Review of Data in Construction Management System (CMS) … · Specifications for Superpave and...

A cooperative transportation research program betweenKansas Department of Transportation,Kansas State University Transportation Center, andThe University of Kansas

Report No. K-TRAN: KSU-09-7 P1▪ FINAL REPORT▪ December 2012

Review of Data in Construction Management System (CMS) and Quality Control and Quality Assurance (QC/QA) Databases to Improve Current Specifications for Superpave and Concrete Pavements in Kansas: Part 1

Daba Gedafa, Ph.D., P.E.Mustaque Hossain, Ph.D., P.E.Lon Ingram, P.E.

Kansas State University Transportation Center

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This page intentionally left blank.

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Form DOT F 1700.7 (8-72)

1 Report No.

K-TRAN: KSU-09-7 Part 1

2 Government Accession No.

3 Recipient Catalog No.

4 Title and Subtitle

Review of Data in Construction Management System (CMS) and Quality Control and

Quality Assurance (QC/QA) Databases to Improve Current Specifications for

Superpave and Concrete Pavements in Kansas: Part 1

5 Report Date

December 2012

6 Performing Organization Code

7 Author(s)

Daba Gedafa, Ph.D., P.E.; Mustaque Hossain, Ph.D., P.E.; and Lon Ingram, P.E.

8 Performing Organization Report No.

9 Performing Organization Name and Address

Department of Civil Engineering


2118 Fiedler Hall

Manhattan, Kansas 66506

10 Work Unit No. (TRAIS)

11 Contract or Grant No.

C1802

12 Sponsoring Agency Name and Address

Kansas Department of Transportation

Bureau of Materials and Research

700 SW Harrison Street

Topeka, Kansas 66603-3745

13 Type of Report and Period Covered

Final Report

February 2009–August 2011

14 Sponsoring Agency Code

RE-0492-01

15 Supplementary Notes

For more information write to address in block 9. See also K-TRAN: KSU-09-7 Part 2

16 Abstract

Statistical specifications for highway construction are usually part of a statistical quality control

process. These specifications provide the means to measure the important quality control attributes and

ensure their compliance. The pay adjustments, part of these specifications, reflect the amount of deduction or

bonus and the optimized risk distributed between the owner and the contractor. The Kansas Department of

Transportation (KDOT) has built a comprehensive database of as-constructed properties of materials for

Superpave pavements from the tests required as part of the Quality Control/Quality Assurance (QC/QA)

program. Currently, KDOT pays incentives/disincentives for air voids and in-place density for Superpave

pavements and thickness and strength for PCC pavements. A practical performance model and a composite

index that include air voids, in-place density, asphalt content, and voids in mineral aggregate for Superpave

pavements and thickness and strength for PCC pavements, respectively are needed to reflect the factors that

affect their performance. The main objectives of this study were to investigate the effect of levels of

significance and lot size, and to develop practical performance models and composite index for Superpave

and PCC pavements in Kansas. Thirty-five Superpave pavements and 13 PCC projects from six

administrative districts of KDOT were selected for this study. Lot-wise comparison showed that QC/QA

means are significantly different in most cases. The number of cases with a significant difference in means

increases with an increase in significance level. Practical performance models and composite index values

from multiple quality characteristics have been proposed as integral parts of performance-related

specifications (PRS) for Superpave and PCC pavements in Kansas. 17 Key Words

QC/QA, Quality Control, Quality Assurance, Superpave,

PCCP, Portland Cement Concrete Pavement

18 Distribution Statement

No restrictions. This document is available to the public

through the National Technical Information Service

www.ntis.gov.

19 Security Classification (of

this report)

Unclassified

20 Security Classification

(of this page) Unclassified

21 No. of pages

132

22 Price

http://www.ntis.gov/

iii

Review of Data in Construction Management System

(CMS) and Quality Control and Quality Assurance

(QC/QA) Databases to Improve Current Specifications for

Superpave and Concrete Pavements in Kansas: Part 1

Final Report

Prepared by

Daba Gedafa, Ph.D., P.E.

Mustaque Hossain, Ph.D., P.E.

Lon Ingram, P.E.


A Report on Research Sponsored by

THE KANSAS DEPARTMENT OF TRANSPORTATION

TOPEKA, KANSAS

and

KANSAS STATE UNIVERSITY TRANSPORTATION CENTER

MANHATTAN, KANSAS

December2012

© Copyright 2012, Kansas Department of Transportation

iv

PREFACE

The Kansas Department of Transportation’s (KDOT) Kansas Transportation Research and New-

Developments (K-TRAN) Research Program funded this research project. It is an ongoing,

cooperative and comprehensive research program addressing transportation needs of the state of

Kansas utilizing academic and research resources from KDOT, Kansas State University and the

University of Kansas. Transportation professionals in KDOT and the universities jointly develop

the projects included in the research program.

NOTICE

The authors and the state of Kansas do not endorse products or manufacturers. Trade and

manufacturers names appear herein solely because they are considered essential to the object of

this report.

This information is available in alternative accessible formats. To obtain an alternative format,

contact the Office of Transportation Information, Kansas Department of Transportation, 700 SW

Harrison, Topeka, Kansas 66603-3754 or phone (785) 296-3585 (Voice) (TDD).

DISCLAIMER

The contents of this report reflect the views of the authors who are responsible for the facts and

accuracy of the data presented herein. The contents do not necessarily reflect the views or the

policies of the state of Kansas. This report does not constitute a standard, specification or

regulation.

v

Abstract

Statistical specifications for highway construction are usually part of a statistical quality

control process. These specifications provide the means to measure the important quality control

attributes and ensure their compliance. The pay adjustments, part of these specifications, reflect

the amount of deduction or bonus and the optimized risk distributed between the owner and the

contractor. The Kansas Department of Transportation (KDOT) has built a comprehensive

database of as-constructed properties of materials for Superpave pavements from the tests

required as part of the Quality Control/Quality Assurance (QC/QA) program. Currently, KDOT

pays incentives/disincentives for air voids and in-place density for Superpave pavements and

thickness and strength for PCC pavements. A practical performance model and a composite

index that include air voids, in-place density, asphalt content, and voids in mineral aggregate for

Superpave pavements and thickness and strength for PCC pavements, respectively are needed to

reflect the factors that affect their performance. The main objectives of this study were to

investigate the effect of levels of significance and lot size, and to develop practical performance

models and composite index for Superpave and PCC pavements in Kansas. Thirty-five

