the faulting of JPCP and JRCP LTPP sections in the wet-freeze and wet-nofreeze 

regions. 

Effect of Base Type 

The influence of two different base type categories (granular and stabilized) on 

JPCP and JRCP transverse joint faulting data was investigated for both doweled and 

undoweled LTPP sections. The data showed that faulting was generally higher for 
Average EDGE Faulting (mm) w &&AkOW*rn~O 

9 rD 

Ei Average EDGE Faulting (mm) 

r: &chLdJONdao\OO~ 

Average EDGE Faulting (mm) 
Sections with Dowels Sections without Dowels 

Linear (Draim) 

= = - hear (No Drains) 

08 

"'t 

Linear (Drains) - Linear (No Drains) 

ESAL's (thousands) ESAL's (thousands) 

Figure 24. Plots showing the influence of edge drains on faulting of dowel and 

undoweled JPCP and JRCP sections. 

doweled and undoweled sections with granular base types. The difference appears to 

be more dramatic for undoweled sections, as expected. These conclusions are 

supported by linear trends and visual inspections of the data shown in figure 26. 

An investigation of the influence of base type category and climatic region on the 

transverse faulting of JPCP and JRCP sections was completed for the available LTPP 

data. For the doweled sections, base type category was found to have no significant 

influence on development of transverse joint faulting in the wet-freeze or wet-nofreeze 

climatic regions. No conclusions could be made about the doweled sections in the dry 

climatic regions due to insufficient data. For the undoweled sections, base type 

category did not appear to have a significant influence on faulting in the dry-freeze or 

wet-nofreeze climatic regions. It did, however, appear to show a trend for the 

undoweled sections in the wet-freeze region. 

For this region, sections with granular base types appeared to exhibit more faulting 

than those with stabilized base types. No conclusions were made about the undoweled 

sections in the dry-nofreeze region because of insufficient data. Figures 27 and 28 

contain plots showing the influence of base type category and climatic region on the 

faulting of doweled and undoweled sections, respectively. 
Sections WITH Dowels 

Wet-freeze) 

-8 J I 

ESAL's (thousands) 

Sections WITH Dowels 

(Wet-nofreeze) 

-Linear @rains) - - Linear (No Drains) 

-81 I 

ESAL's (thousands) 

Sections WITHOUT Dowels 

Wet-freeze) 

ESAL's (thousands) 

Sections WITHOUT Dowels 

(Wet-nofreeze) 

-81 I 

ESAL's (thousands) 

Figure 25. Plots showing the influence of dowels and climate on JPCP and JRCP 

faulting in wet-freeze and wet-nofreeze regions. 
Sections WITH[ Dowels Sections m0UT Dowels 

ESAL's (thousands) 

Figure 26. Plots showing the influence of base type and dowels on faulting of PCP 

and JRCP. 

; Linear (Qaulm) 

near (StrCilliS4 

Figure 27. Plots showing the influence of base type and climatic region on faulting of 

doweled JPCP and JRCP sections. 
Figure 28. Plots showing the influence of base type and climatic region on faulting of 

undoweled JPCP and JRCP sections. 

Effect of PCC Elastic Modulus 

The influence of two different PCC elastic modulus (laboratory-measured) levels 

(Epr- < 27.6 MPa and 227.6 MPa) on JPCP and JRCP transverse joint faulting data was 

investigated for both doweled and undoweled LTPP sections. The linear trends 

through the data showed that faulting was generally higher for sections where the PCC 

elastic modulus was in the higher category (EKc 2 27.6 MPa). A visual inspection of 

the data appeared to show that this impact was greater for the doweled than the 

undoweled sections. Figure 29 shows the influence of PCC elastic modulus category on 

faulting of doweled and undoweled JPCP and JRCP. 

Presence of Widened Lane (Lane Width) 

The influence of two different lane width categories (lane width = 3.6 m and 2 3.6 

m) on JPCP and JRCP transverse joint faulting was investigated for both doweled and 

undoweled sections. The data showed that faulting was generally less for those 

doweled and undoweled sections with widened lanes. Figure 30 contains plots 

showing the influence of lane width category on the faulting of doweled and 

undoweled JPCP and JRCP. 
Sections WITH mls Sections mom Dowels 

ESAL's (thousands) 

Figure 29. Plots showing the influence of PCC elastic modulus on faulting of doweled 

and undoweled JPCP and JRCP (all climatic regions included). 

Sections WlTH Dowels 

Lmear (- 96 m) 6 - Linear (2 3.6 m) 

*t 

6 

W 10,000 20,000 30,000 40;: 6 

- 

ESAL's (thousands) 

Sections WITHOUT Dowels 

' ' ' Linear (>3.6 rn) 

. 

( * 10,008 * ZO,~ 30,000 40.p 

ESAL's (thousands) 

Figure 30. Plots showing the influence of lane width on faulting of doweled and 

undoweled JPCP and JRCP sections. 
Eflect of Outside Shoulder Type 

The influence of two different outer shoulder types (KC and non-PCC) was 

investigated for both doweled and undoweled JPCP and JRCP LTPP sections. The 

linear trends through the doweled section data showed that faulting was generally 

higher for the sections with non-PCC shoulder types, as expected. The opposite trend 

was observed for the undoweled LTPP sections. Undoweled LTPP sections with PCC 

shoulders developed more faulting on average than those undoweled sections with 

non-PCC shoulders. Figure 31 contains plots showing the influence of outside shoulder 

type category on the faulting of doweled and undoweled JPCP and JRCP LTPP sections 

(data from all climatic regions are included). 

Effect of Transverse Joint Sealant 

Linear trends suggest that greater transverse joint faulting developed for doweled 

sections with unsealed joints and undoweled sections with sealed joints. For both 

cases, the difference between sealed and unsealed joints did not appear to be that 

significant. Figure 32 contains plots showing the influence of joint sealant on the 

faulting of doweled and undoweled JPCP and JRCP LTPP sections (data from all 

climatic regions are included). An analysis of the sensitivity of transverse joint faulting 

data to the presence of joint sealant and climatic region did not reveal any clear trends 

because of the lack of data in many regions. 

Effect of Joint Sealant Type 

The investigation of the influence of joint sealant type showed that the majority of 

the doweled sections with large faulting values were from sections with asphalt or 

rubberized asphalt joint sealant. These trends hold true for both the wet-freeze and 

wet-nofreeze climatic regions (limited data were available for the sections in the 

regions with dry conditions). No obvious trends of the faulting of the undoweled 

sections on the basis of joint sealant could be determined for the data in all climatic 

regions. 

However, a breakdown of the data by climatic region showed that the majority of 

the high faulting values experienced by undoweled sections in the wet-freeze region 

were from sections with asphalt or rubberized asphalt joint sealant. The majority of the 

high faulting values experienced by undoweled sections in the wet-nofreeze region 

were from sections with a preformed joint sealant. Figure 33 contains plots showing the 

influence of joint sealant type on the faulting of doweled and undoweled JPCP and 

JRCP LTPP sections (data from all climatic regions are included). 
Sections WITH Dowels 

-31 I 

ESAL'e (thousands) 

1 I PCC 1 

Linaer (PCC) - - - Lhrear(Ncn PCC) i +.! 

E5AL1s (thousands) 

Figure 31. Plots showing the influence of shoulder type on faulting of doweled and 

undoweled JPCP and JRCP. 

sections WITH m1s Sedions mm Dowels 

-8 J i 

ESAL's (thousands) 

10 r-Xz7-l Linear I- 

-8 J ' I 

ESAL's (thousands) 

Figure 32. Plots showing the influence of joint sealant on faulting of doweled and 

undoweled JPCP and JRCP. 
Figure 33. Plots showing the influence of joint sealant type on faulting of doweled and 

undoweled JPCP and JRCP. 

Figure 34 contains plots showing the influence of joint sealant type and climatic region 

on the faulting of doweled and undoweled JPCP and JRCP LTPP sections. 

10 7 

8 -- 0 Lw Ma] Siliaone 

6 -- X 

X 

X 

X; * - ............................. ....................... * ....................................... 0 

X OXr t +a*, 

J 

20,m 3poo am 

Influence of Construction Practices 

ESAL's (thousands) ESAL's (thousands) 

$ 

-4-4 

-6 

-8 

10 

8 -- 

+ ..................................................................... 

1 

=Po0 4,330 

The Level 2 investigation involved analyzing the effect of the two main construction 

practices that influence faulting, transverse joint forming method and dowel placement 

method. The data available for this stage of the analysis were limited. Therefore, more 

data will be required for further analysis. Using the data available currently, the results 

of the bivariate analysis are as follows. 

-- 

-- -6 

Effect of Transverse Joint Forming Method 

-- 

The influence of transverse joint forming method on the development of faulting 

was investigated for doweled and undoweled pavements. Three different joint 

forming methods were observed for the available LTPP data-plastic insert, metal 

insert, and sawed. For the doweled sections, a comparison of metal inserts to sawed 

joints showed that more faulting developed for those sections constructed using metal 

inserts. The analysis of the undoweled sections showed that those sections with plastic 

inserts exhibited significantly less faulting than those with sawed joints. 

-8- 
Sections WrrH Dowels 

(Wet-freeze) 

Sections WlTHOUT Dowels 

(Wet-freeze) 

ESAL's B housands) 

Sections Wrm Dowels 

(Wet-nofreeze) 

XRubberAsphalt 

a Low Mod Silicone 

ESAL's (thousands) 

X Rubber Asphalt 

.Low Mod Silicone 

ESAL's (thousands) 

Sections WITHOUT Dowels 

(Wet-nofreeze) 

 rubber Asphalt 

-8 

ES4L's (thousands) 

Figure 34. Plots showing the influence of joint sealant type and climatic region on the 

faulting of doweled and undoweled JPCP and JRCP. 
A secondary analysis of the influence of climatic region on these data sets was not 

completed due to insufficient data. Figure 35 contains plots showing the influence of 

transverse joint forming method and presence of dowels on the transverse joint faulting 

of JPCP and JRCP LTPP sections (data from all climatic regions were included). 

Effect of Dowel Placement Method 

The influence of dowel placement method on the development of transverse joint 

faulting was also investigated for doweled UPCP and JRCP) pavements. Two different 

dowel placement methods were observed for the available LTPP data-baskets and 

inserted using a mechanical dowel bar inserter. The data showed that those sections in 

which the dowels were mechanically inserted exhibited significantly more joint 

faulting (on average) than those placed using traditional baskets. Most of the data used 

for this analysis were for pavements built over 10 years ago. It is possible to obtain 

different results from pavement built recently using newer dowel bar inserter 

equipment. Figure 36 contains a plot showing the influence of dowel placement method 

on the transverse joint faulting of doweled JPCP and JRCP LTPP sections (data from all 

climatic regions were included). 

Sediom WITH Dowels Sections WCWT bwe1s 

. ESAL's (thousands) ESALts (thousands) 

Figure 35. Plots showing the influence of transverse joint forming method and dowels 

on the transverse joint faulting of JPCP and JRCP. 
10 

+ Baskets 1 

Linear (Baskets) 

3 6 ti - - - - Linear (Inserter) 

Y - 

-8 1 

ESAL's (thousands) 

Figure 36. Plot showing the influence of dowel placement method 

on transverse joint faulting of JPCP and JRCP. 

Canonical Discriminant Analysis for Faulting 

Canonical Functions 

The canonical functions were developed using the SAS statistical 

Following are two canonical variables, CANFl and CANF2, developed and used to 

classify pavements according to performance: 

CANF1 = - 0.032MIW + 0.154 STYP -1.02 SKEW - 1.35WET - 0.57 

where 

MINT = annual average minimum temperature, "C 

STYP = shoulder type, 1 = rigid, 0 = others 

SKEW = skew joint, presence = 1,0 = otherwise 
WET = wet region, wet = 1,O = otherwise 

DRAIN = presence of drain, present =1,O = otherwise 

RLS = radius of relative stiffness, mm 

DOW = dowel bar diameter, mm 

KVAL = modulus of subgrade reaction, kPa/mm 

The mean values of CANFl and CANF;! for the expected, below, and above expected 

categories are given in table 5. A plot of CANFl versus CANF2 for each observation of 

the data used in the analysis is shown in figure 37. The plot clearly demarcates the 

areas (CANF1, CANF2) where the pavements in the different performance categories 

are located. This is in agreement with the mean values of the canonical variables for the 

different categories. The areas of the pavement performance categories are summarized 

as follows: 

a The canonical variable CANFl was greater than 0.5 for pavements classified as 

performing above expectation, 

a The canonical variable CANFl was less than 0.5 for pavements classified as 

performing as expected or below expectation. 

Summary 

The results of the canonical discriminant analysis show that CANFl is a canonical 

variable that distinguishes between pavements performing above expectation and the 

other performance categories. An in-depth look at the canonical function for predicting 

CANFl shows that the function is generally in agreement with engineering principles 

and past studies. Pavements with higher CANFl values tend to perform better in 

faulting than those with smaller CANF1 values. An increase in the minimum 

temperature, the use of skewed joints, and pavements located in wet environments 

decrease pavement performance, while the use of rigid shoulders, a higher radius of 

relative stiffness, dowels, and a higher modulus of subgrade reaction results in higher 

CANFl values and, hence, better performing pavements. 

Table 5. Class means for canonical variables CANFl and CANF2. 

Also, pavements located in dry regions or pavement in wet regions with adequate 

drainage facilities experienced less faulting. All these trends are reasonable and are in 

agreement with past research results. 

Analysis of Variance for JPCP Faulting 

A comprehensive analysis of variance was performed to determine the effect and 

significance of each design feature and site condition variable on faulting. The ANOVA 

procedure was also used to compute the mean faulting for each design feature and site 

condition used in the ANOVA. The mean faulting was calculated for the different 

classification levels of each variable. Multiple comparison test of the means (Duncan's 

Test) was then used to determine if there were significant differences between the mean 

faulting distress levels for the different classes of a given design feature or site 

condition variable, Table 6 is a summary of the analysis of variance Duncan's multiple 

range test results. Table 6 shows clearly the variables that have a significant effect on 

faulting. Table 7 is a summary of variables that, though not significant, show quite a 

difference in mean faulting values for different class levels and thus potentially could 

affect faulting in more carefully controlled experiments. The effects of the variables are 

discussed in the next few sections. 