Superpave pavements and 13 PCC projects from six administrative districts of KDOT were

selected for this study. Lot-wise comparison showed that QC/QA means are significantly

different in most cases. The number of cases with a significant difference in means increases

with an increase in significance level. Practical performance models and composite index values

from multiple quality characteristics have been proposed as integral parts of performance-related

specifications (PRS) for Superpave and PCC pavements in Kansas.

vi

Acknowledgements

The authors would like to acknowledge the Kansas Department of Transportation for

sponsoring this study under its Kansas Transportation and New Developments (K-TRAN)

Program. Special thanks are due to Mr. Rick Barezinsky, Mr. Stephen Morris, and Mr. Bill

Parcells of KDOT for providing QC/QA data for this study.

vii

Table of Contents

Abstract ........................................................................................................................................... v

Acknowledgements ........................................................................................................................ vi

Table of Contents .......................................................................................................................... vii

List of Tables .................................................................................................................................. x

List of Figures ............................................................................................................................... xii

Chapter 1: Introduction ................................................................................................................... 1

1.1 General ................................................................................................................................ 1

1.2 Problem Statement .............................................................................................................. 2

1.3 Objectives of the Study ....................................................................................................... 2

1.4 Organization of the Report.................................................................................................. 3

Chapter 2: Data Analysis ................................................................................................................ 4

2.1 General ................................................................................................................................ 4

2.2 Project Selection ................................................................................................................. 4

2.3 Data Collection ................................................................................................................... 4

2.3.1 Superpave Pavements .................................................................................... 4

2.3.1.1 Air Voids .................................................................................................. 6

2.3.1.2 Density ..................................................................................................... 7

2.3.1.3 Smoothness .............................................................................................. 7

2.3.2 PCC ................................................................................................................ 7

2.4 Control Charts ..................................................................................................................... 8

2.4.1 Superpave Pavements .................................................................................... 8

2.4.1.1 QC Air Voids ........................................................................................... 8

2.4.1.2 QC Density............................................................................................. 12

2.4.1.3 QA Density ............................................................................................ 15

2.4.2 PCC Pavements ............................................................................................ 18

2.4.2.1 QC Strength ........................................................................................... 19

2.4.2.2 QC Thickness ......................................................................................... 21

2.5 Comparison of QC and QA Density ................................................................................... 24

2.5.1 Mean Density Comparison .......................................................................... 25

2.5.2 Maximum Density Comparison ................................................................... 28

2.5.3 Minimum Density Comparison.................................................................... 31

2.5.4 Standard Deviation (STD) for Density Comparison .................................... 36

2.5.5 Coefficient of Variation (COV) for Density Comparison ........................... 39

viii

2.6 Comparison of Means ....................................................................................................... 43

2.6.1 Fisher’s Least Significant Difference (LSD) Test ....................................... 44

2.6.2 Tukey’s Honestly Significant Difference (HSD) Test ................................. 44

2.6.3 Student-Newman-Keuls (SNK) Test ........................................................... 44

2.6.4 Scheffe’s Test............................................................................................... 45

2.6.5 Lot-Wise Means Comparison ...................................................................... 45

2.6.5.1 Superpave Pavements ............................................................................ 45

2.6.5.2 PCC Pavements ...................................................................................... 48

2.6.6 Sublot-Wise Means Comparison ................................................................. 48


2.6.6.2 PCC Pavements ...................................................................................... 52

2.6.7 Sublot-Wise Comparison of Means ............................................................. 54


2.6.7.2 PCC Pavements ...................................................................................... 55

2.6.8 Effect of Significance Level on Pay Adjustments ....................................... 57

2.6.8.1 QC/QA Air Voids .................................................................................. 57

2.6.8.2 QC/QA Density ...................................................................................... 57

2.7 F & t Tests Using Superpave Pavement Density Data ..................................................... 59

2.8 Comparison of Design and Actual Asphalt Content ......................................................... 61

Chapter 3: Practical Performance Model and Composite Index ................................................... 65

3.1 General .............................................................................................................................. 65

3.2 Practical Performance Model ............................................................................................ 65

3.2.1 Superpave Pavements .................................................................................. 66

3.2.1.1 Two Quality Characteristics .................................................................. 66

3.2.1.2 Three Quality Characteristics ................................................................ 70

3.2.1.3 Four Quality Characteristics .................................................................. 74

3.2.1.4 Five Quality Characteristics ................................................................... 79

3.2.2 PCC Pavements ............................................................................................ 84

3.2.2.1 Two Quality Characteristics .................................................................. 85

3.2.2.2 Three Quality Characteristics ................................................................ 89

3.2.3 Pay Schedule ................................................................................................ 94

3.3 Composite Index ............................................................................................................... 95

3.3.1 Superpave Pavements .................................................................................. 95

3.3.1.1 Two Quality Characteristics without Cross-Product ............................. 95

3.3.1.2 Two Quality Characteristics with Cross-Product .................................. 96

ix

3.3.1.3 Three Quality Characteristics without Cross-Product ........................... 98

3.3.1.4 Three Quality Characteristics with Cross-Product ................................ 99

3.3.1.5 Four Quality Characteristics without Cross-Product ........................... 102

3.3.1.6 Four Quality Characteristics with Cross-Product ................................ 103

3.3.1.7 Five Quality Characteristics with Cross-Product ................................. 104

3.3.2 PCC Pavements .......................................................................................... 105

3.3.2.1 Two Quality Characteristics without Cross-Product ........................... 105

3.3.2.2 Two Quality Characteristics with Cross-Product ................................ 106

3.3.2.3 Three Quality Characteristics without Cross-Product ............................... 108

3.3.2.4 Three Quality Characteristics with Cross-Product .............................. 109

Chapter 4: Conclusions and Recommendations ......................................................................... 112

4.1 Conclusions ..................................................................................................................... 112

4.2 Recommendations ........................................................................................................... 112

References ................................................................................................................................... 114

x

List of Tables

TABLE 2.1 Superpave Pavements Test Sections ........................................................................... 5

TABLE 2.2 PCC Pavements Test Sections .................................................................................... 6

TABLE 2.3 Summary of Control Charts for Superpave Pavements ............................................ 18

TABLE 2.4 Summary of Control Chart in PCC Pavements ......................................................... 24

TABLE 2.5 Pay Adjustment for QC/QA Air Voids ..................................................................... 57

TABLE 2.6 Pay Adjustment for QC/QA Density ........................................................................ 58

TABLE 3.1 Data for PPM for Superpave Pavements (Two Variables, V=2) .............................. 66

TABLE 3.2 Test of Derivation of PPM for Superpave Pavements (V=2 and C=1) ..................... 66

TABLE 3.3 Test of Extremes of PPM for Superpave Pavements (V=2 and C=1) ....................... 67

TABLE 3.4 Test of Offsetting Property of PPM for Superpave Pavements (V=2 and C=1) ....... 68

TABLE 3.5 Test of Offsetting Property of PPM for Superpave Pavements (V=2 and C=0.5) .... 68

TABLE 3.6 Test of Extremes of PPM for Superpave Pavements (V=2 and C=0.5) .................... 69

TABLE 3.7 Test of Progressively Poorer Quality for Superpave Pavements (V=2 and C=0.5) .. 69

TABLE 3.8 Data for PPM for Superpave Pavements (Three Variables, V=3) ............................ 70

TABLE 3.9 Test of Derivation of PPM for Superpave Pavements (V=3 and C=1) ..................... 71

TABLE 3.10 Test of Extremes of PPM for Superpave Pavements (V=3 and C=1) ..................... 71

TABLE 3.11 Test of Offsetting Property of PPM for Superpave Pavements (V=3 and C=1) ..... 72

TABLE 3.12 Test of Offsetting Property of PPM for Superpave Pavement (V=3 and C=0.5) ... 73

TABLE 3.13 Test of Extremes of PPM for Superpave Pavements (V=3 and C=0.5) .................. 73

TABLE 3.14 Test of Progressively Poorer Quality for Superpave Pavements (V=3 and C=0.5) 74

TABLE 3.15 Data for PPM for Superpave Pavements (Four Variables, V=4) ............................ 75

TABLE 3.16 Test of Derivation of PPM for Superpave Pavements (V=4 and C=1) ................... 75


TABLE 3.18 Test of Offsetting Property of PPM for Superpave Pavements (V=4 and C=1) ..... 77

TABLE 3.19 Test of Offsetting Property of PPM for Superpave Pavements (V=4 and C=0.5) .. 77

TABLE 3.20 Test of Extremes of PPM for Superpave Pavements (V=4 and C=0.5) .................. 78

TABLE 3.21 Test of Progressively Poorer Quality for Superpave Pavements (V=4 and C=0.5) 79

TABLE 3.22 Data for PPM for Superpave Pavements (Five Variables, V=5) ............................ 80

TABLE 3.23 Test of Derivation of PPM for Superpave Pavements (V=5 and C=1) ................... 81


xi

TABLE 3.25 Test of Offsetting Property of PPM for Superpave Pavements (V=5 and

C=0.5)………………………………………………………………………………………..83

TABLE 3.26 Test of Offsetting Property of PPM for Superpave Pavements

(V=5 and C=0.5) ..................................................................................................................... 83

TABLE 3.27 Test of Extremes of PPM Models for Superpave Pavements (V=5 and C=0.5) ..... 83

TABLE 3.28 Test of Progressively Poorer Quality for Superpave Pavements

(V=5 and C=0.5) ..................................................................................................................... 84

TABLE 3.29 Data for PPM for PCC Pavements (Two Variables, V=2) ..................................... 85

TABLE 3.30 Test of Derivation of PPM for PCC Pavements (V=2 and C=1) ............................ 85

TABLE 3.31 Test of Extremes of PPM for PCC Pavements (V=2 and C=1) .............................. 86

TABLE 3.32 Test of Offsetting Property of PPM for PCC Pavements (V=2 and C=1) .............. 87

TABLE 3.33 Test of Offsetting Property of PPM for PCC Pavements (V=2 and C=0.5) ........... 87

TABLE 3.34 Test of Extremes of PPM for PCC Pavements (V=2 and C=0.5) ........................... 88

TABLE 3.35 Test of Progressively Poorer Quality for PCC Pavements (V=2 and C=0.5) ......... 88

TABLE 3.36 Data for PPM for PCC Pavements (Three Variables, V=3) ................................... 89

TABLE 3.37 Test of Derivation of PPM for PCC Pavements (V=3 and C=1) ............................ 90

TABLE 3.38 Test of Extremes of PPM for PCC Pavements (V=3 and C=1) .............................. 91

TABLE 3.39 Test of Offsetting Property of PPM for PCC Pavements (V=3 and C=1) .............. 92

TABLE 3.40 Test of Offsetting Property of PPM for PCC Pavements (V=3 and C=0.5) ........... 92

TABLE 3.41 Test of Extremes of PPM for PCC Pavements (V=3 and C=0.5) ........................... 93

TABLE 3.42 Test of Progressively Poorer Quality for PCC Pavements (V=3 and C=0.5) ......... 93

TABLE 3.43 Data for Composite Index for Superpave Pavements (Variables, V=2) ................. 97

TABLE 3.44 Data for Composite Index for Superpave Pavements (Variables, V=3) ............... 101

TABLE 3.45 Data for Composite Index for PCC Pavements (Two Variables, V=2) ................ 107

TABLE 3.46 Data for Composite Index for PCC Pavements (Variables, V=3) ........................ 110

xii

List of Figures

FIGURE 2.1 Typical Moving Average Control Charts for QC Air Voids in District 1 ................. 8

FIGURE 2.2 Typical Moving Average Control Charts for QC Air Voids in District 2 ................. 9

FIGURE 2.3 Typical Moving Average Control Charts for QC Air Voids in District 3 ............... 10




FIGURE 2.7 Moving Average Control Charts for QC Density in District 2 ............................... 13

FIGURE 2.8 Moving Average Control Charts for QC Density in District 3 ............................... 14

FIGURE 2.9 Typical Moving Average Control Charts for QC Density in District 5 .................. 14

FIGURE 2.10 Typical Moving Average Control Charts for QC Density in District 6 ................ 15

FIGURE 2.11 Typical Moving Average Control Charts for QA Density in District 2 ................ 16

FIGURE 2.12 Moving Average Control Charts for QA Density in District 3 ............................. 16



FIGURE 2.15 Typical Moving Average Control Charts for QC PCC Strength in District 1 ...... 19




FIGURE 2.19 Typical Moving Average Control Charts for QC PCC Thickness in District 1 .... 22

FIGURE 2.20 Moving Average Control Charts for QC PCC Thickness in District 2 ................. 22