Influence of Site Conditiom 

Effect of Annual Number of Wet Days 

The results presented in table 6 show that pavements located in areas with a high 

number of wet days are more likely to experience faulting. This is in agreement with 

past studies that have shown that pumping of the underlying pavement materials is 

directly related to excess moisture in the pavement and is a major cause of erosion and 

faulting. Pavements located in such areas will need to be designed with materials that 

are less susceptible to erosion, such as stabilized permeable bases or high-quality 

stabilized bases. 

Efect of Subgfade Type 

The ANOVA results showed that even though the difference in measured faulting 

for pavements with fine or coarse subgrade was not significant, pavements with coarse 

subgrades experienced less faulting than pavements with fine subgrades. Coarse 

subgrades in general provide greater permeability and therefore less free water 

available for erosion. 
Table 6. Summary of analysis of variance results for faulting. 

Annual I less than 100 

Significant 

Difference 

Duncan 

Class* 

number of 

wet days 

Base elastic I less than 690 1 0.073 

Variable Faulting, mm 

greater than 140 

Classification 

100 to 140 

0.11 I B I 

I greater than 6900 I 0.025 I B I 

I I 

modulus, 

MPa 

0.06 

690 to 6900 

Joint 

coefficient' ' 

Presence of 

dowel 

Radius of 

relative 

stiffness, mrn 

A 

Load transfer 

system 

Yes 

0.09 

less than 3.2 

greater than 3.2 

no dowels 

dowels 

less than 890 

890 to 1150 

greater than 1150 

Drainage 

coefficient, C, 

Drain 

Pavement 

tY Pe 

aggregate interlock 

Dowels 

I 

A 

0.03 

0.07 

0.08 

0.02 

0.05 

0.08 

0.03 

* Duncan's multiple range test; those with the same letter are not significantly differen1 

less than 1 

greater than 1 

Yes 

no edge drain 

edge drain 

JPCP on untreated 

JPCP on treated 

JPCP on non-AC 

JPCP over unbound 

JPCP on AC treated 

1.77 

0.46 

A 

B 

A 

B 

A 

A 

B 

0.084 

Yes 

Yes 

Yes 

0.07 

0.03 

2.85 

1.4 

0.97 

0.91 

0.37 

A 

B 

Yes 

0.05 I B 

A 

A 

B 

A 

B 

C 

C 

D 

- 

Yes 

Yes 

Yes 
Table 7. Summary of analysis of variance results for variables that potentially 

influence faulting. 

Classification Signif icant 

Difference 

II Wet I wet areas 

Base type not stabilized 0.073 A 

stabilized 0.054 A No 

11 mm I greater than 240 mrn I 0.060 A 

I 

No 

Joint forming sawed 1.10 A No 

method plastic insert 0.56 A - # Dowels 1 other methods 1 1.72 1 A I 

11 I preplaced on baskets I 0.27 1 C I 

* Duncan's multiple range test; those with the same letter are not significantly different 

placement 11 method 

Efect of Wet and Freeze Climates 

The mean faulting values for pavements located in both wet and freeze areas show 

that pavements located in extreme climates tend to experience more faulting. This is a 

reasonable result, since an increase in precipitation generally corresponds to higher 

pumping and faulting. The ANOVA test also shows that pavements in areas with 

precipitation of more than 1 m experience more faulting than those in areas with 

precipitation less than 1 m. This again confirms the relationship between moisture and 

faulting. Pavements in areas with high rates of precipitation (greater than 1 rn/yr) 

mechanically installed 0.68 B Yes 
should be designed with adequate drainage facilities to reduce moisture damage. 

Influence of Desi~n Features 

Effect of Base Modulus 

Table 6 shows that pavements with a higher base elastic modulus experienced less 

faulting. This is because an increase in base modulus results in less deflections of the 

PCC slab joints. Stabilized bases have higher moduli than granular untreated bases, 

and lean concrete bases have higher moduli than PCC- or AC-treated bases. 

Effect of the Presence of Dowels 

The ANOVA results show clearly that pavements with dowels perform better than 

those without. Dowels provide load transfer across the joints of adjacent slabs, and load 

transfer reduces deflections at the joints. 

Effect of Radius of Relative Stiffness 

Table 6 shows that pavements with a higher radius of relative stiffness experienced 

less faulting. This is a reasonable result because it is known from mechanistic analysis 

that pavements with higher radius of relative stiffness values have less corner 

deflections and, thus, less pumping and faulting. The variable radius of relative 

stiffness is obtained from several design variables and is defined as follows: 

Q* = E"h3 / [12*k(l-~~)] 

where 

E = PCC elastic modulus, kPa 

h = PCC thickness, mrn 

k = modulus of subgrade reaction, kPa/mm 

p = Poisson's ratio 

The values of these design variables should be selected to increase the radius of relative 

stiffness, thereby increasing performance. 

Effect of Drains and Drainage Co@cient 

The ANOVA analysis shows that the provision of drains to a pavement generally 

seduces the occurrence and severity of faulting. This can be explained by the fact that 

adequate drainage reduces the amount of free water within the pavement structure, 

thereby reducing the potential for pumping and the erosion of underlying pavement 

materials. Providing a base material that enhances drainage also reduces faulting. This 
is shown by the fact that an increase in the pavement base drainage coefficient reduces 

faulting. The use of stabilized free draining bases should therefore reduce faulting. 

Effect of Base Type 

The ANOVA results showed that, even though the difference in measured faulting 

for pavements with or without stabilized bases was not significant, pavements with 

stabilized bases experienced less faulting than pavements with unstabilized bases. This 

is because stabilized bases are less erodible and reduce the potential of pumping. 

Effect of Skewed Joints and PCC Thickness 

The analysis shows that pavements with skewed joints experienced less faulting 

than those without. The use of skewed joints is a means of enhancing load transfer. This 

is true especially for JPCP with undoweled joints. It enhances load transfer because 

wheels of the same axle strike the joints at different times, reducing the impact of a 

given axle load on the joint. Pavements with thicker PCC slabs also experienced less 

faulting. Thicker PCC slab thickness increases the strength of the pavement, reducing 

deflections and, hence, pumping and faulting. 

Effect of Pavement Type 

The pavement types with slab placed directly on the subgrade material showed the 

highest amount of faulting. This is because subgrade materials in general tend to be 

weaker than base materials and other pavement material types. Past studies have 

shown that treated subgrades are not strong enough to prevent pumping. Also, 

subgrade materials tend to contain a higher percentage of fine material than base 

materials or stabilized materials, and this increases the potential for such materials to 

pump, resulting in faulting. 

Effect of Load Transfer System 

The ANOVA analysis shows that the best form of load transfer is through the use of 

dowels and not aggregate interlock. Pavements with aggregate interlock experienced a 

greater level of faulting than those without. This agrees with the ANOVA results for 

the effect of design features, which shows that pavements with dowels perform better 

than those with other kinds of load transfer devices. 

Influence of Construction Practices 

Results of the comprehensive ANOVA and Duncan's multiple comparison test are 

presented in tables 6 and 7. A summary of the significant construction variables that 
influence faulting are discussed in the next few sections. 

Effect of Dowel Placement Method 

The ANOVA analysis results show that pavements with dowels placed from 

preplaced baskets experience about a third of the faulting as do pavements with dowels 

placed mechanically. This may be due to the care taken by workmen in placing the 

dowels manually. As noted in earlier sections of this chapter, most of the data used for 

this analysis were for pavements built more than 10 years ago. It is possible to obtain 

different results from pavements built quite recently using newer dowel bar inserter 

methods and equipment. 

Effect of Joint Forming Method 

The pavements with joints formed by plastic insert experienced far less faulting 

than those with sawed joints. This may be due to the fact that sawing PCC slabs 

introduces weaknesses into the pavement structure. 

Summary 

Using univariate analysis (appendix A), bivariate analysis, canonical discriminant 

analysis, and ANOVA, the design and climatic variables that influence the occurrence 

and severity of faulting in JPCP have been analyzed. According to the results of the 

analysis, pavements with strong or stiff foundations tend to develop less faulting. This 

is shown by the fact that pavements with higher radius of relative stiffness and 

pavements constructed on coarse and stronger base material experience less faulting 

and therefore performed better. Also, the presence of dowels resulted in better 

performance because dowels enhanced load transfer and decreased deflectiohs. 

The analysis also showed that pavements located in climates with higher annual 

number of wet days and with extreme climate performed worse than did those in 

milder climates. These observations are reasonable and could serve as basic guidelines 

for pavement design and evaluation and for the prevention of faulting. Also, the 

canonical function CAW2 can be a useful tool in classifying newly designed 

pavements and to check preliminarily for adequate performance through the 

pavement's design life. 

The construction features that influence faulting are all related to the pavement 

joint. The ANOVA result shows that the joint forming method, dowel placement 

method, and load transfer mechanism all influence faulting. Using the right methods of 

load transfer, such as dowels and joint formation, will therefore minimize or eliminate 

faulting and enhance performance. 

4. EVALUATION OF SITE, DESIGN, AND CONSTRUCTION 

VARIABLES THAT INFLUENCE TRANSVERSE CRACKING 

Introduction 

The primary design features, climate, and site conditions that influence PCC 

transverse cracking were identified in appendix B. They include the site conditions of 

traffic, climate, and subgrade support, as well as the specific design features that are 

incorporated into pavements to reduce cracking, such as PCC slab thickness, the 

presence of dowels, and drainage facilities. 

A prioritized list of the features and practices (relevant to each distress type) 

proposed for investigation with LTPP data was presented in table 1 of chapter 2 of this 

report. The list of data elements available in the LTPP database for transverse cracking 

is presented in table 8. This list was not meant to be exhaustive, and any other variables 

that were found to significantly influence JPCP transverse cracking were investigated 

thoroughly. The first step in the performance evaluation analysis was a comprehensive 

univariate analysis, which is presented in appendix A. This chapter presents the results 

of a preliminary bivariate analysis, canonical discriminant analysis, and ANOVA to 

identify the data elements that influence transverse cracking and, hence, pavement 

performance. 

For the bivariate analysis, the effects of individual design features were investigated 

separately for each pavement type. As a step toward developing specific regional 

recommendations, the data were further divided based on the four climatic regions in 

order to observe any regional differences. Because there are relatively few data 

sections in the LTPP database, engineering judgment was used to make some 

intelligent divisions of the data in order to keep the individual evaluation data sets a 

reasonable size. Each of these divisions is specific to the distress data type being 

investigated and is explained in greater detail in later sections of this chapter. 

Preliminary Evaluation of Transverse Cracking Data 

Jointed plain concrete pavements typically develop fatigue cracks caused by traffic 

loading. Repeated traffic loading at the slab edge, midway between transverse joints, 

results in the development of critical stresses at the bottom of the JPCP. The damage 

caused by the critical stresses accumulates over time and eventually causes a transverse 

crack to develop approximately midway between the joints. Slab thickness was chosen 

as the most important pavement characteristic influencing JPCP transverse (fatigue) 

cracking; therefore, it is the appropriate variable for Level 1 analysis, 
Table 8. Key design features, sit2 conditions, and construction practices 

available in LTPP database for transverse cracking. 

* Data elements were calculated from other original data elements in the LTPP 

database. 

The conclusions and inferences drawn at the bivariate analysis stage are preliminary. 

There is a great possibility at this stage of the analysis for some of the results to be 

misleading because of confounding effects and interactions between the data elements. 

The results of these analyses are summarized and presented as follows. 
Transverse Cracking Level 1 Investigation 

The Level 1 analysis consisted of dividing the transverse cracking data into two slab 

thickness categories (< 250 mm and 2 250 mm). The LTPP JPCP data were then 

evaluated to determine the effect of slab thickness on transverse cracking. 

Jnfluenre of Slab Thickness on !PCP Transverse Fatipue Cracking 

Figure 38 shows a plot of number of cracks per mile versus cumulative ESAL's for 

all JPCP sections in the LTPP database. Lines were fit through the data to more clearly 

observe the trends. The available LTPP data indicate that thinner JPCP sections (< 250 

mm) experience more fatigue cracking than thicker JPCP sections & 250 mm). Thicker 

pavements showing a high number of cracks at an early age may be due to inadequate 

construction practices. 

The data were analyzed further to determine the effect of both slab thickness and 

climate on transverse cracking. The linear trends showed that thinner JPCP sections 

(< 250 mm) in dry-freeze and wet-freeze regions developed much more transverse 

fatigue cracking than thicker JPCP sections (1 250 m). The opposite trends were 

visible in the wet-nofreeze region, where the thicker JPCP sections appeared to exhibit 

more cracking. Finally, slab thickness showed no effect on the development of 

transverse cracking for those JPCP sections in the dry-nofreeze region. 

Transverse cracking, as discussed in appendix B, occurs for different reasons, 

including traffic loading, temperature and other climate-related loads, and early age 

cracking, There is the need therefore to investigate further the opposing trends 

observed. Figure 39 contains plots showing the influence of slab thickness and climatic 

region on the transverse fatigue cracking of JPCP sections. 

Transverse Cracking Level 2 Investigation 

Level 2 investigations of the transverse cracking data consisted of dividing the Level 

1 subsets into smaller data sets based on many of the possible influential site and 

design features identified in table 8. A summary of the Level 1 and 2 variables chosen 

for the transverse cracking investigation is presented in table 9. Each Level 2 variable is 

investigated by plotting the observed joint cracking data (expressed as the combined 

total number of low-, medium-, and high-severity cracks per kilometer for the section) 

versus the cumulative ESAL's as determined from regression equations of the LTPP 

annual traffic data. The preliminary analysis consisted of creating plots showing the 

influence of each Level 2 variable at each Level 1 definition for all of the data 

observations, regardless of climatic region. Linear trend lines were again fit through 

the data. The different groups of plots were first created for each pavement type. To 

(Dry-freeze Region) (Dry-nofreeze Region) 

0 1 0,000 20,000 30,000 40,000 

ESAL's (thousands) 

Thickness >= 250 mm 

Linear (Thickness < 250 mm) 

160 - - - Linear(Thickna~sh 250 mm) 

140 - 

1 120 - k 4 100. 

S 

-8 so- d 8 - 

40 - 

(Wet-freeze Region) (Wet-nofreeze Region) 

Unear (Thickness c 250 mm) 

0 10,000 20,000 30,000 40,000 

ESAL'e (thousands) 

Thi&neas r= 250 mm 

-Linear (Thickness c 250mm) 

160 - Linear(Thickness >=Z5Omm) 

Figure 39. Plots showing the influence of slab thickness and climatic region on the 

transverse fatigue cracking of JPCP sections. 
Table 9. Level 1 and Level 2 data elements used in the evaluation of 

transverse cracking. 