FIGURE 2.23 QC and QA Mean Density Comparison for US-81 in District 2 ........................... 25

FIGURE 2.24 QC and QA Mean Density Comparison for K-383 in District 3 ........................... 26



FIGURE 2.27 Summary of QC and QA Mean Density Comparison ........................................... 28

FIGURE 2.28 QC and QA Maximum Density Comparison for US-81 in District 2 ................... 29

FIGURE 2.29 QC and QA Maximum Density Comparison for K-383 in District 3 ................... 29



xiii

FIGURE 2.32 Summary of QC and QA Maximum Density Comparison ................................... 32

FIGURE 2.33 QC and QA Minimum Density Comparison for US-81 in District 2 .................... 32

FIGURE 2.34 QC and QA Minimum Density Comparison for K-383 in District 3 .................... 33



FIGURE 2.37 Summary of QC and QA Minimum Density Comparison .................................... 35

FIGURE 2.38 QC and QA STD Density Comparison for US-81 in District 2 ............................ 36

FIGURE 2.39 QC and QA STD Density Comparison for K-383 in District 3 ............................ 37



FIGURE 2.42 Summary of QC and QA STD Density Comparison............................................. 39

FIGURE 2.43 QC and QA COV Density Comparison for US-81 in District 2 ........................... 40

FIGURE 2.44 QC and QA COV Density Comparison for K-383 in District 3............................ 41



FIGURE 2.47 Summary of QC and QA COV density comparison. ............................................ 43

FIGURE 2.48 Lot-Wise Means Comparison for QC Air Voids ................................................... 46

FIGURE 2.49 Lot-Wise Mean Comparison for QC Smoothness ................................................. 46

FIGURE 2.50 Lot-Wise Means Comparison for QC Density ...................................................... 47

FIGURE 2.51 Lot-Wise Means Comparison for QA Density ...................................................... 47

FIGURE 2.52 Lot-Wise Means Comparison for PCC Strength ................................................... 49

FIGURE 2.53 Lot-Wise Means Comparison for PCC Thickness ................................................ 49

FIGURE 2.54 Sublot-Wise Means Comparison for QC/QA Air Voids ....................................... 50

FIGURE 2.55 Sublot-Wise Means Comparison for QC Density ................................................. 51

FIGURE 2.56 Sublot-Wise Means Comparison for QA Density ................................................. 51

FIGURE 2.57 Sublot-Wise Means Comparison for QC/QA Density .......................................... 52

FIGURE 2.58 Sublot-Wise Means Comparison for QC PCC Strength........................................ 53

FIGURE 2.59 Sublot-Wise Means Comparison for QC PCC Thickness ..................................... 53

FIGURE 2.60 Sublot-Wise Comparison of Mean Magnitude for QC/QA Air Void ................... 54

FIGURE 2.61 Sublot-Wise Comparison of Mean Magnitude for QA Density ............................ 55

FIGURE 2.62 Sublot-Wise Comparison of Mean Magnitude for QC PCC Strength .................. 56

FIGURE 2.63 Sublot-Wise Comparison of Mean Magnitude for QC PCC Thickness ................ 56

FIGURE 2.64 Pay Adjustment for QC/QA Air Voids ................................................................. 58

xiv

FIGURE 2.65 Pay Adjustment for QC/QA Density ..................................................................... 59

FIGURE 2.66 QC/QA Density Significant Difference Test ......................................................... 60

FIGURE 2.67 Discrepancy of QC/QA Significant Difference Test for Equal and Unequal

Variance .................................................................................................................................. 61

FIGURE 2.68 Typical Comparisons of Design and Actual AC When Actual Is Lower Than

Design ..................................................................................................................................... 62

FIGURE 2.69 Typical Comparison of Design and Actual AC When Actual is Lower Than

Design ..................................................................................................................................... 62

FIGURE 2.70 Typical Comparison of Design and Actual AC When Actual is Lower and Higher

Than Design ............................................................................................................................ 63

FIGURE 2.71 Summary of Actual and Design AC Content Comparison .................................... 63

1

Chapter 1: Introduction

1.1 General

The history of highway quality assurance has progressed from the early materials and

methods specifications through statistical end-result specifications to the current trend toward

performance-related specifications (PRS) based on mathematical models and statistical concepts

(Weed 2000). The impetus for use of statistical methods in managing highway construction

quality began in 1963 with an initiative led by the Bureau of Public Roads (Benson et al. 2000).

This initial effort resulted in development and implementation of Portland Cement Concrete

(PCC) specifications in 1973, followed by their evaluation in 1979 (Diwan et al. 2003). These

developments led to research efforts by a number of states to obtain information relative to the

variability and quality level of the construction practices. This information on variability was

then translated into specifications using standard statistical procedures for quality control and

quality acceptance. The initial effort was finally completed by implementing these statistical

specifications for highway materials and construction (Shah 1976).

Many states have adopted statistical quality control/quality assurance (QC/QA) programs.

The properties controlled under statistical QC/QA programs should be either related to

performance or desirable end-results. These end-result specifications are usually based on

statistics from historical construction data (Schmitt et al. 1998, Parker and Hossain 2002). Many

agencies now also include bonus provisions that award payment somewhat in excess of the

contract price when the quality level substantially exceeds the level that has been specified

(NCHRP 1995, Weed 2002, Weed and Tabrizi 2005). One of the advantages of statistical

specifications is the production of accurate data from valid random sampling procedures. This

data may be analyzed later to improve the specifications further (Afferton et al. 1992).

Some agencies are moving in the direction of PRS that specifies the desired levels of key

construction quality characteristics that have been found to correlate with fundamental

engineering properties which predict performance. When there are different types of tests to be

performed on a particular construction item, it can become a complex matter to design an

acceptance procedure that is fair, effective, and free from inconsistencies. Composite index

avoids certain inconsistencies in practice that may occur with other methods for dealing with

2

multiple quality characteristics. It leads to rational pay schedules in that it assures that all

combinations of individual quality measures that predict the same level of expected life will

receive the same amount of pay adjustment (Weed 2006).

1.2 Problem Statement

The Kansas Department of Transportation (KDOT) has built an impressive database of

as-constructed materials properties for Superpave and Portland Cement Concrete (PCC)

pavements from the tests required as part of the QC/QA program. KDOT also has a Construction

Management System (CMS) that captures data on selected attributes related to highway

construction in Kansas. Burati et al. (2004) have argued that any specification must also be an

evolutionary process. Since new information is constantly becoming available in the form of

additional test results, and as new construction or testing processes are employed, the

specification must be continually monitored to see if improvements are needed. Thus a review of

the current QC/QA specifications of KDOT is needed to find the opportunities for improvement.

This need has also been echoed by the recent Federal Highway Administration (FHWA) QA

Stewardship Review of KDOT with respect to use of a different payment lot size, review of

acceptance of contractor’s test results, changing level of significance for statistical testing,

developing composite index, and practical performance model.

1.3 Objectives of the Study

The main objectives of this study were to:

Investigate any systematic bias in KDOT QC/QA data using moving average

control chart analysis;

Compare lot-and sublot-wise means and to investigate the possibility of changing

lot size;

Analyze the consequences of changing the level of significance from 1% to

2.5%;

Determine the consequences of using the F-test along with the t-test to determine

whether or not to use the test results from the contractor for acceptance;

3

Analyze actual and design asphalt contents to see if including asphalt content in

pay adjustment is justifiable; and

Develop practical performance models and composite index.

1.4 Organization of the Report

This report includes four chapters. The first chapter deals with brief literature review,

problem statement, and objectives of the study. Data analysis for both Superpave and PCC

pavements is described in the second chapter. The third chapter includes practical performance

models and composite index that include various quality characteristics for both Superpave and

PCC pavements. The last chapter deals with conclusions and recommendations based on this

study.

4

Chapter 2: Data Analysis

2.1 General

This chapter deals with data analysis for Superpave and PCC pavements in Kansas.

Control chart analysis for different quality characteristics, QC and QA density comparison, lot-

and sublot-wise comparison of means using four comparison methods at three significance

levels, effect of significant levels on pay adjustments, feasibility of using F-test instead of t-test,

and finally asphalt content of Superpave pavements constructed have been described in this

chapter.

2.2 Project Selection

Both Superpave and PCC pavements have been selected in this study. Thirty five

Superpave pavements, built between 2004 and 2007, were selected based on total tonnage as

shown in Table 2.1. The selected projects are such that multiple lots of 3,000 tons were produced

and placed on these projects. These projects are from all six administrative districts of KDOT.

The length of the projects varies from 1.92 miles to 31.03 miles.

The PCC pavements have been selected based on size as well. Thirteen PCC pavements

were selected from four KDOT districts as shown in Table 2.2. Most of the PCC pavements are

on interstate highways.

2.3 Data Collection

Random sampling procedures were used to collect QC/QA data. It is well established that

random sampling procedures avoid biases and lead to a more reliable estimate of the as-built

construction quality (Weed 1989).

2.3.1 Superpave Pavements

Air voids, in-place density, asphalt content, smoothness, and voids in mineral aggregates

(VMA) data have been used in this study. The following sections describe the ways these data

have been collected.

5

TABLE 2.1 Superpave Pavements Test Sections

Route County

Name

KDOT

Dist.

No.

Fiscal

Year

Let

Date

No. of

Lanes

Length

(mi)

1 U075 Brown 1 2004 08/20/03 4 7.52

2 K007 Doniphan 1 2004 01/14/04 2 5.28

3 U036 Doniphan 1 2004 03/17/04 4 3.98

4 U040 Douglas 1 2004 05/19/04 4 1.92

5 I135 McPherson 2 2003 02/19/03 4 9.22

6 I070 Saline 2 2004 12/17/03 4 13.73

7 U077 Marion 2 2005 12/15/04 2 8.82

8 I135 McPherson 2 2005 12/15/04 4 10.07

9 U081 Ottawa 2 2006 11/16/05 2 10.20

10 K156 Ellsworth 2 2006 01/18/06 2 15.10

11 U036 Jewell 2 2007 11/15/06 2 6.40

12 U283 Graham 3 2003 06/18/03 2 13.48

13 U283 Norton 3 2003 06/18/03 2 6.03

14 U024 Rooks 3 2005 11/17/04 2 31.03

15 U283 Trego 3 2005 03/16/05 2 10.00

16 U283 Trego 3 2005 03/16/05 2 11.93

17 K027 Sherman 3 2006 02/15/06 2 7.08

18 K027 Sherman 3 2006 02/15/06 2 6.09

19 U083 Sheridan 3 2007 11/15/06 2 11.34

20 K383 Decatur 3 2007 02/21/07 2 14.13

21 U160 Crawford 4 2003 02/19/03 2 4.78

22 I035 Coffey 4 2006 07/20/05 4 5.53

23 U077 Butler 5 2003 12/11/02 2 13.92

24 U077 Cowley 5 2003 02/19/03 2 9.35

25 U050 Edwards 5 2003 03/12/03 2 8.82

26 U050 Reno 5 2005 01/19/05 2 7.83

27 U054 Kingman 5 2006 03/15/06 4 6.41

28 K096 Barton 5 2007 10/18/06 2 13.63

29 K027 Greeley 6 2003 01/08/03 2 15.91

30 U050 Finney 6 2004 01/14/04 2 10.07

31 U056 Stevens 6 2005 03/16/05 2 11.37

32 U054 Seward 6 2006 12/14/05 4 3.73

33 K027 Stanton 6 2006 12/14/05 2 12.30

34 K096 Wichita 6 2007 10/18/06 2 11.78

35 U054 Seward 6 2007 11/15/06 2 11.37

6

TABLE 2.2 PCC Pavements Test Sections

Route County Name

KDOT

District No. Let Date

1 I035 Osage 1 07/16/03

2 I035 Wyandotte 1 12/17/03

3 I035 Wyandotte 1 01/14/04

4 I035 Johnson 1 06/16/04

5 I035 Leavenworth 1 07/21/04

6 I070 Wyandotte 1 06/15/05

7 I070 Dickinson 2 07/21/04

8 U054 Bourbon 4 04/16/03

9 I035 Coffey 4 -

10 U069 Miami 4 12/17/03

11 U069 Bourbon 4 06/16/04

12 U054 Sedgwick 5 01/08/03

13 I135 Sedgwick 5 07/16/03

2.3.1.1 Air Voids

The normal lot-size for air voids is 3,000 tons. The lot is divided into four subolts of

uniform size. KDOT specifies roadway sampling. Roadway samples are obtained for each sublot

from behind the paver before compaction. A three-sided template is pushed into the mat prior to

compaction. A square shovel is then used to extract all asphalt mixtures from the selected

locations. The sample is obtained from a minimum of three locations randomly selected by

KDOT personnel throughout one truck load of placed material. The selection process involves

one random number for the sampled tonnage (truck load) and two random numbers for

transverse and longitudinal locations (Elseifi et al. 2009).