Slab thickness 

observe any differences in the influence of the Level 2 variables between climatic 

regions, these plots were then recreated by using only the respective data in each 

climatic region (when data were available). The results of these analyses are 

summarized and presented below. 

Influence of Site Conditions 

The specific effects of the different site conditions on pavement performance were 

investigated as part of this study. The site-related variables investigated were 

categorized as follows: 

e Freezing index: < 270 and 2 270 "C days. 

Average annual precipitation: < 1 m and 2 1 m/ yr. 

e Subgrade type: fine or coarse-grained. 

e Modulus of subgrade reaction: < 40.7 and 2 40.7 kPa/mrn. 

Effect of Freezing Index 

For JPCP data with thickness < 250 mm, sections with a FI 2 277 "C days showed 

greater cracking than sections with FI <277 "C days. Freezing index did not appear to 

have any significant effect on the sections with slab thickness 2 250 mm. Plots of the 
Scctionswith%ckness<W)m Mor~ with Thickwas >= 250 rn 

ESAL1s ((thousands) ESAL's (timxwmds) 

Figure 40. Plots of JPCP fatigue cracking versus traffic as influenced by slab thickness 

and freezing index (all climatic regions included). 

JPCP data as influenced by slab thickness and freezing index are presented in figure 40. 

Effect of Annual Precipitation 

For JPCP data in both thickness categories (< 250 mm and 2 250 mm), sections with 

more annual precipitation 1.0 m) showed greater cracking than sections with less 

annual precipitation (< 1.0 m). It also appeared that the significance of precipitation 

level was greater for sections with slab thickness c 250 mm. Plots of the JPCP data as 

influenced by slab thickness and average annual precipitation are presented in figure 

41. 

Effect of Su bgrade Type 

An analysis of the sensitivity of JPCP transverse fatigue cracking data to subgrade 

type showed that, for both levels of slab thickness (c 250 mm and 2 250 mm), the 

sections with coarse-grained subgrade soils appeared to develop more cracking than 

those built on fine-grained soils. An investigation of the influence of region on these 

data was not completed due to the lack of sufficient data in each climatic region. 

Figure 42 contains plots showing the influence of subgrade type on the cracking of 

JPCP LTPP sections. 
Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

0 10,000 20,000 30,000 40,000 

ESAL's (thousands) 

0 10,000 20,000 30,000 40,000 

ESALis [thousands) 

Figure 41. Plots of JPCP fatigue cracking versus traffic as influenced by slab thickness 

and average annual precipitation (all climatic regions included). 

Sections with Thickness >= W3 mm 

160 Linear (Base Type - Granular) 

- - - Linear (Base Type - Stabilized) 

$ 120 

ESAL's (thousands) 

Sections with Thickness < 250 rnrn 

160 Linear (Base Type- G~tifar] 

= = - Linear (Base Type = Stabilized) 

0 10,000 20,000 30,OOMJ 40,000 

ESAL's (thousands) 

Figure 42. Plots showing the influence of slab thickness and subgrade type on the 

cracking of JPCP LTPP sections (all climatic regions included). 

66 
Effect of Modulus of Subgrade Reaction (Backcalculated Static k-value) 

The influence of the two different static k-value levels (k-value < 40.75 kPa/mm and 

2 40.75 kPa/mm) on JPCP transverse fatigue cracking was investigated for the sections 

divided into categories of slab thickness (c 250 mm and 2 250 mm). The results 

showed that for both thickness categories, the sections with k-value 2 40.75 kPa/mm 

exhibited more cracking. In fact, the sections with k-values < 40.75 kPa/rnrn showed 

minimal cracking in comparison. Figure 43 contains plots showing the influence of slab 

thickness and k-value on the transverse fatigue cracking of JPCP LTPP sections (all 

climatic regions included). An analysis of the sensitivity of JPCP transverse fatigue 

cracking data to k-value for different climatic regions was limited by the lack of data in 

some regions. The available data did, however, show that more JPCP cracking 

developed for the category of k-value > 40.75 kPa/mm in all climatic regions except the 

dry-nofreeze. This particular region showed virtually no cracking for those sections 

where the k-value was 2 40.75 kPa/mm. Figures 44 and 45 contain plots showing the 

influence of k-value category and climatic region on the cracking of FCP sections in the 

different thickness categories. 

Jnfluence of Desi~n Features 

The specific effects of the different design features on pavement performance were 

investigated as part of this study. The design features investigated were categorized as 

follows: 

0 Average joint spacing: 5 4.6 m and > 4.6 m 

m Base type: stabilized or unstabilized 

PCC 28-day modulus of rupture: < 4.5 MPa and 2 4.5 MPa 

The effects of these design variables on faulting are presented in the next few sections. 

Effect of Average Transverse Joint Spacing 

A Level 2 analysis of the effects of average transverse joint spacing on JPCP 

transverse fatigue cracking involved further separating the sections already divided by 

slab thickness into the two different average joint spacing categories (1 4.6 m and > 4.6 

m). The results showed that for the thinner sections (< 250 mm), more cracking 

developed in the sections with longer joint spacing. For the thicker sections, the trends 

showed that average joint spacing had little or no effect on the development of 

transverse fatigue cracking. Figure 46 contains plots showing the influence of slab 

thickness and average joint spacing on the transverse fatigue cracking of JPCP LTPP 

sections (data from all climatic regions were included). 
Sections with Thickness e 250 rnrn Sections with Thickness >= 250 mm 

0 10,OOO 20,OOO 30,000 40,000 0 l0,oOO 20,000 30,000 40,000 

ESALts Ithoumds) ESALts (thousands) 

Figure 43. Plots showing the influence of slab thickness and k-value on the transverse 

fatigue cracking of JPCP LTPP sections (all climatic regions included). 

Sections with Thickness % 250 mm 

(Wet-freeze) 

+ kvalue<40.7Wmm 

0 k-value = 40.7 kPalmm 

Limar (k-vaRp c 40.7 Wdmm) 

160 - - - Limr (k-wlw h 40.7 kPiJmm) 

Sections with Thickness r 250 mm 

(Wet-nofreeze) 

Figure 44. Plots showing the influence of k-value category and climatic region on the 

transverse fatigue cracking of JPCP sections with thickness 2 250 mm. 

6 8 
Sections with Thickness cr 250 mm 

(Wet-nofreeze) 

k-value < 40.7 kPa/mm 

-Linear &-values 40.7 kPa/mm) 

160 ' ' ' Linear (I-value z= 40.7 kPa/mm) 

0 

? 

k-value >- 40 7 kPahlml 

Unesr gvalue c 60.7 kPm) 

- - - Unesr (k-velus >= 40.7 kPs/mn) 

0 

0 

I 

. 

r. 

8 

-- 0 

Sections with Thickness < 250 mm 

(Dry-nofreeze) 

200 

0 

0 10,000 20,000 30,000 40,000 

ESALts (thousands) 

Sections with Thickness < 250 mm 

(Wet-freeze) 

180 ./ ,/ 1 0 k-v~>-40.7W~/m 1 1 Linear &-value < 40.7kPalnun) 

160 - ' Lineu~-v.iue>-40.7kP~/mm) 

Sections with Thickness < 250 mm 

(Dry-freeze) 

4 k-valalw e 40.7 kP6'mn 

k-value r- 40.7 kPmm 

160 - - - Linear (k-vdur -40.7 Wm] 

ESAL's (thousands) 

Figure 45. Mots showing the influence of k-value category and climatic region on the 

transverse fatigue cracking of JPCP LTPP sections with thickness < 250 mm. 

Efect of Base Type 

The influence of two different base types (granular and stabilized) on JPCP 

transverse fatigue cracking was investigated for the sections already divided into 

categories of slab thickness (c 250 mm and 2 250 mm). The results showed that for 

both thickness categories, the sections with stabilized bases exhibited more cracking (on 
McmswithThickmss<25Omm Mom with Thidcms >= 250 mrn 

BAL's (thousands) 

Figure 46. Plots showing the influence of slab thickness and average joint spacing on 

the transverse fatigue cracking of JPCP LTPP sections (all climatic regions included). 

average). It is also important to note the linear trend developed for the thinner sections 

(thickness < 250 mm), with granular bases showing virtually no transverse fatigue 

cracking. Figure 47 contains plots showing the influence of slab thickness and base 

type on the transverse fatigue cracking of JPCP LTPP sections (data from all climatic 

regions were included). 

Effect of PCC Strength (28-day Modulus of Rupture) 

The influence of two different modulus of rupture categories (M, < 4.5 MPa and M, 

- > 4.5 MPa) on JPCP transverse fatigue cracking was investigated for the sections 

already divided into categories of slab thickness (< 250 mm and 2 2511 mm). The results 

showed that for thinner pavements, JPCP sections with modulus of rupture < 4.5 MPa 

exhibited more cracking (on average). However, for thicker pavements, JPCP sections 

with M, 2 4.5 MPa showed the most cracking, while the sections with M, < 4.5 MPa 

showed virtually no cracking. PCC slabs with very high modulus of rupture tend to 

have higher stress intensity factors, increasing the likelihood of crack propagation. This 

trend will, however, require further investigation. Figure 48 contains plots showing the 

influence of slab thickness and modulus of rupture on the transverse fatigue cracking of 

JPCP LTPP sections (data from all climatic regions were included). 
Sections with Thickness < 2.50 mm Sections with Thickness z= 250 mm 

+ Base Type - Granular 

0 Bm Type - Stabilized 

Lineer (Base Type - Gmular) 

0 " ' ' Lheer (Base Type - Stabiliaed) 

0 

0 10,000 20,000 30,000 40,000 

ESAL's (thousands) 

-Linear (Brs Type- Grauk) 

- - - Linear (BaseTyp-SGablked) 

0 10,000 20,000 30,000 40,W 

ES AL's (thousands) 

Figure 47. Plots showing the influence of slab thickness and base type on the transverse 

fatigue cracking of JPCP LTPP sections (all climatic regions included). 

Lima (M < 45 hiPaJ 

1Mf = rn LInsir(Mr45WqI 

Figure 48. Plots showing the influence of slab thickness and modulus of rupture on the 

transverse fatigue cracking of JPCP LTPP sections (all climatic regions included). 
Influence of Construction Practices 

The specific effects of construction practices on pavement performance were 

investigated as part of this study. The construction practices investigated are as follows: 

@ Paver type. 

Curing method. 

Transverse joint forming method. 

The effects of these construction practices on transverse cracking are presented in the 

next few sections. 

Effect of Paver Type 

The JPCP fatigue cracking data (already divided into categories based on slab 

thickness) were further divided into data sets based on paver type. An analysis of 

these data sets showed that sections constructed with a slip form paver exhibited more 

transverse fatigue cracking (on average) than those constructed with a side form paver. 

These trends held true for both slab thickness categories (c 250 mm and 2 250 mm). It 

is important to note that the majority of the sections in both data sets were constructed 

using a slip form paver. A secondary analysis of the effects of climatic region on the 

data sets was not completed because of the lack of sufficient data. Figure 49 contains 

plots showing the influence of paver type and slab thickness on the transverse fatigue 

cracking of JPCP LTPP sections (data from all climatic regions is included). 

Effect of Curing Method 

The influence of curing method on the development of transverse fatigue cracking 

in JPCP sections was investigated as part of this study. The use of burlap polyethylene 

was compared with the use of a curing compound, and the results showed that the 

thinner JPCP sections (< 250 mm) exhibited more fatigue cracking (on average) when 

the burlap polyethylene was used. The opposite trend was observed for the thicker 

JPCP sections (1 250 mm), where those sections cured with burlap polyethylene 

exhibited virtually no cracking. It is important to note, however, that very few sections 

in the database were cured using the burlap polyethylene. Figure 50 contains plots 

showing the influence of curing method and slab thickness on the transverse fatigue 

cracking of JPCP LTPP sections (data from all climatic regions is included). 
Sections with Thickness e 250 mm %dims with Thickness >= 250 mm 

0 10,000 20,000 30,000 4wc"j 0 10,000 20,000 30,000 40,000 

BSAL's (thousands) ESAL's (thousands) 

Figure 49. Plots showing the influence of paver type and slab thickness on the 

transverse fatigue cracking of JPCP LTPP sections. 

Sections with Thickness < W3 mm Sections with Thickness >= 250 mrn 

5 

0 Curing Conpcund 

0 Lima (Bvrlq, Pdy) 

- 0 

00 

0 

, 

, 

, 

C 

Oc ' 0 0 

H = =l I 

10,OM) 20,000 30,000 

ESAL's (thousands) 

Figure 50. Plots showing the influence of curing method and slab thickness on the 

transverse fatigue cracking of JPCP LTPP sections (all climatic regions included). 
Efiect of Transverse Joint Forming Method 

Three different joint forming methods were observed for the available LTPP 

data-plastic insert, metal insert, and sawed. For the JPCP sections, the sections with 

sawed joints consistently showed more transverse fatigue cracking than those sections 

with joints formed with plastic inserts. This trend held true for both thickness 

categories (< 250 mm and 2 250 mm). The highest observed fatigue cracking values for 

the thicker sections came from those constructed using metal inserts. A secondary 

investigation looking at the influence of climatic region was not completed due to 

insufficient data. Figure 51 contains plots showing the influence of transverse joint 

forming method and slab thickness on the transverse fatigue cracking of JPCP LTPP 

sections (data from all climatic regions were included). 

Sections with Thickness .c 250 mm Sections with Thickness >= W) mm 

0 l0poa 20,000 30,m 40~00 0 10,m 20,000 30,000 40w 

ESAL's (thousands) ESAL's (thousands) 

Figure 51. Plots showing the influence of transverse joint forming method and slab 

thickness on the transverse fatigue cracking of JPCP (all climatic regions included). 

200 7 200- - 

180 -- 

160 

6 140 

Y 

$ 120 

0 Linear (Plastic Insert) 

-- Linear (Saws$ 

0 -- 

0 -- 

+ Plastic Insert 

4 loo-- e 

U so 0 

g 60- 

40-- 

la-- 

6 1~-- 

2 

& 120 

0 I Lim (Plaslic hs@ 

'Linear (Metd Insert) 

10 

-- 

r 

-P I 

I 

0 0 r I I r + r I # 0 I I + 

20-00 rC 

0 

u 80 o 

60 

40 

20- 

OI 

-- 00 

-- 0 

I -- I r 

+ 

051' 00 

O+ l 0 00 

4 *: I 
Canonical Discriminant Analysis for JPCP Transverse Cracking 

The LTPP database for JPCP transverse cracking had very few failed sections or 

sections with extremely high numbers of transverse cracks. Therefore, the pavement 

sections were classified as "above" expectation or "other." Because there were only two 

performance classifications, a single canonical variable, CANTC1, was sufficient for 

discriminating between the two performance classes. The final form of the canonical 

discriminant function CANTCl is as follows: 

CANTCl = -0.0025hmc + 3.0 ST + 8.25Cd - 2.17DRAIN - 0.0063hb 

where 

h,c 

ST 

Cd 

DRAIN 

hb 

WET 

DAYST 

RJTSP 

SKEW 

PCC slab thickness, mm 

sealant type, 1 = preformed, 0 = other 

drainage coefficient 

presence of drain, present =I, 0 = other 

base thickness, mm 

wet region, wet = 1,0 = other 

annual number of days with temperature below 0 "C 

1 = random joint spacing, 0 = uniform joint spacing 

skew joint, presence = 1,O = other 

The mean values of CANTCl are given in table 10. A plot of CANTCl versus 

CANTCl for each observation of the data used in the analysis is shown in figure 52. 

The plot clearly shows the areas where the pavements in the different performance 

categories are located. Pavements with a negative CANTCl value perform above 

expectation, and the others had CANTCl values greater than zero. This is in agreement 

with the mean values of the canonical variables for the different categories. The 

locations of the different pavement performance categories are summarized as follows: 

e Pavement sections performing above expectation were generally located in the 

area where CANTCl is less than -0.5. These are the sections of interest. 

Pavement sections performing as expected or below expectation were located in 

the area where CANTCl is greater than -0.5. 

Summarv of Canonical Discriminant Analvsis 

The model for predicting the canonical variable CANTCl shows that contrasting 

PCC slab thickness, drainage type, base thickness, and use of random joint spacing 

against sealant type, drainage coefficient, wet climates, annual number of days below 

Table 10. Class means for canonical variable CANTCI. *I Above Expectation 

0 "C, and the presence of skewed joints separates pavements according to performance 

classification. An increase in PCC slab and base thickness strengthens the pavement 

structure and results in better transverse cracking performance according to the model, 

and this is in agreement with results from past studies. 

Pavements located in extreme climates tend to perform worse than those in milder 

climatic areas, as shown by the influence of number of days below 0 "C and wet 

regions. One interesting trend is that the provision of drains generalIy improves 

performance by making CANTCl more positive; however, providing a more open base 

by increasing the value of the drainage coefficient results in worse transverse cracking 

performance. It must be noted, however, that the canonical variables are independent 

uncorrelated variables developed from basic pavement characteristics and do not 

always agree with known engineering trends. 

Analysis of Variance for JPCP Transverse Cracking 

A comprehensive ANOVA was performed to determine the significance of each site 

condition, design feature, and construction practice that affects transverse cracking. 

Also, the ANOVA procedure was used to compute the mean transverse cracking for 

each design feature and site condition used in the ANOVA analysis. Multiple 

comparison test of the means (Duncan's Test) was then used to determine if there were 

significant differences between the mean transverse cracking distress levels for the 

different classes of a given design feature or site condition variable. 

Table 11 shows clearly the variables that have a significant effect on faulting. Table 

12 is a summary of variables that, though not significant, show quite a difference in 

mean transverse cracking values for different class levels. These variables could be 

significant in better designed experiments. The effect of the variables is discussed in the 

next few sections. 
Table 11. Summary of analysis of variance results for JPCP transverse cracking. 

* Duncan's multiple range test; those with the same letter are not significantly different. 
Table 12. Summary of analysis of variance results for variables that potentially 

influence transverse cracking. 

11 Variable I Classification I Percent Slabs with I Duncan I Significant 

I I Transverse Cracking I Class* 1 Difference 

Random joint Yes 2.582 

spacing No 3.225 

A 

A 1 No 

Base type not stabilized 1.191 A 

stabilized 4.341 A No 

Subgrade type fine 1.665 A 

coarse 4.895 A No 

Underlying Yes 

PCC material 

we 

unbound material I 0.15 C I I 

Number of 3 12 A Yes 

layers 4 10 A 

Fine agg. less than 1300 4.5 A Yes 

kg'm3 greater than 1300 19 B 

* Duncan's multiple range test; those with the same letter are not significantly different. 
IC 

Effect of Annual Number of Wet Days 

The results presented in table 11 show that pavements located in areas with a low 

number of wet days (less than 100 days per annum) are the most likely to experience 

transverse cracking. This is followed by pavements in areas with more than 140 wet 

days per annum. This may be due to the fact that pavements in dry areas experience 

more shrinkage and warping stresses and, hence, more transverse cracking. Also, the 

dry regions generally tend to be hotter than the wet areas, increasing the potential of 

temperature gradient stresses and curling. Pavements in areas with moderate climates 

(100 to 140 wet days per annum) performed best and had the lowest amount of 

transverse cracking. 

Efect of Wet and Freeze Climates 

The mean transverse cracking values for pavements located in extreme climates 

were higher than those located in the milder climatic regions (moderate number of wet 

days and freezing). This is a reasonable result, since an increase in precipitation 

generally corresponds to lower pavement foundation strength, extreme temperatures 

result in either curling or shrinkage, and both result in higher levels of transverse 

cracking. The ANOVA test also shows that pavements in areas with precipitation of 

more than 1.0 rn experience more transverse cracking than those in areas with 

precipitation less than 1.0 m. 

Effect of Mean Temperature 

Pavements in locations with higher mean temperatures are more likely to be 

subjected to curling stresses and, therefore, cracking. The ANOVA results show that 

pavements located in areas with mean temperatures less than 7.2 T experienced 

approximately 7 to 8 times less transverse cracking than those in regions with higher 

mean temperatures. 

Efect of Subgrade Type 

The ANOVA results showed that, even though the difference in measured percent 

slabs with transverse cracking for pavements with or without stabilized bases was not 

significant, pavements with stabilized bases experienced more transverse cracking than 

pavements with unstabilized bases. Pavements with coarse subgrade types also 

experienced more transverse cracking. This trend is in agreement for pavements with 

higher modulus values for both base and subgrade. The explanation for this must be 

similar to that for higher modulus values explained in the previous section. 
Effect of Modulus of Subgrade Reaction 

The ANOVA results showed clearly that JPCP with very strong foundations (high 

subgrade modulus and a high base modulus) experienced more transverse cracking, 

This is unusual and is contrary to expectation unless temperature gradients are 

considered. JPCP with high effective modulus of subgrade reaction values, or high base 

and subbase values, have high curling stresses and, therefore, more transverse 

cracking. 

Effect of PCC Slab Thickness 

The ANOVA results clearly show that pavements with thicker slabs experience less 

cracking. Thicker slabs in general are able to withstand wheel loads and temperatwe 

stresses better than thinner ones, resulting in less bending moments, stresses, and 

deflections. Also, a microcrack initiated at the bottom of the PCC slab will take more 

load cycles to propagate through the slab to fracture and crack. 

Efjcct of Base Modulus 

Results for the effect of base modulus on transverse cracking show that there was a 

significant amount of cracking for pavements with cement-treated or lean concrete 

bases (base modulus > 6900 MPa). This may be due to the fact that very stiff bases and 

foundations provide less support when the PCC slab is curled up or warped, and this 

increases the likelihood of cracking with the application of wheel loads. Bases with a 

high modulus are also likely to crack by themselves, with the cracks propagating 

through the PCC slab. 

The group with the second highest mean transverse cracking value was pavements 

with base modulus less than 690 MPa (unstabilized base material). This is due to the 

fact that such pavements generally have a weak base and provide less support to the 

PCC slab. The group with the least amount of cracking was pavements with base 

modulus values close to 4485 MPa (AC-treated base). This type of base provides 

uniform support to the PCC slab and is flexible enough to bend with or accommodate 

movements of the slab when curled or warped. 

Effect of Modulus of Rupture 

The effect of modulus of rupture on transverse cracking is interesting. Pavements 

with modulus of rupture values less than 4.5 MPa and greater than 4.8 MPa 
experienced more transverse cracking than pavements with values between 4.5 and 4.8 

MPa. It is obvious from engineering principles that pavements with lower modulus of 

rupture values are less likely to withstand the stresses imposed by wheel loads and, 

therefore, are more likely to fracture and crack. However, fracture mechanics principles 

show that higher elastic modulus and modulus of rupture values tend to increase the 

stresses that propagate cracks through most materials, including pavement surface 

layer material, resulting in a faster crack growth rate. 

Effect of Dowel Diameter 

The ANOVA result shows clearly that pavements with dowels perform better than 

those without. Dowels provide load transfer across the joints of adjacent slabs. Load 

transfer reduces deflections and stresses throughout the PCC slab, particularly at the 

top of the mid-section of the PCC slab. The reduction of stresses and deflections at this 

location lowers the possibility of top-down mid-section transverse cracking. 

Effect of Base Type 

Pavements with flexible foundations such as asphalt-treated base generally perform 

better with less cracking than those with rigid foundations (lean concrete bases). This is 

because during hot or cold seasons the PCC slab curls or warps. Flexible pavements are 

better suited to accommodate the changes and movements of the slab and provide a 

uniform base or foundation during these periods of slab movements. Rigid pavements, 

on the other hand, cannot provide such support, resulting in voids beneath the slab and 

a higher potential for cracking. 

Effect of Underlying PCC Material Type and Number of Layers 

The pavement types with slab directly overlaid on subgrade material showed the 

highest amount of faulting. This is because subgrade materials tend to be weaker than 

base and other pavement material so the pavement will likely experience more 

deflections. The ANOVA results also showed that pavements with more layers had less 

transverse cracking. Such pavements have stronger foundations, whicl-1 provides better 

support to the PCC slab and reduces the potential for excessive deflections. Also, 

pavements with more layers generally have a more flexible foundation that provides 

adequate and uniform support at all times and reduces cracking. 
Influence of Construction Practices 

Effect of Coarse and Fine Aggregate Content 

The pavements with more coarse aggregates (greater than 1,800 kg/m3) and less 

fines (less than 1,300 kg/m3) experienced less transverse cracking than those with less 

coarse aggregates and more fine aggregates. This is expected because concrete materials 

with a high content of coarse material generally are stronger. They can withstand 

higher compression, higher stresses, and have a higher resistance to fracture and 

cracking. Such concrete slabs also tend to be less susceptible to disintegration from 

moisture and therefore are more likely to resist cracking and experience fewer 

transverse cracks. 

Summary 

Using univariate analysis, frequency plots, canonical discriminant analysis, and 

ANOVA, the variables that influence the occurrence and severity of transverse cracking 

in JPCP have been analyzed. 

According to the results of the analysis, pavements with thicker PCC slabs tend to 

perform above expectation. Also, pavements with higher modulus of rupture values 

performed better, and the presence of dowels resulted in better performance because 

they provide enhanced load transfer and decreased deflections and stresses. 

Pavements with stronger foundations (stabilized bases and higher modulus of 

subgrade reaction values) were more likely to experience transverse cracking. The 

analysis also showed that pavements located in extreme climates (higher freezing or 

high temperatures) performed worse than those in milder climates. These observations 

are reasonable and could serve as basic guidelines for pavement design and evaluation. 

Also, the canonical function CANTCl was used to determine if pavements performed 

better than expected or otherwise. This can be a useful tool in classifying newly 

designed pavements and to check preliminarily for adequate performance through the 

pavement's design life. 

The construction factors that influence transverse cracking are all related to 

pavement strength and mix design. These factors included using more dense concrete 

with a higher percentage of coarse aggregate, using more layers to strengthen the 

pavement foundation and structure, and placing the PCC slab aver a firm, strong 

stabilized base. Past studies also have shown that stronger pavements have reduced 

transverse cracking; however, the use of rigid bases such as lean concrete often results 

in more cracking because of the lack of flexibility and the base itself being prone to 

cracking. Therefore, using very stiff bases must be avoided when possible. 

5. EVALUATION OF SITE CONDITIONS AND PCC PAVEMENT 

DESIGN FEATURES THAT INFLUENCE ROUGHNESS 

Introduction 

Many of the primary design features, site conditions, and construction practices that 

influence PCC pavement performance were identified in appendix B. They include the 

site conditions of traffic, climate, and subgrade support, as well as the specific design 

features that are incorporated into pavements to improve performance, such as PCC 

slab thickness, the presence of dowels, and drainage facilities. 

A prioritized list of the features and practices (relevant to each distress type) 

proposed for investigation with LTPP data was presented in table 1 of chapter 2 of this 

report. Some of the data elements identified during a literature review are not available 

in the LTPP database, The list of data elements available in the LTPP database for 

roughness is presented in table 13. This list was not meant to be exhaustive, and any 

other variables that were found to significantly influence roughness were investigated 

thoroughly. The first step in the performance evaluation analysis was a comprehensive 

univariate analysis, which is presented in appendix A. This chapter presents the results 

of a preliminary bivariate analysis and a more detailed canonical discriminant analysis 

and ANOVA to identify the data elements that influence pavement performance. 

For the bivariate analysis, the effects of individual design features were investigated 

separately for each pavement type. The data were further divided based on the four 

climatic regions to observe any regional differences. Because there are relatively few 

data sections in the LTPP database, engineering judgment was used to make some 

intelligent divisions of the sections in order to keep the individual evaluation data sets 

a minimum size. Each of these divisions is specific to the distress data type being 

investigated and is described in greater detail in later sections of this chapter. 

Preliminary Investigation of Roughness 

Roughness is a distress that develops on all three rigid pavement types PCP, PCP, 

and continuously reinforced concrete pavement [CRCP] ). The development of 

roughness in each of these pavement types is generally influenced by the same primary 

design variables and construction practices. Each pavement type was analyzed 

separately using slab thickness as the Level 1 analysis variable. The conclusions and 

inferences drawn at the bivariate analysis stage are preliminary. There is a great 

possibility at this stage of the analysis for some of the results to be misleading because 

of confounding effects and interactions between the data elements. The results of these 

analyses are summarized and presented as follows. 
Table 13. Key design features, site conditions, and construction practices 

available in LTPP database for roughness. 

Roughness Level 1 Analysis 

The Level 1 analysis of the roughness data consisted of dividing the roughness data 

into two slab thickness categories (c 250 mm and 2 250 mm). The next step was to 

analyze the effect of slab thickness on roughness for JPCP, JRCP, and CRCP. 
The JPCP and JRCP data showed that sections with greater slab thicknesses (> 250 

mm) generally exhibited higher IRI values than thinner slabs. The opposite trend was 

observed for the CRCP data, as the linear trend representing the thicker sections 

decreased as cumulative ESAL's increased. Figure 53 contains plots illustrating the 

influence of slab thickness on the IRI of JPCP, JRCP, and CRCP sections in all climatic 

regions. 