The samples are transported to the test facility using a method to retain heat to facilitate

sample quartering procedures. Air voids tests are performed on Superpave gyratory-compacted

samples of a given mix design. A lot normally consists of results of four contiguous results of

individual QC and one QA.

7

2.3.1.2 Density

KDOT considers the day’s placement as a lot for density measurements. This lot is also

subdivided into five uniform sublots. Random test locations are selected by the Contractor or the

Engineer. Mat density is typically measured with nuclear density gages but can also be measured

from cores. Contractor makes two and KDOT makes one independent mat density measurement

for each sublot (2 to 1 sampling ratio) (Turochy and Parker 2007).

2.3.1.3 Smoothness

A California-type profilograph or if approved by the Bureau of Materials and Research of

KDOT, other types of profilographs (such as a Light Weight Profiler) that produce results

comparable to the California-type profilograph may be used. A 0.1-mile long sublot size is used.

Only QC data is collected. Pay adjustment is based on pavement smoothness in terms of average

profile index of the pavement section before any corrective work is performed. A zero blanking

band is used for profilogram analysis.

2.3.2 PCC

In Kansas, pay adjustments for pavement thickness and concrete compressive strength are

based on test results from cores taken from each lot. All cores for determining strength shall be

taken at a minimum of 21 days after the pavement has been placed, and in time to determine 28-

day compressive strengths. For mainline and other pavements subject to coring for pay

adjustments for both thickness and strength, a lot is defined as the surface area of mainline

pavement lane placed in a single day. Normally, a lot representing a day's production is divided

into five sublots of approximately equal surface area. For high daily production rates (rates

exceeding 6,000 square yards per day), the contractor may choose to divide the day’s production

into two approximately equal lots consisting of five sublots each. Normally one core is taken per

sublot (Khanum et al. 2006). Cores are transported to the laboratory as soon as possible and the

thickness is measured at three points at approximately 120º apart. Then the 4-inch diameter cores

are cured to be tested for 28-day compressive strength.

8

2.4 Control Charts

Microsoft Excel was used to calculate moving averages, average, lower, and upper limits

(minus/plus three times standard deviation) for different quality characteristics. Typical control

charts for Superpave and PCC pavements are presented.


Typical control charts for different quality characteristics from each district have been

presented. Control charts for QC air voids, and QC/QA density have been discussed.

2.4.1.1 QC Air Voids

Figure 2.1 shows moving average control chart for US-75 route in Brown County. It is

typical for QC air voids in District 1. The moving average values are sometimes lower and

higher than average value though the difference is not significant. All moving average values are

within 3 where σ is the standard deviation.

FIGURE 2.1 Typical Moving Average Control Charts for QC Air Voids in District 1

0

1

2

3

4

5

6

1 6 11 16 21

Air

Void

s (%

)

No. of Moving Average

503082011: U75 (Brown)- PG 70-28, SM-9.5A, 1.5in.

Moving Average Lower Average Upper

9

Figure 2.2 shows moving average control chart for US-81 route in Ottawa County. It is


higher than average value though the difference is not significant. All moving average values are

within 3 .

Figure 2.3 shows moving average control chart for K-383 route in Decatur County. It is

typical for QC air voids in District 3. The moving average values are slightly lower than the

average for about half of the points and slightly higher than the average for another half of the

points though the difference is not significant since all moving average values are within 3 .


0

1

2

3

4

5

6

1 6 11 16 21 26 31 36 41 46

Air

Vo

ids

(%)


505136222: U81 (Ottawa)-PG 70-28, SM-19A, 2.5in.


10


Figure 2.4 shows moving average control chart for US-160 route in Crawford County. It

is typical for QC air voids in District 4. The moving average values are mostly lower than

average value though the difference is not significant. All moving average values are within 3


0

1

2

3

4

5

6

1 6 11 16 21 26 31

Air

Vo

ids

(%)


507026343: K383 (Decatur)- PG 64-22, SR-19A, 2in.


0

1

2

3

4

5

6

1 6 11 16 21

Air

Void

s (%

)


503022124: U160 (Crawford)-PG 64-22, SM-19A


11

Figure 2.5 shows moving average control chart for US-77 route in Butler County. It is


higher than average value. All moving average values are within 3 .

Figure 2.6 shows moving average control chart for US-50 route in Finney County. It is

typical for QC air voids in District 6. The moving average values are very close to the average

value except at few points where they are slightly higher or lower than the average. All moving

average values are within 3 .


0

1

2

3

4

5

6

1 6 11 16 21 26 31 36 41

Air

Vo

ids

(%)


502132105: U77 (Butler)-SM-19A


12


2.4.1.2 QC Density

Districts 1 and 4 do not have complete QC density data for moving average control chart

analysis i.e. one or more sublot density data is missing. All moving average values are within

3 except for K-383 route in Decatur County. Typical moving average control charts will be

described for Districts 2, 3, 5 and 6.

Moving average control chart for US-81 route is shown in Figure 2.7. This is the only QA

density moving average control chart in District 2. Most of the moving averages are less than the

average for all of the first 25% of data points whereas the reverse is true for the next 30% of the

data points. The moving average for the remaining data points is close to the average.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141

Air

Void

s (%

)


504012396: U50 (Finney)-SM-19A, 11in.


13

FIGURE 2.7 Moving Average Control Charts for QC Density in District 2

Figure 2.8 shows the moving average control charts for K-383 in Decatur County. This is

the only QC density moving average control chart which lies outside 3 . This shows density at

the beginning of the tests was very low compared to the rest. Except for the first few readings,

the other moving average values are mostly equal to or greater than the average density.

Figure 2.9 shows moving average control charts for US-54 route in Kingman County.

This is typical for QC density moving average control chart in District 5. The moving averages

are lower and higher than the average at certain interval, respectively. The trend may be due to an

action taken by the contractor when density is low or high to keep it close to the average.

84

86

88

90

92

94

96

1 11 21 31 41 51 61 71 81 91 101 111

Den

sity

(%

)


505136222: U81 (Ottawa)- PG 58-28, SR-19A, 3.5 in.