Roughness Level 2 Analysis 

Level 2 investigations of the IRI data consisted of dividing the Level 1 subsets into 

smaller data sets based on the possible influential site conditions, design features, and 

construction practices identified in table 14. The LTPP data were used to investigate as 

many of these design features and site conditions as possible. Each Level 2 variable was 

investigated by plotting the observed roughness, expressed as IRI versus cumulative 

ESALfs. The preliminary analysis consisted of creating plots showing the influence of 

each Level 2 variable at each Level 1 definition for all of the data observations, 

regardless of climatic region. Linear trend lines were fit through the data in order to 

more easily identify trends in the data. To observe any differences in the influence of 

the Level 2 variables between climatic regions where necessary, the plots were 

recreated (for each pavement type) by using only the respective data in each climatic 

region. The results of the analyses on the influence of site conditions and design 

features on roughness are summarized and presented in the next few sections. 

Influence of Site Conditions 

The specific Level 2 climatic investigations looked at the effects of freezing index, 

freeze-thaw cycles, average annual precipitation, average annual temperature range, 

and the average annual number of wet days on the measured IRI of JPCP, JRCP, and 

CRCP sections at different slab thickness levels (< 250 mm and 2 250 mm). For these 

cases, each variable was divided into the following categories for investigation: 

a Freezing index: < 270 and 2 270 "C days. 

o Annual freeze-thaw cycles: < 70 and 2 70. 

Average annual precipitation: < 1.0 and 2 1.0 m/yr. 

o Average annual temperature range: < 11 and 2 11 "C. 

Average annual number of wet days: < 125 and 2 125 days. 

Subgrade type: fine- or coarse-grained. 

o Modulus of subgrade reaction: c 40.7 and 2 40.7 kPa/mm. 

The effects of the site conditions investigated are presented as follows. 
Table 14. Level 1 and 2 data elements used in the evaluation of roughness (IRI) data. 

Effect of Climatic Region 

An analysis of the JPCP IRI data by climatic region showed the highest IRI values in 

the wet-freeze region and the lowest in the dry-nofreeze region. For all four dimatic 

regions, the thicker JPCP sections (thickness 2 250 mm) generally exhibited higher IRI 

values than the thinner JPCP sections. Figure 54 contains plots showing the influence 

of slab thickness and climatic region on the IRI of JPCP LTPP sections. Only a few JRCP 

data points were available in the regions with dry conditions; therefore, making an 

analysis across all climatic regions is difficult. The JRCP sections in the wet-freeze and 

wet-nofreeze regions showed that thicker pavements had higher IRI values than 

thinner JRCP sections. Figure 55 contains plots showing the influence of slab thickness 

on the IN of JRCP LTPP sections in the wet-freeze and wet-nofreeze climatic regions. 
Figure 53. Plots showing the influence of slab thickness on the IRI of JPCP, JRCP, and 

CRCP sections (all climatic regions included). 
( Wet-nofreeze region) ( Wet-freeze region) 

0 Thickness z=250m 

4 I 1- Linear (Thickness < 250 mrn) 

I I = Linear (Thickness >=250mm) I 

FSAL's (thousands) ESAL's (thousands) 

Figure 54. Plots showing the influence of slab thickness and climatic region on the IRI 

of PCP LTPP sections. 

Figure 55. Plots showing the influence of slab thickness and climatic region on the IRI 

of JRCP LTPP sections. 
An analysis of the CRCP IRI data by climatic region showed consistent trends for 

three of the four climatic regions. No conclusions could be made about the influence of 

slab thickness in the dry-nofreeze region because of insufficient data. The other three 

climatic regions showed that thinner CRCP sections exhibited higher BI values than 

the thicker CRCP sections. Figure 56 contains plots showing the influence of slab 

thickness and climatic region on the IRI of CRCP LTPP sections. 

Effect of Freezing Index 

The JPCP and CRCP data at both levels of slab thickness showed that sections 

subjected to a freezing index 2 270 OC days were generally rougher than those sections 

subjected to the lower level of freezing index, The JRCP data appeared to show an 

opposite trend at both levels of slab thickness. The thinner JRCP sections with freezing 

index < 270 OC days were clearly rougher than those sections experiencing the higher 

level of freezing index. Overall, the JPCP sections appeared to have the highest IH 

values, followed by the JRCP sections, with the CRCP sections being the smoothest of 

the three pavement types. Plots of the JPCP, JRCP, and CRCP IN data as influenced by 

slab thickness and freezing index are presented in figures 57 through 59, respectively. 

EfJect of Annual Freeze-Thaw Cycles 

The JPCP data at both levels of slab thickness showed that sections subjected to 

freeze-thaw cycles 2 70 were generally rougher than those sections subjected to the 

lower level of freeze-thaw cycles. The JRCP and CRCP data did not show any 

significant trends, as the linear regressions fit through the data were generally very 

close together, or in most cases, crossing. Plots of the JPCP data as influenced by slab 

thickness and freeze-thaw cycles are presented in figure 60. 

Effect of Average Annual Precipitation 

The JPCP data for sections with slab thickness < 250 rnm showed that sections 

subjected to an average annual precipitation 2 1.0 m were generally rougher than those 

sections receiving less precipitation. The trend appeared to be the opposite for JPCP 

sections. For both thickness levels, the largest IRI values came from sections receiving 

< 1.0 m of precipitation. Plots of the JPCP data as influenced by slab thickness and 

average annual precipitation are presented in figure 61. 

The JRCP data for sections with slab thickness < 250 mrn showed that those sections 

subjected to an average annual precipitation 2 1.0 m were generally rougher than those 

sections receiving less precipitation. Plots of the JRCP data as influenced by slab 

thickness and average annual precipitation are presented in figure 62. The amount of 

annual precipitation appeared to have no significant effect on the IN of CRCP sections, 
Figure 56. Plots showing the influence of slab thickness and climatic region on the IRI 

of CRCP LTPP sections. 

Sections with Thickness < 250 mm 

5 - 

FI >= 277 deg. C days 

-Unoar (Fi < 277 de$. C days) 

t 

Sections with Thickness >= 250 mm 

5 - 

0. 0. 

0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESALts (thousands) ESALts (thousands) 

FI r- 277 deg. C days 

Unear (FI c 277 deg. C days) 

0 00 

' ' ' Unw (Fl>= 277 6~. C days) 

i 

Figure 57, Mots of JPCP IRI versus traffic as influenced by slab thickness and freezing 

index (all climatic regions included). 

92 

1 I I 
Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

+ FI c 277 deg C dep 

FI >= 277 dq. C days 

Linear (FI c 277 deg. C days) 1 ' ' ' Li w (FI 2- 277 dog. C days) 

I I -I 

I + F1 c277deg. C days I 

FI W= 277 deg. C days 

Linear (FI >= 277 deg. C days) 

0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESAL's (thousands) ESAL's (thousands) 

Figure 58. Plots of JRCP IRI versus traffic as influenced by slab thickness and freezing 

index (all climatic regions included). 

Sections with Thickness < 250 mm 

5 

FI r= 277 deg. C days 

Linear (FI c 277 deg. C days) 

1' 1 

Sections with Thickness >= 250 mm 

5 

0 0 

0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESAL's (thousands) ESAL's (thousands) 

- 

Figure 59. Plots of CRCP IRI versus traffic as influenced by slab thickness and freezing 

index (all climatic regions included). 

Flc277deg C days 

Fl w= 277 deg. C days 

Lmw (FI < 277 deg C days) 

Linear (FI >= 277 deg. Cdays) 

. * I 

C ' 

00 C'' 

I I . 
Sections with Thickness < 250 mm 

5 

0 

0 10,000 20,000 30,000 

ESAL's (thousands) 

7 

FTCYC*= 70 

-Linear (FTCYC c 70) 

f 

0 

Sections with Thickness >= 250 mm 

Linear (FTCW: c 70) 

' ' ' Linear (FTC YC>= 70) 

C 6 

0 1 4 I I 1 

40,000 0 10,000 20,000 30,000 40,000 

ESAL's (thousands) 

Figure 60. Plots of JPCP IRI versus traffic as influenced by slab thickness and freezethaw cycles (all climatic regions included). 

Sections with Thickness < 250 mm 

5 

-Linear (Precip c 1 m) 

0 

Sections with Thickness >= 250 mm 

5 * Pmp<lm 

Q Precp>= 1 rn 

Limeer (Precip < 1 m) 

- - ' Linear (Precip >- 1 m) 

0 0 

0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESAL's (thousands) ESAL's (thousands) 

Figure 61. Plots of JPCP IRT versus traffic as influenced by slab thickness and average 

annual precipitation (all climatic regions included). 
Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

4 ' Linear (Precip >= 1 m) 

0 -l I I 

0 10,000 20,000 30,000 40,000 . 

ESAL's (thousands) 

0 

0 10,MX) 20,000 30,000 40,000 

ESAL's (thousands) 

Figure 62. Plots of JRCP IRI versus traffic as influenced by slab thickness and average 

annual precipitation (all climatic regions included). 

as the linear trends were virtually the same within each thickness category. Plots of the 

CRCP data as influenced by slab thickness and average annual precipitation are 

presented in figure 63. 

Effect of Average Annual Temperature Range 

The JPCP data for sections with slab thickness 2 250 mm showed that sections 

subjected to a smaller average annual temperature range (< I I "C) generally exhibited 

higher IRI values than those sections subjected to a larger temperature range (2 11 OC). 

No clear trends could be determined for the PCP thinner sections (thickness < 250 

mm). 

The JRCP sections showed the complete opposite trends. The thicker JRCP sections 

subjected to the larger temperature range developed higher IRI values than those 

sections subjected to the smaller temperature range. The CRCP plots showed that 

average annual temperature range had little or no significant influence on the IRI 

within either slab thickness category. Plots of the JPCP, JRCP, and CRCP IRI data as 

influenced by slab thickness and average annual temperature range are presented in 

figures 64,65, and 66. 
Sections with Thickness < 250 mm Sections with Tlrickness >= 250 rnrn 

5 

Precipc 1 m 

0 Preclp >- 1 rn 

Llnear (Precb c 1 m) 

' tlnear (Preclp >- 1 rn) 

0 ! I 

0 10,000 20,000 30,000 40,000 

ESAL's (thousands) 

Preoips 1 m 

Linear (Precp < 1 rn) 

", 

0 10,000 20,000 30,000 40,000 

ESAL's (thousands) 

Figure 63. Plots of CRCP IN versus traffic as influenced by slab thickness and average 

annual precipitation (all climatic regions included). 

Sections with Thickness c 250 mm Sections with Thickness >= 250 mm 

5 

TenpRangec 11 dogC 

Terrp Ranger- 1 1 deg C . -Linear (Tenp Range c 1 1 dog C) 

4t#. ' Linear (Tenp Rangers 11 deg C) 

0 

0 10,000 20,000 30,000 40,000 

ESAL's (thousands) 

rn 

& Tenp Range < 1 1 deg C 

- o Tenp Range >= 11 deg C bnesr (Temp Range c 11 dog C) 

" ' Linear (Temp Range = 11 d6g C) 

.--' 1-- 

0 -I 

0 10,000 20,000 30,000 

ESAL's (thousands) 

Figure 64. Plots of JPCP IRI versus traffic as influenced by slab thickness and average 

annual temperature range (all climatic regions included). 
Sections with Thickness c 250 mm Sections with Thickness >= 250 mm 

o! I 

0 10,000 20,000 30,000 40,000 

ESAL's (thousands) 

5 - + TerrpRangeclldegC 

Unw vemp Range c 11 d~g C) 

3 - Linw(TmpRqe>m 11 dogG) 4 

1 1 

5 l0,OOO 20ioOo 30,000 A0,ooo 

ESAL1s (thousands) 

Figure 65. Plots of JRCP IRI versus traffic as influenced by slab thickness and average 

annual temperature range (all climatic regions included). 

-- 

Sections with Thickness c 250 mm 

5 

* TompRmgeclldsgC 

0 TonpRangor~lldqC 

-,near (Tonp Rang. < 11 deg C) 

g g Linoar(TwrpRanger11depC) 

0 

0 10,000 20,000 30,000 

ESAL's (thousands) 

Sections with Thickness r= 250 mm 

Toy, Range r- 4 1 dng C 

Linw (Tsnp Range < 11 dog C) - Linear(TerrpRange~11degg 

40,000 0 l0,OOO 20,000 30,OQO 40,000 

ESAL1s (thousands) 

Figure 66. Plots of CRCP IN versus traffic as influenced by slab thickness and average 

annual temperature range (all climatic regions included). 
Efect of Average Annud Number of Wet Bays 

The JPCP data for sections with slab thickness 2 250 mm showed that sections 

subjected to an average annual number of wet days < 125 were more likely to develop 

higher IRI values than those sections subjected to 2 125 wet days. The data 

representing the &inner JPCP sections showed that the average annual number of wet 

days had no significant influence on those sections. Plots of the JPCP data as 

influenced by slab thickness and average annual number of wet days are presented in 

figure 67. The JRCP data showed that sections subjected to more average annual 

number of wet days (2 125) developed higher IN values (on average) regardless of slab 

thichess category. Plots of the JRCP and CRCP data as influenced by slab thickness 

and average annual number of wet days are presented in figures 68 and 69. 

Effect of Subgrade Type 

An analysis of the sensitivity of IRI to subgrade type for JPCP sections revealed no 

consistent linear trends (regardless of thickness category). The JRCP sections in both 

thickness categories exhibited higher IN values for those sections with fine-grained 

subgrades. The linear trends for the thinner CRCP sections also showed that those 

sections with fine-grained subgrade types exhibited higher IRI values than those with 

coarse-grained subgrade types. No conclusions could be made for the CRCP sections 

with slab thickness 2 250 mm. A more general visual inspection of the data for all three 

pavement types appeared to show the highest IN values coming from sections 

constructed on fine-grained soils. An analysis of the influence of slab thickness and 

subgrade type within each climatic region was not possible because of insufficient data. 

Figures 70 through 72 contain plots showing the influence of slab thickness and 

subgrade type on the IRT of JPCP, JRCP, and CRCP in all climatic regions. 