14

FIGURE 2.8 Moving Average Control Charts for QC Density in District 3

FIGURE 2.9 Typical Moving Average Control Charts for QC Density in District 5

Moving average control charts for US-54 route in Seward County is shown in Figure

2.10. This is typical for QC density moving average control chart in District 6. The moving

86

88

90

92

94

96

98

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151

Den

sity

(%

)


507026343: K383 (Decatur)-PG 64-22, SR-19A, 2in.


82

84

86

88

90

92

94

96

98

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81

Den

sity

(%

)


506032255: U54 (Kingman)-PG 64-28, SR-12.5A, 2.5in.


15

averages are higher than the average for some of the data points at the beginning, but they are

equal to or less than the average for the majority of points.

FIGURE 2.10 Typical Moving Average Control Charts for QC Density in District 6

2.4.1.3 QA Density

Districts 1 and 4 do not have complete QA density data for moving average control chart

analysis i.e. one or more sublot density data is missing. All moving average values are within

3 except for K-383 route in Decatur County. Typical moving average control charts will be

described for each District.

Moving average control chart for US-81 route is shown in Figure 2.11. This is typical QA

density moving average control chart in District 2. Most of the moving averages are less than the

average for most of the first 50% of data points whereas the reverse is true for the rest of data

points though all moving averages are close to average from a practical point of view.

Figure 2.12 shows moving average control chart for K-383 in Decatur Country. This is

the only QA density moving average control chart which lies outside 3 . This shows density at

the beginning of the tests was very low compared to the rest. Except for the first few readings,

88

89

90

91

92

93

94

95

96

97

1 11 21 31 41 51 61 71 81 91 101 111 121

Den

sity

(%

)


506126676: U54 (Seward)- PG 70-22, SM-12.5A, 2in.


16

the other moving average values closer to the average value. The same trend was observed for

QC density control chart for the same route in this county.

FIGURE 2.11 Typical Moving Average Control Charts for QA Density in District 2

FIGURE 2.12 Moving Average Control Charts for QA Density in District 3

84

86

88

90

92

94

96

1 6 11 16 21 26 31 36 41 46 51 56

Den

sity

(%

)


505136222: U81 (Ottawa)-PG 58-28, SR-19A, 3.5in.


84

86

88

90

92

94

96

98

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76

Den

sity

(%

)


507026343: K383 (Decatur)-PG 64-22, SR-19A, 2in.


17

Figure 2.13 indicates moving average control chart for US-54 route in Kingman County.

It is typical QA density control chart in District 5. Moving averages are lower than average for

the first 30% of data points and higher than the average for the rest of the data points.

Moving average control chart for US-54 in Seward County is shown in Figure 2.14. It is

typical QA density control chart for District 6. Moving average is sometimes lower and higher

for about 60% of data points whereas it is lower than the average for rest of the data.

Table 2.3 shows the summary of control chart analysis for Superpave pavements in

different districts. The moving averages for air voids for all 49 QC cases are inside 3 where σ

is the standard deviation. Density in one sublot is outside 3 for both QC and QA. These

results do not clearly show any systematic bias in QC and QA data.


84

86

88

90

92

94

96

98

1 6 11 16 21 26 31 36 41

Den

sity

(%

)


506032255: U54 (Kingman)-PG 64-28, SR-12.5A, 2.5in.


18


TABLE 2.3

Summary of Control Charts for Superpave Pavements

District

Superpave Pavements

Air Voids

Quality Control

Density

Quality Control Quality Assurance

1 4 - -

2 13 2 2

3 6 5 5

4 1 - -

5 12 2 2

6 13 3 3

Total 49 12 12

2.4.2 PCC Pavements

Typical control charts for different quality characteristics from each district have been

presented. Control charts for QC PCC strength and thickness have been discussed. Districts 3

and 6 do not have complete PCC strength and thickness data for moving average control charts.

Typical moving average control charts are presented for the rest of the districts.

87

88

89

90

91

92

93

94

95

96

97

1 6 11 16 21 26 31 36 41 46 51

Den

sity

(%

)


506126676: U54 (Seward), PG 70-22, SM-12.5A, 2in.


19

2.4.2.1 QC Strength

Figure 2.15 indicates moving average control chart for Interstate 35 (I-35) route in Osage

County. The moving averages are slightly higher or lower than the average for about 50% of data

points whereas they are higher than the average for rest of the data points.

FIGURE 2.15 Typical Moving Average Control Charts for QC PCC Strength in District 1

Figure 2.16 shows moving average control chart for I-70 in Dickinson County. This is

typical for QC PCC strength moving average control charts in District 2. The moving averages

are mostly higher than the average for about 60% of the data points whereas they are mostly

lower than the average for the rest of the data points.

Moving average control chart for US-69 in Bourbon County is given in Figure 2.17. It is

typical for QC PCC strength control chart analysis in District 4. Moving averages are mostly less

than the average for the first 45% of the data points. The moving averages for the remaining data

points are sometimes higher and lower than the average value.

0

10

20

30

40

50

60

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141

PC

C S

tren

gth

(M

Pa

)


503071011: I35 (Osage)


20



0

10

20

30

40

50

60

1 16 31 46 61 76 91 106 121 136 151 166 181 196

PC

C S

tren

gth

(M

Pa

)


504071012: I70 (Dickinson)


0

10

20

30

40

50

60

1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241

PC

C S

tren

gth

(M

Pa

)


504062164: U69 (Bourbon)


21

Figure 2.18 shows moving average control chart for US-54 route in Sedgwick County. It

is the typical QC PCC moving average control chart in District 5. The moving averages are

slightly higher or lower than the average, but it is very close to the average for all practical

purposes.


2.4.2.2 QC Thickness

All PCC thickness moving averages are within 3 except moving average control

chart for I-70 route in Dickinson County. Figure 2.19 shows moving average control chart for I-

35 route in Osage County. This is typical for QC PCC thickness control chart in District 1.

Moving averages are lower than the average for some of the data points and higher than the

average for the rest. The moving averages are close to the average for last 12.5% of data points.

Figure 2.20 shows moving average control chart for I-70 route in Dickinson County. This

is the only QC PCC thickness moving average control chart which lies slightly outside the lower

limit at two points. The remaining moving averages are very close to the average value.