Effect of Modulus of Subgrade Reaction (k-value) 

An analysis of the sensitivity of IRE to k-value for JPCP pavements revealed that 

sections with k-values < 40.7 kPa/mm generally exhibited higher IRI values than those 

sections with k-values 40.7 kPa/mm (regardless of slab thickness category). The 

opposite was observed for the thicker sections (thickness 2 250 mm), where the sections 

with k-values 2 40.7 kPa/mm showed higher IRI values. Figure 73 contains plots 

showing the influence of slab thickness and static k-value on the IRI of JPCP LTPP 

sections. The LTPP data collected for JRCP sections showed that k-value had no 

significant influence on ]IN for thinner JRCP sections (thickness < 250 mm). The thicker 

JRCP sections (thickness 2 250 mm) tended to exhibit higher IRI values for sections 

with larger k-values (k-value 2 40.7 kPa/mm). Figure 74 contains plots showing the 

influence of slab thickness and static k-value on the IRI of JRCP LTPP. Backcalculated 

static k-value did not appear to have any significant influence on the IRI of CRCP. 
Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

5 

No, d Wet Days < 125 

Linear (No. d Wet Days < 125) 

- - - Linear (No. d Wet Days >- 125) 4 

o! I I 0 

0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESALts (thousands) ESAL's (thousands) 

Figure 67. Plots of JPCP IRI versus traffic as influenced by slab thickness and average 

annual number of wet days (all climatic regions included). 

-- No. d Wet Days c 125 

0 No d Wet Day$ >= 125 

tinw (No. d Wet Days c 125) 

' ' ' Linear (No. d Wet Days r 125) 

-*-a 

rr-r 

I I 

Sections with Thickness < 250 mm Sections with Thickness >= 250 nun 

5 5 

i 

. 

0 No. of Wet Days r 125 

bnea (No. d Wet Days < 125) 

0 

1- .** n n 

4 

**. 

I 

0 No of Wet Days r 126 

Linear (No. of Wet Days < 125) 

0 O 

m0 1-=- 

9-- 

-1--- e 2. - - 0 .-• r 0 *ti- * * v- A* 

I I 

ESAL's (thousands) ESAL's (thousands) 

Figure 68. Plots of JRCP IRI versus traffic as influenced by slab thickness and average 

annual number of wet days (all climatic regions included). 
Sections with Thickness < 250 mm 

5 * No. of Wet Days e 125 I / 

No, of Wet Days z= 125 

-Linear (No. d Wol Days c 125) 

Sections with Thickness >= 250 mm 

5 

No, of We! Days r- 125 

Llnw (No of W& Days c 126) 

1 

I* 

, -I- o! I I I 0 I .. 0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESAL's (thousands) ESALis (thousands) 

Figure 69. Plots of CRCP IRI versus traffic as influenced by slab thickness and average 

annual number of wet days (all climatic regions included). 

Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

5 

Fine Griuned Subgrade 

-Linear (Coarse Grand Subgrade) 

4tm 

' ' ' Linear (Fine Grained Sukgrade] 

- 

Cme Grrvned Subgrade 

g Fine Grained Subgrade 

Ltnear (CoarseGrained Subgrade) 

LIW (FineGra~ned S&gade) + 0 . 1-- 

I 0 I 

0 10,000 20,000 30,000 40,OQO 0 10,000 20,000 30,000 40,000 

ESAL's (thousands) ESAL's (thousands) 

Figure 70. Plots showing the influence of slab thickness and subgrade type on the IRI 

of JPCP sections (all climatic regions included). 
Sections with Thickness < 250 mm Sections with Thickness r 250 mm 

- - - Linear (F~neGr- Sbgrde) 

ESAL's (thousands) 

Figure 71. Plots showing the influence of slab thickness and subgrade type on the IRI 

of JRCP sections (all climatic regions included). 

Sections with Thickness c 250 mm Sections with lluckness >= 250 mrn 

Linw (came Gmned Subgrade) 

- - ' Lmnw (Fine Graind Subgrade) 

5 

L~nmr (Coarse Gralrmd Subgrade) 

bnmr (Fine Grained Subgirade) 

* 

b 

I 

+ Coarse Grained Skkgra.de 

o Fine Giained Skkgrade 

0 ! 0 

0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESAL's (thousands) ESAL's (thousands) 

I 

Figure 72. Plots showing the influence of slab thickness and subgrade type on the IRI 

of CRCP sections (all climatic regions included). 
Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

k-value >= 40.7 kPelmm 

Linear (k-value < 40.7 kpelmm) 

5 5 

4 4 

C1 1 j 

2 3 

& t2 4 4 C2 

1 1 

0 0 

0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESAL's (thousands) ESAL's (thousands) 

Figure 73. Plots showing the influence of slab thickness and backcalculated static 

k-value on the IRI of JPCP sections (all climatic regions included). 

Sections with Thickness < 250 mm 

k-value r- 40.7 kPelmm 

-Linear @-value < 40.7 kPalmm) - ' - Linear (k-value >- 40.7 kPalmm) 

ESAL's (thousands) 

Sections with Thickness >= 250 mm 

5 

0 k-value r 40.7 kPelmm 

0 

0 10,000 20,000 30,000 40,000 

ESAL's (thousands) 

Figure 74. Plots showing the influence of slab thickness and backcalculated static 

k-value on the IRI of JRCP sections (all climatic regions included). 
Influence of Desim Features 

The specific Level 2 design features investigated as part of this study were joint 

spacing, base type, base elastic modulus, modulus of rupture, shoulder type, and 

percentage steel content. For these cases, each variable was divided into the following 

categories for investigation: 

0 Joint spacing. 

Base type. 

Base elastic modulus. 

Modulus of rupture. 

e Shoulder type. 

Percentage steel content (JRCP and CRCP). 

The results of the analysis is presented as follows. 

Effect of Average Joinf Spacing 

An analysis of the sensitivity of IRI to average transverse joint spacing (for JPCP and 

JRCP) revealed that joint spacing generally had very little or no influence on IRI. These 

conclusions are based on the virtually identical linear trends developed for the different 

average joint spacing categories within each of the four data sets representing each 

combination of pavement type and slab thickness. Figures 75 and 76 show these 

respective plots of JPCP and JRCP IRI versus traffic for the different thickness 

categories. 

Effect of Base Type 

The influence of the two base type categories (granular and stabilized) on the IRI of 

JPCP, JRCP, and CRCP was investigated for the sections already divided into categories 

of slab thickness (< 250 mm and 2 250 mm). Linear trends through the JPCP data 

showed higher IRI values (on average) for those sections with granular bases, 

regardless of thickness category. No clear trends were observed for the data in each 

individual climatic region. Figure 77 contains plots showing the influence of slab 

thickness and base type on the IRI of JPCP sections (data from all climatic regions were 

included). 

Linear trends through the data representing the thinner JRCP sections (thickness 

< 250 mrn) showed that base type had very little or no influence on the IRI of those 

sections. The conclusions were not the same, however, for thicker JRCP sections (L 250 

mm). Thicker JRCP sections with granular bases were found to exhibit higher IRI 

values (on average) than those sections with stabilized bases. Like the JPCP data, 
Sections with Thickness < 250 rnm Sections with Thickness >= 250 mm 

5 

0 Jdnt Spacing > S6 rn 

Linear (Joint Gpacing 6 3.6 rn) 

' ' ' Linw (Ja'nt Spacing >3.6tn) 

5 

0 Jdnt Spaeiig r 3.6 m 

Linsar (Joint wing c- 3.6 m) 

4 

0 l0,Ooo 20,Ooo 30,m 40,000 0 

0 10,000 20,000 

ESALts (thousands) ESAL's (thousands) 

Figure 75. Plots showing the influence of slab thickness and average joint spacing on 

the IRI of JPCP sections (all climatic regions included). 

Sections with Thickness < 250 mm 

Joint Spacing <- 14 rn 

Joint Spac~ng > 14 m 

Linear (Joint Spacing <= 14 m) 

Linear (Joint Spacing > 14 m) 

I I 

0 10,000 20,000 30,000 40,000 

ESALts (thousands) 

Sections with Thickness z= 250 mrn 

Lrnear (Join! Spacing <= 14 m) 

0 10,000 20,000 30,000 40,000 

ESAL's (thousands) 

Figure 76. Plots showing the influence of slab thickness and average joint spacing on 

the IRI of JRCP sections (all climatic regions included). 
Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

LIW (Granular Base) 

o! I 0 1 I I I I 

0 l0,oOO 20,m ~Ioc'o 40,@30 0 10,oOO 20,000 30,000 40,000 

ESAL's (thousands) ESAL's (thousands) 

Figure 77. Plots showing the influence of slab thickness and base type on the IRI of 

JPCP sections (all climatic regions included). 

no consistent trends were observed by looking at the data for different individual 

climatic regions. Figure 78 contains plots showing the influence of slab thickness and 

base type on the IRI of JRCP sections (data were included from all climatic regions). 

An analysis of the CRCP IRI data showed that base type had no influence on the IRI 

of thinner sections (< 250 mm) and very little influence on the IRI of thicker sections 

(2 250 mm). Insufficient data made it difficult to look at these trends within each 

climatic region. Figure 79 contains plots showing the influence of slab thickness and 

base type on the IRI of CRCP sections (data were included from all climatic regions). 

Effect of Base Elastic Modulus 

The influence of base elastic modulus (2 690 MPa and >690 MPa) was investigated 

for the JPCP, JRCP, and CRCP data sets already divided by slab thickness (< 250 mm 

and 2 250 mm). The investigation revealed that base elastic modulus only had a 

significant influence on the IRI of JPCP sections. The JPCP sections in both thickness 

categories were found to exhibit higher IRI values (on average) when the base elastic 

modulus was 690 MPa, This trend was particularly clear for the thicker JPCP sections 

in the wet-freeze region and all of the sections in the wet-nofreeze regions (both 
Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

Lii (Granular 0-4 

0 ! I I I 

0 10,m 20,000 30,000 40,m 

ESAL1s (thousands) 0 10,000 20,000 30,000 40,m 

ESALts (thousands) 

Figure 78. Plots showing the influence of slab thickness and base type on the IRI of 

JRCP sections (all climatic regions included). 

Sections with Thickness < W> mm 

0 Stabilized Base 

Linea (Gr- B=e) 

Lim (StPilized Base) 

0 ! I 

0 10,000 20,000 30,000 40,000 

ESAL's (thousands) 

Sections with Thickness r= 2.50 mm 

- Lii (Gmiar Baw) - - - Li! (stabiliaad Ebse) 

0 1 I 

0 14000 20,m 40,000 

ESAL's (thousands) 

Figure 79. Plots showing the influence of slab thickness and base type on the IN of 

CRCP sections (all climatic regions included). 
thickness categories included). It was difficult to determine clear trends in any of the 

other data sets (combinations of climatic region and slab thickness) because of a lack of 

sufficient data. Figure 80 contains plots showing the influence of slab thickness and 

base elastic modulus on the IRI of JPCP sections (data were included from all climatic 

regions). 

Effect of PCC 28-day Modulus of Rupture 

An investigation of the influence of PCC 28-day modulus of rupture on IRI was 

conducted for the JPCP, JRCP, and CRCP LTPP sections. The analysis revealed that 

PCC 28-day modulus of rupture had little or no significant influence on the IRI of JPCP, 

JRCP, and CRCP sections when data from all climatic regions were grouped together. 

An analysis of the data in the individual climatic regions did, however, show some 

clear trends. The investigation of the JPCP sections in the regions with wet conditions 

showed that higher IRI values were exhibited by sections with a PCC 2&day modulus 

of rupture 2 4.5 MPa (regardless of thickness category). No clear trends were observed 

for the JPCP in the other climatic regions because of insufficient data. 

Effect of PCC Elastic Modulus 

The influence of two different PCC elastic modulus levels (Em < 27,000 MPa and 

- > 27,000 MPa) on IRI was investigated for JPCP, JRCP, and CRCP sections already 

divided by thickness category (c 250 mm and 2 250 mm). Only the JPCP data showed 

that PCC elastic modulus had a significant influence on IRI. The thicker JPCP sections 

showed higher IRI values (on average) when PCC elastic modulus was < 27,000 MPa. 

The thinner JPCP sections showed an opposite trend. Slightly higher IRI values (on 

average) were exhibited by those sections with PCC elastic modulus 2 27,000 MPa, A 

secondary investigation of the PCP data in the four individual climatic regions 

revealed trends for the sections in the wet-freeze region only. The trends in this region 

followed the trends described above for the data in all climatic regions. The thinner 

JPCP sections with PCC elastic modulus 2 27,000 MPa and the thicker JFCP sections 

with PCC elastic modulus < 27,000 MPa showed higher IRI values (on average). Figure 

81 contains plots showing the influence of slab thickness and PCC elastic modulus on 

the IRI of JPCP LTPP sections (all climatic regions are included). 

Effect of Outside Shoulder Type 

The influence of two outside shoulder types (PCC and non-PCC) on the IRI of JPCP, 

JRCP, and CRCP was investigated for the sections already divided into categories of 

slab thickness (< 250 mm and 2 250 mm). Linear trends through the JPCP data showed 

higher IRI values for thicker sections (2 250 rnrn) with non-PCC shoulders, whereas 
Sections with Thickness < 250 mm Sections with Thickness r= 250 mm 

Figure 80. Plots showing the influence of slab thickness and base elastic modulus on 

the IRI of JPCP sections (all climatic regions included). 

Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

0 r 27,600 MPa 

-L~near (c 27,600 MP4 

ESAL's (thousands) 

Linear (c 27,600 MPa) 

' ' - Linear b- 27.600 MPa) 

ESAL's (thousands) 

Figure 81. Plots showing the influence of slab thickness and PCC elastic modulus on 

the IRI of JPCP LTPP sections (all climatic regions are included). 
there appeared to be little or no influence of shoulder type on the IRI of thinner JPCP 

sections (c 250 mm). No clear trends were observed for the data within each individual 

climatic region. Figure 82 contains plots showing the influence of slab thickness and 

outer shoulder type on the IRI of JPCP sections (data from all climatic regions were 

included). 

Linear trends through the JRCP data representing the thinner sections (< 250 mm) 

showed the highest IRI values for sections with PCC shoulders. Linear trends through 

the JRCP data representing the thicker sections (2 250 mm) showed the opposite trend, 

with the highest IN values coming from sections with non-PCC shoulders, However, it 

is important to note that very few JRCP sections with thickness > 250 mm had PCC 

shoulders. Again, no clear trends were observed for the data within each individual 

climatic region because of the lack of sufficient data. Figure 83 contain plots showing 

the influence of slab thickness and outer shoulder type on the IRI of JRCP sections (data 

from all climatic regions were included). 

Linear trends through the CRCP data representing the thinner sections (< 250 mm) 

showed a general trend of the highest IRI values coming from sections with non-PCC 

shoulders. No clear trends were developed for the data representing the thicker CRCP 

sections; however, a visual inspection of the CRCP data showed that the highest IRI 

values were from sections with PCC shoulders. Insufficient data were available to 

identify trends for the CRCP sections in the individual climatic regions. Figure 84 

contains plots showing the influence of slab thickness and outer shoulder type on the 

IRI of CRCP sections (data were included from all climatic regions). 

Effect of Percentage Steel Content 

An investigation of the influence of steel percentage on IN was conducted for the 

JRCP and CRCP LTPP sections. The analysis revealed that steel percentage had little or 

no significant influence on IRI when data from all climatic regions were grouped 

together. 

Influence of Construction Practices on Roughness 

The main Level 2 construction practice that influences roughness directly is surface 

texturing; however, other construction practices that could potentially influence 

roughness, such as paver type, curing method, ~oint forming method, steel placement 

method, and dowel placement method were investigated at the preliminary stage. 
Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

Nm PCC Shoulder 

"""Linear (PCC Shwlder) 

-. 

Non PCC Shudder 

Linear (PCC Shoulder) 

0 Linear (Nm PCC Shoulda) 

I 

C 

I 

I # 

I > 

0 1 OrOOO 20,000 30,000 40,000 0 10tOfM 20,OMf 3OfO00 40t000 

BSAL's (thousands) HSAL's (thousands) 

Figure 82. Plots showing the influence of slab thickness and outer shoulder type on the 

IRI of JPCP sections (all climatic regions included). 

Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

Non PCC Shoulder 

-Linear (PCC Shouldar) 

0 NonPCCShoul& 

-Lima (PCC Shoulder) 

0 0 

0 

0 

0 i # 

0 10,000 20,000 30,000 0 

40,000 0 10,000 20,000 30,000 40,000 

ESAL's (thousands) ESAL's (thousands) 

Figure 83. Plots showing the influence of slab thickness and outer shoulder type on the 

IRI of JRCP sections (all climatic regions included). 
Sections with Thickness < 250 mm Sections with Thickness >= 250 rnm 

Ncm PCC Shoulder 

-Linear (PCC Shoulder) 

Linw (Non PCC Sbuider) 

ESAL's (thousands) ESAL's (thousands) 

5 5 

Figure 84. Plots showing the influence of slab thickness and outer shoulder type on 

the IRI of CRCP sections (all climatic regions included). 

4 4 

'3 3 

8 

- 6 

I 

8 3-- 

Effect of Paver Type 

hbn PCC SbUlda! 

-- Linear (PCC Shoulder) 

- L~near (Ncm PCC Shwldei) 

The infiuence of paver type on the IRI of JPCP, JRCP, and CRCP sections was 

investigated for the data sets already divided by slab thickness category (< 250 mm and 

7 

> 250 mm). For all three pavement types, two different paver types were 

compared-slip form and side form. 

E n 

%, & + 

g 2 

2 VP~*,. , . . .. . . . , 

1 -0-0- eon p- -0. . - , , _ , _ 

0 1 I 

0 10,000 20,000 30,000 40,000 0 10,000 30,000 40,000 

Paver type appeared to have a significant effect on the IRI of JPCP sections, with 

higher values appearing for those sections constructed with a slip form paver. This 

trend held true for JPCP sections in both slab thickness categories. The influence on the 

JRCP data was not as clear. Paver type appeared to have no significant influence on the 

JRCP data with slab thickness < 250 mm; however, for the thicker sections (2 250 mm) 

those constructed using a side form paver exhibited higher IRI values on average (note: 

this trend is based on very few data points). Finally, no clear trends were observed for 

the CRCP data in either thickness category. Insufficient data made it impossible to 

investigate the specific effects of climatic region on these data sets. Figure 85 contains 

plots showing the influence of paver type and slab thickness on the IRI of JPCP, JRCP, 

and CRCP LTPP sections, respectively (data from all climatic regions are included). 
Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESAL's (thousands) ESAL's (thousands) 

Figure 85. Plots showing the influence of paver type and slab thickness on the IRI of 

JPCP LTPP sections (all climatic regions included). 

Effect of Curing Method 

The influence of curing method on the IRI of JPCP, JRCP, and CRCP sections was 

investigated separately for each pavement type. Four different curing methods were 

observed for the different pavement types-burlap poly, curing compound, burlap 

blanket, and white poly sheeting. The JPCP sections with slab thickness c 250 mm 

showed that higher IRI values (on average) developed for those sections cured with 

burlap poly when compared with sections cured with a curing compound or white 

poly sheeting. The influence of curing compound on the IRI of thicker JPCP sections 

250 mm) was not as clear. An evaluation of the linear trends showed that those sections 

cured with white poly sheeting exhibited higher IRI values (on average) than those 

cured with a curing compound. Figure 86 contains plots showing the influence of 

curing method and slab thickness on the IRI of JPCP LTPP sections. 

For JRCP pavements, comparisons could only be made for those sections with slab 

thickness < 250 mm (all JRCP sections with slab thickness 2 250 mm were cured with 

curing com.poound only). No clear trends identifying the influence of curing method on 

IRI were determined. All of the CRCP sections were cured using a curing compound; 

therefore, no comparisons could be made for those data sets. Also, due to insufficient 

data, no conclusions could be drawn regarding the effects of climatic region. 
Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

+ BurlepPdy 

0 Curing Conpaund 

)r: Whii Pdy Sheeting 

LIW (Burlap Poly) - - LIW (Curing Ccnpound) 

- ~L~near (Whids Pdy Sheding) 

C 

i 

1 I o Curing Conpaund 1 I I x WhasPdy Sheeting I Bud* Blanket I - - - Linear lCurira Eonmound\ I I 

0 

l0,OOa 20,000 30,000 40,000 

00 

0 0 10,000 20,000 30,000 40,000 

ESAL1s (thousands) ESAL1s (thousands) 

Figure 86. Plots showing the influence of curing method and slab thickness on the IRI 

of JPCP LTPP sections (all climatic regions included). 

Efect of Texture Method 

The influence of texture method on the IRI of JPCP, JRCP, and CRCP was 

investigated for the LTPP data already divided by slab thickness. Six different texture 

methods were observed for the available LTPP data-astro turf, broom, burlap drag, 

tine, tine and astro turf, and grooved float. For the JPCP data in both slab thickness 

categories, the highest IRI values appeared to come from those sections textured with 

astro turf, and tine and astro turf. 

Those sections textured with tines, a broom, or a burlap drag consistently showed 

lower IRI values for JPCP pavements (on average). Figure 87 contains plots showing 

the influence of texture method and slab thickness on the IRI of JPCP LTPP sections 

(data from all climatic regions are included). For the JRCP sections with slab thickness 

< 250 mm, the sections textured using astro turf and tines and astro turf again showed 

the highest IN values (on average), 

There did not appear to be much significant difference between the other methods, 

although those sections textured with a grooved float showed a linear trend with a 

large decreasing slope. It was difficult to determine any trends for the JRCP data with 

slab thickness 2 250 mm. The sections textured with a burlap drag appeared to show 

the largest IRI values (on average), and there appeared to be no significant difference 
Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

5 

e Astro Turf 0 Brm 

' t e Tine & Astm Turf 

e Astro Turf 0 Brmm 1 I 

,y Burlap Drag x Tine 

X a*. 

o! I 1 o! I 

0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESAL's (thousands) ESALt (thousands) 

Figure 87. Plots showing the influence of texture method and slab thickness on the IRI 

of JPCP LTPP sections (all climatic regions included). 

between those sections textured with tines or astro turf. Figure 88 contains plots 

showing the influence of texture method and slab thickness on the IRI of JRCP LTPP 

sections (data from all climatic regions are included). For the CRCP sections with slab 

thickness < 250 mm, the sections textured with astro turf and tines and astro turf again 

showed the greatest linear trends in the IRI values (on average). There did not appear 

to be much significant difference between the other methods, although those sections 

textured with a burlap drag showed the lowest IRI values (on average). For those JRCP 

sections with slab thickness 2 250 mm, those textured using the burlap drag showed the 

greatest linear trend slope. The lowest observed IRI values came from those sections 

textured with a broom. Figure 89 contains plots showing the influence of texture 

method and slab thickness on the IRI of CRCP LTPP sections (data from all climatic 

regions are included). The influence of climatic region was not investigated because of 

a lack of data in most of the data sets. 

Effect of Transverse Joint Forming Method 

The influence of transverse joint forming method on IRI was investigated for the 

JPCP and JRCP data already divided by slab thickness. Three different joint forming 

methods were observed for the available LTPP data-plastic insert, metal insert, and 

sawed. For the JPCP sections in both thickness categories, a comparison of plastic 

inserts to sawed joints showed that higher IRI values developed (on average) for those 
Sections with Thickness < W3 rnrn Sections with Thickness >= 250 mrn 

5 

+Asvo Turl 0 BKD~ 

't .Tine & Astm Turf -+GI& Float 

0 ! 1 

0 10,000 20,000 40,000 

ESAL's (thousands) 

*Astro Turf X Burlap Drag XTine 

0 10,000 20,000 30,000 40,000 

ESAL's (thousands) 

Figure 88. Plots showing the influence of texture method and slab thickness on the IRI: 

of JRCP LTPP sections (all climatic regions included). 

Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

+Astro Turf 

Tine h Astm Turf + Granred Fkat 

7 

0 61~ X Bud41 Drag *Tine 

m 

x 

bla 

t I 

ESAL's (thousands) ESAL's (thousands) 

Figure 89. Plots showing the influence of texture method and slab thickness on the IRI 

of CRCP LTPP sections (all climatic regions included). 
sections constructed with sawed joints. Figure 90 contains plots showing the influence 

of transverse joint forming method and slab thickness on the IRI of JPCP LTPP sections. 

Comparisons could only be made for the thinner JRCP sections (< 250 mm) because 

of insufficient data. The observed linear trends showed that those JRCP sections 

constructed with plastic inserts developed slightly higher IRI values (on average) than 

those sections with sawed joints, Figure 91 contains plots showing the influence of 

transverse joint forming method and slab thickness on the IRI of JRCP LTPP sections 

(data from all climatic regions were included). Secondary analyses investigating the 

influence of different climatic regions did not show any clear trends because of a lack of 

sufficient data, 

Effect of Steel Placement Method 

The influence of steel placement method on IRI was investigated for the JRCP and 

CRCP data already divided by slab thickness, Three different steel placement methods 

were observed for the available LTPP data-between layers, chairs, and mechanically 

inserted. Consistent trends were observed for the JRCP sections in both thickness 

categories. Those JRCP sections constructed using chairs exhibited the highest IRI 

values (on average), followed by those sections using mechanically inserted steel, with 

the sections constructed by placing the steel between layers showing the lowest values 

of IRI (on average). Figure 92 contains plots showing the influence of steel placement 

method and slab thickness on the IRI of JRCP LTPP sections. 

The CRCP data did not show any clear trends identifying the influence sf steel 

placement method on IRI. For both thickness categories, their appeared to be no 

significant difference between the IRI of those sections constructed with chairs and 

mechanical inserters. Even though the linear trend representing the thinner CRCP 

sections with a steel placement method of between layers shows a steep slope, it is 

important to note that this line is based on very few data points. Figure 93 contains 

plots showing the influence of steel placement method and slab thickness on the IN of 

CRCP LTPP sections (data from all climatic regions were included). Secondary analyses 

investigating the influence of different climatic regions did not show any clear trends 

because of a lack of sufficient data. 

Effect of Dowel Placement Method 

The influence of dowel placement method on the development of IRI for JPCP and 

JRCP pavements was investigated for the data sets already divided by slab thickness. 

Two different dowel placement methods were observed for the available LTPP 

data-baskets and inserted using a mechanical dowel bar inserter. 
Sections with Thickness c 250 mm Sections with Thickness >= 250 mm 

Linear (Plastb Insert) 

0 

0 

0 

C 

I 

+ Plastio lnsotl 

Llnear (Plastic Inssrt) 

0 0 I I I 

0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESAL's (thousands) ESAL's (thousands) 

Figure 90. Plots showing the influence of transverse joint forming method and slab 

thickness on the IN of JPCP LTPP sections (all climatic regions included). 

Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

ESAL's (thousands) ESAL's (thousands) 

Figure 91. Plots showing the influence of transverse joint forming method and slab 

thickness on the IRI of JRCP LTPP sections (all climatic regions included). 

5 - 

4 

3 $ 3-- 

0 10,000 20,000 30,000 ‘QOOO 0 l0,OOO 20,000 30,000 4'J,m 

-- 

0 &pO 

40 

0 

5 -. 

4 -- 

3 1 .-- 

a 

a 

1 

0 

"0 000 -%.----m-- 

0 1 

- 0 ~--- Llnear (Sawed) 

00 

0 

b,eo0 .-- g.--s 

.mm- 0 oll*- 0 82-;08..m.- 0 04- woo 

-- 

I I 
Sections with Thickness < 250 mm Sections with Thickness >= 250 mm 

Between Layers 

-Linear (Between Layers) - ' Linear (Cheirs) 

-Linear (Mschanically) 

0 -- O 80 

0 

-Linear (Belween Layers) I I linear (Chairs) 

- -linear (Mechanicalty) 

ESALts (thousands) ESAL's (thousands) 

Figure 92. Plots showing the influence of steel placement method and slab thickness on 

the IRI of JRCP LTPP sections (all climatic regions included). 

Sections with Thickness < 250 rnm 

0 10,oOO 20,000 30,000 40,000 

ESAL's (thousands) 

5

Sections with Thickness >= 250 mm 

4 - - - Linear (Chairs) 

- -Cmr (Mechanicaly) 't I 

ESAL1s (thousands) 

-- 

Figure 93. Plots showing the influence of steel placement method and slab thickness on 

the IRI of CRCP LTPP sections (all climatic regions included). 

4 Between Layers 

0 Chairs 

x Mechanically 

liar (Between Lnyers) - - - Linear (Chairs) 

- -Linear (Mechanically) 
For the JPCP data, comparisons could only be made for the thicker sections 250 mm), 

since only baskets were used for the thinner sections. The data representing the thicker 

JPCP sections showed that higher IRI values were exhibited (on average) by those 

sections constructed using dowel baskets. 

Figure 94 contains plots showing the influence of dowel placement method on the 

IRI of JPCP LTPP sections (data from all climatic regions were included). An analysis of 

the JRCP data showed that the thinner sections constructed using a dowel bar inserter 

exhibited higher IRI values when compared with those sections constructed using 

dowel baskets. The data representing the thicker JRCP sections did not appear to show 

any significant trends. Figure 95 contains plots showing the influence of dowel 

placement method on the IRI of JRCP LTPP sections (data from all climatic regions 

were included). 

Canonical Discriminant Analysis for JPCP Roughness 

The LTPP database for JPCP was categorized into pavements performing above 

expectation and others (expected and below expected) using the performance 

classification procedure outlined in chapter 3 of this report. A single canonical variable, 

CANJPCI, was adequate for separating the PCP according to roughness. The final 

form of the canonical function for estimating the value of CANJPCl was as 

follows :(5,6,7,8) 

where 

WTDYS = 

MEANT = 

D32 7 - 

DO - 

TEXT = 

STYPE = 

hrcc - 

Ercc - 

hb - 

J 7 - 

BASTYP = 

DRAIN = 

annual number of wet days 

annual mean temperature, *C 

annual number of days with temperature above 32 OC 

annual number of days with temperature below 0 OC 

concrete texture method, 1 = tine, 0 = others 

shoulder type, 1 = rigid, 0 = other 

PCC slab thickness, mrn 

PCC elastic modulus, kPa 

base thickness, mm 

load transfer coefficient 

base type, 1= stabilized, 0 = unstabilized 

1 = presence of drain 
SUBTYP = subgrade type, 1 = coarse, 0 = fine material 

Sections with Thickness < 250 mm Sections with Thickness >*= 250 mm 

Lir~ear (Baskets) 

bear (Insenor) 

-. 

J 4 Baskets Linear (Baskets) 

-- C 

Figure 94. Plots showing the influence of dowel placement method on the IRI of JPCP 

LTPP sections (all climatic regions included). 

a$*. 

7 

41 

Y 

d 

Sections with Thickness < 250 mm 

5 

B 

2 

Linear (Baskets) 

- - - Linear (Inserter) 

I 

* C # 

. 8. P # 0 6 C 

- , @.* 

. , 

1 

Sections with Thickness >= 250 mm 

E. 2 

Linear (Baskets) 

- - - Linear (lnsener] 

& 

0 10,000 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESAL1s (thousands) ESAL's (thousands) 

Figure 95. Plots showing the influence of dowel placement method on the IRI of JRCP 

LTPP sections (all climatic regions included). 

it 8 2 

4 

1 

0 

9! 4 

1 

0 

0 l0,oOO 20,000 30,000 40,000 0 10,000 20,000 30,000 40,000 

ESALTs (thousands) ESAL's (thousaruds) 
The mean values of CANJPC1 for pavements performing above expectation and others 

are given in table 15. A plot of CANJPCl versus CANJPCl for each observation of the 

data used in the analysis is shown in figure 96. 

The plot shows that pavements performing above expectation are located in the 

negative region of the plot (CANJPC1 < 0), while the pavements in the others category 

are in the positive region, (CANJPC1.0). This is in agreement with the mean values of 

the canonical variables for the different categories presented in table 15. Classifying 

pavement performance according to the canonical variable CANJPC1 is summarized as 

follows: 

Pavement sections performing above expectation were generally located in the 

area CANJPCl is less than -0.5. 

Pavement sections performing below expectation or as expected were located in 

the area where CANJPCl is greater than 0.5. 

Summarv of Canonical Discriminant Anal sis for IRI Rowhness 

The canonical function shows that adverse climatic conditions generally contribute 

to more roughness and, therefore, worse performance. An increase in the values of the 

annual number of wet days, mean temperature, annual number of days with 

temperature above 32 "C, and annual number of days with temperature below 0 "C 

increases the value of CANJPC1. Positive CANJPCl values imply expected or below 

expected performance. 

A review of the design variables in the canonical function shows that JPCP designed 

with a rigid shoulder, a stabilized base, constructed over a coarse subgrade material, 

and with high PCC modulus reduced the value of CANJPCl (i.e., showed better 

performance). Also, providing drainage facilities and reducing the load transfer 

coefficient, J, enhances performance. The trends are in agreement with mechanistic 

analysis and results of past studies. 

Analysis of Variance for JPCP Roughness 

A comprehensive ANOVA was performed to determine the significance of each 

design feature and site conditions that influence the occurrence of roughness for JPCP. 

Table 16 is a summary of the significant variables that influence roughness according to 

the ANOVA results and Duncan's multiple range test for comparison of means. Table 

17 also summarizes the variables that, though not significant, show an effect on 

roughness that could be significant. The effect of the design features and site conditions 

influencing performance are discussed in the next few sections. 
CAN JPCl 

A OTHERS 

rn ABOVE 

EXPECTED 

Figure 96. A plot of CANJPCl versus CANJPC1 for each observation of the data, 
Table 15, Class means for canonical variable CANJPC1. 

Category CANJPCl 

Others 

-0.68 

Table 16. Summary of analysis of variance results for roughness. 

Significant 

Difference 

Yes 

Yes 

No 

No 

Yes 

No 

No 

Yes 

different. 

Duncan 

Class* 

A 

B 

C 

A 

B 

C 

A 

A 

A 

A 

A 

B 

A 

A 

A 

A 

A 

B 

B 

are not significantly 

Mean IRI 

mlkm 

1.56 

2.07 

1.81 

1.94 

1.58 

1.78 

1.73 

1.86 

1.87 

1.70 

1.72 

1.95 

1.70 

1.86 

1.75 

1.89 

2.05 

1.62 

1.70 

the same letter 

Variable 

Precipitation, m 

Base elastic 

modulus, MPa 

I 

Joint coefficient, J 

Dowel diameter 

Concrete texture 

Shoulder type 

PCC slab 

thickness' 

Annual mean 

temperature, "C 

* Duncan's multiple 

Classification 

less than 0.6 

0.6 to 1.0 

greater than 1.0 

less than 207 

690 to 6900 

greater than 6900 

less than 3.5 

greater than 3.5 

no dowels 

dowels 

tine 

others 

rigid 

others 

less than 250 

greater than 250 

less than 10 

10 to 15.5 

greater than 15.5 

range test; those with 
Table 16. Summary of analysis of variance results for roughness (continued). 

* Duncan's multiple range test; those with the same letter are not significantly different. 

Influence of Site Conditions 

Effect of Precipitation 

The results presented in table I1 show that pavements located in areas with an 

annual precipitation of less than 0.635 mm had the least amount of roughness (1.56 

m/km), whereas pavements located in regions with precipitation greater than 0.635 

experienced a roughness level of greater than 1.81 m/km, This shows that precipitation 

has a significant effect on roughness, and pavement designers should take the effect of 
excess precipitation into consideration by providing adequate drainage facilities. The 

variable Dry, which indicates dry and wet climates according to the LTPP database and 

definitions, also shows that pavements located in wet areas experience more roughness 

than those in dry climates. This agrees with the results for precipitation. Precipitation 

generally increases the potential for moisture-related damage, such as pumping, 

stripping of AC stabilized layers, and lowering the pavement foundation material 

modulus. All of these increase the potential for distress and, hence, roughness. 

Effect of Mean Temperature 

Pavements located in warm climates generally have higher mean temperatures than 

those located in cold climatic areas and subjected to cooler or freezing temperatures. 

Both extremes are harmful to the pavement structure and result in increased distresses 

and roughness. The ANOVA results show that pavements located in cold climates 

(mean temperature less than 10 "C) experience significantly less roughness than those in 

warmer climates. Pavements in the above 15.5 "C mean temperature range experience 

more roughness, even though this was not significantly higher than those in the 10 to 

15.5 "C range. The results show that extreme climates and temperature adversely affect 

performance and should be considered in design. 

Efect of Base Modulus 

Table 11 shows three levels of roughness for the different base types in the LTPP 

database. Pavements with unstabilized granular bases (modulus c 345 MPa) 

experienced the highest level of roughness. This is due to the fact that such bases result 

in weaker foundations for the pavement and increase pavement deflections and 

stresses. Pavements with lean concrete bases or cement-stabilized bases were the group 

with the next most roughness (1.57 m/km). 

The reason for this may be similar to that observed for transverse cracking. PCC 

slabs on a rigid foundation that are subjected to very high temperature gradients curl 

up, resulting in voids between the slab and the base material and high stresses within 

the PCC slab. This increases the likelihood of cracking. The bases with modulus values 

between 690 and 6900 MPa (AC-treated bases) had the least roughness. This is because 

the AC-stabilized layer provides a uniform base support, even when the PCC slab is 

curled. The trend is similar to that observed for transverse cracking. 
Effect of Dowels and Load Transfer Coefficient 

The ANOVA results show clearly that pavements with dowels perforrr~ better than 

those without. Dowels provide load transfer across the joints of adjacent slabs, which 

reduces deflections and faulting at the joints and results in decreased roughness. The 

AASHTO equation for pavement design includes the J coefficient, which is an 

adjustment factor for the load transfer conditions of a joint and also for the drainage 

conditions of the pavement. An increase in J reduces the number of load cycles to 

failure of a pavement. Pavements with lower J coefficient values are therefore expected 

to perform better and last longer. The ANOVA results confirm this trend. Pavements 

with lower J values experience less roughness (1.73 m/km) than those wit11 higher J 

values (1.86 m/km). 

Effect of Concrete Texture Method 

Past studies have shown that the initial roughness of a pavement has an effect on 

the rate of progression and the final roughness of a pavement. Pavements built rough 

generally stay rougher. The methods of finishing a pavement's surface after 

construction ultimately affect the final smoothness of the pavement. The ANOVA 

results show that JPCP with tined surfaces generally are smoother than those with other 

kinds of finishing. 

Effect of Shoulder Type 

Jointed concrete pavements with PCC tied shoulders tend to experience less distress 

and, hence, roughness. This is because there is some load transfer between the PCC 

pavement and the shoulder. It reduces the critical bending stresses and deflections at 

the midsection of the slabs when subjected to wheel loads. The ANOVA results show 

that pavements with PCC shoulders had an average IRI of 1.7 m/km, whereas 

pavements with other kinds of shoulders had an average IRI value of 1.86 m/km. Even 

though the difference between the two groups was not significant statistically, the 

results show that tied PCC shoulders decrease the amount of roughness a pavement 

experiences. 

Effect of Drains and Drainage Coefzcient 

The ANOVA analysis shows that the provision of drains to a pavement generally 

reduces roughness. This can be explained by the fact that adequate drainage reduces 

the amount of water that infiltrates into the pavement structure, thereby reducing the 

potential for pumping and faulting and weakening of the underlying pavement 

materials. Providing a base material that enhances drainage also reduces distresses 

such as faulting. Accordingly, the LTPP data show that an increase in the pavement 
base drainage coefficient reduces roughness. Pavements with a drainage coefficient less 

than 1 had a mean IRI value of 1.97 m/km, whereas those with a drainage coefficient of 

greater than 1 had a mean IRI value of 1.64 m/km. 

Effect of PCC Thickness 

The analysis shows that pavements with thicker PCC slabs (> 250 mrn) experienced 

more roughness (1.69 m/km) than those with thinner PCC slabs (1.75 m/km). This is 

because thicker pavements are generally built rougher than thinner sections (because of 

constructional difficulties) and they remain rougher throughout the pavement's life. 

Efhect of Pavement Strength and Foundation Support 

The variables that determine the support conditions of PCC pavements and 

pavement strength in the LTPP database are PCC modulus, modulus of subgrade 

reaction, base thickness, and subgrade type. Increases in magnitude of all of these 

variables and construction of pavements on coarse subgrade materials increase 

pavement strength. The ANOVA results show that strengthening a pavement decreases 

roughness. This is a reasonable result, even though for some distresses increasing base 

and subgrade stiffness could have a detrimental effect on the pavement. Generally, 

stronger pavements experience less distress and, therefore, roughness. 

Influence of Construction Practices 

Effect of Concrete Texture Method 

The ANOVA analysis showed that, generally, pavements finished by a grooved 

float or astroturf were smoother that those finished by other finishing methods. 

Summarv of Site Conditions, Desim Features. and Construction Practices that Influence 

Using univariate analysis, frequency plots, canonical discriminant analysis, and 

ANOVA, the design-, site-, and construction-related variables that inff uence the 

occurrence and severity of roughness in JPCP have been analyzed. According to the 

results of the analysis, pavements with stiffer foundations tend to have less roughness 

than those constructed over weaker foundations. Also, the presence of dowels resulted 

in less roughness because of reduced faulting; however, thicker PCC slabs and bases 

resulted in pavements with more roughness. The analysis also showed that pavements 

located in climates with high precipitation and with extreme temperatures (hot or cold) 

performed worse than those located in milder climates. These observations and the 

canonical function developed can be used in design to enhance pavement performance. 
Canonical Discriminant Analysis for JRCP Roughness 

The LTPP database for JRCP was categorized into pavements performing above 

expectation, as expected, and below expectation using the procedures outlined in 

chapter 3 of this report. Two canonical variables, CANJRCl and CANJRC2, were 

developed and used for classifying performance using design and site variables. The 

final form of the canonical functions for estimating CANJRCl and CANJRC2 were as 

follows:(5, 6,7,8) 

where 

PRECIP 

D32 

TEXT 

STYPE 

DSC 

DTS 

hb 

C d 

KVAL 

annual precipitation, mm 

annual number of days with temperature above 32 OC 

concrete texture method, 1 = tine, 0 = other 

shoulder type, 1 = rigid, 0 = other 

design steel content 

design steel spacing, mm 

base thickness, mm 

drainage coefficient 

modulus of subgrade reaction, kPa/mm 

The mean values of CANJRCl and CANJRC2 for pavements performing atlove 

expectation, as expected, and below expectation are given in table 17. A plot of 

CANJRC1 versus CANJRC2 for each observation of the LTPP data used in the analysis 

is shown in figure 97. The plot shows that the canonical variable (CANJRC:L) accounted 

for most of the discrimination between pavements performing above expectation and 

the other performance categories. Performance classification based on the canonical 

variables CANJRC1 and CANJRC2 are as follows: 

m Pavement sections performing above expectation were generally lociated in the 

area where CANJRC1 is greater than 0. 

Pavements with performance classification as expected or below expectation 

generally had CANJRC1 values less than -1.0.