0

10

20

30

40

50

60

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141

PC

C S

tren

gth

(M

Pa

)


503012185: U54 (Sedgwick)


22

FIGURE 2.19 Typical Moving Average Control Charts for QC PCC Thickness in District 1

FIGURE 2.20 Moving Average Control Charts for QC PCC Thickness in District 2

11

11.5

12

12.5

13

13.5

14

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161

PC

C T

hic

kn

ess

(in

)


503071011: I35 (Osage)


0

2

4

6

8

10

12

14

16

1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226

PC

C T

hic

kn

ess

(in

)


504071012: I70 (Dickinson)


23

Figure 2.21 shows moving average control chart for US-69 route in Bourbon County. It is

the typical QC PCC thickness moving average control chart in District 4. The moving averages

are very close to the average value for most of the points.


Moving average control chart for US-54 in Sedgwick County is shown in Figure 2.22. It

is typical QC PCC thickness moving average control chart in District 5. The moving averages at

some points are very close to the upper limit. Most of the moving averages are very close to the

average value.

Table 2.4 shows the summary of control chart analysis for PCC pavements in different

districts. All moving averages for PCC QC data for thickness are within 3 except for one

sublot for thickness. These results do not clearly show any systematic bias in QC data.

0

2

4

6

8

10

12

14

1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271

PC

C T

hic

kn

ess

(in

)


504062164: U69 (Bourbon)


24


TABLE 2.4

Summary of Control Chart in PCC Pavements

District Quality Characteristics

Strength Thickness

1 6 6

2 1 1

3 - -

4 4 4

5 2 2

6 - -

Total 13 13

2.5 Comparison of QC and QA Density

QC and QA density comparison in terms of mean, minimum, maximum, standard

deviation (STD), and coefficient of variation (COV) has been carried out in order to investigate

whether QC density is consistently higher than the QA density. Districts 1 and 4 do not have

complete data for QC and QA comparison. All the statistics were based on lots that include 10

QC data points and five QA data points. Projects with the highest number of lots were selected

from the remaining districts.

0

2

4

6

8

10

12

1 16 31 46 61 76 91 106 121 136 151 166 181 196

PC

C T

hic

kn

ess

(in

)


503012185: U54 (Sedgwick)


25

2.5.1 Mean Density Comparison

Figure 2.23 shows QC/QA mean density comparison for US-81 route in Ottawa County.

It has the highest number of lots of all projects in District 2 in this study. QC mean density is

higher than QA mean density in nine out of 12 lots, which is 75%. The mean difference is the

highest in lot 5 and the smallest in lot 2, respectively. The mean difference may not be significant

statistically.

FIGURE 2.23 QC and QA Mean Density Comparison for US-81 in District 2

Figure 2.24 shows QC/QA mean density comparison for K-383 route in Decatur County.

It has the highest number of lots of all projects in District 3 in this study. QC mean density is

higher than QA mean density in 13 out of 16 lots, which is about 81%. The mean difference is

the highest in lot 3 and the smallest in lot 8, respectively. Lot 1 has the lowest QC/QA mean

density. The mean difference may not be significant from a practical point of view.

Figure 2.25 shows QC/QA mean density comparison for US-54 route in Kingman

County. It has the highest number of lots of all projects in District 5 in this study. QC mean

density is higher than QA mean density in six out of nine lots, which is about 67%. The mean

difference is the highest in lot 2 and the smallest in lot 7, respectively. Lot 4 has the lowest QC

88.5

89.0

89.5

90.0

90.5

91.0

91.5

92.0

92.5

93.0

1 2 3 4 5 6 7 8 9 10 11 12

Mea

n D

en

sity

(%

)

Lot Number

505136222: U81 (Ottawa)-PG 58-28, SR-19A, 3.5 in

QC QA

26

mean density whereas lot 1 has the lowest QA mean density. The mean difference may not be

significant from a practical point of view.

FIGURE 2.24 QC and QA Mean Density Comparison for K-383 in District 3


86.0

87.0

88.0

89.0

90.0

91.0

92.0

93.0

94.0

95.0

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

Mea

n D

ensi

ty (%

)

Lot Number

507026343: K383 (Decatur)-PG 64-22, SR-19A, 2 in

QC QA

88.0

89.0

90.0

91.0

92.0

93.0

94.0

1 2 3 4 5 6 7 8 9

Mea

n D

ensi

ty (%

)

Lot Number

506032255: U54 (Kingman)-PG 64-28, SR-12.5A, 2.5 in

QC QA

27

Figure 2.26 shows QC/QA mean density comparison for US-54 route in Seward County.

It has the highest number of lots out of all projects in District 6 in this study. There were no QA

data for lots 1 and 2. QC mean density is higher than QA mean density in five out of 11 lots that

had both QC and QA data, which is about 45%. This result is different from majority of the

projects in which QC mean density is higher than QA mean density in most of the lots. The mean

difference is the highest in lot 8 and the smallest in lot 9, respectively. Lot 11 has the lowest QC

mean density whereas lot 5 has the lowest QA mean density.


Figure 2.27 shows summary of QC/QA mean density comparison for 12 projects in four

KDOT districts. QC mean density was compared to QA mean density. QC mean density is higher

than QA mean density for all projects except three. The three projects in which QC mean density

is lower than QA mean density in most of the cases are projects 7, 11, and 12. Project 7 is US-83

route in Sheridan County, which is located in District 3. Projects 11 and 12 are located in District

6. Project 11 is K-27 route in Stanton County whereas project 12 is US-54 route in Seward

County. In general, QC mean density is higher than the QA mean density.

91.5

92.0

92.5

93.0

93.5

94.0

94.5

95.0

1 2 3 4 5 6 7 8 9 10 11 12 13

Mea

n D

ensi

ty

(%)

Lot Number

506126676: U54 (Seward)-PG 70-22, SM-12.5A, 2 in

QC QA

28

FIGURE 2.27 Summary of QC and QA Mean Density Comparison

2.5.2 Maximum Density Comparison

Figure 2.28 shows QC/QA maximum density comparison for US-81 route in Ottawa

County. It has the highest number of lots out of all projects in District 2 in this study. QC

maximum density is higher than QA maximum density in 10 out of 12 lots, which is about 83%.

QC/QA maximum density difference is the highest in lot 5 like mean density difference. QC/QA

maximum density difference is the smallest in lot 6 unlike mean density difference.

Figure 2.29 shows QC/QA maximum density comparison for K-383 route in Decatur

County. It has the highest number of lots of all projects in District 3 in this study. QC maximum

density is higher than QA maximum density in 12 out of 16 lots, which is 75%. QC/QA

maximum density difference is the highest in lot 3 like mean density difference. The smallest

QC/QA maximum density difference is observed in lot 6. Lot 1 has the lowest QC/QA maximum

density like the mean density. QC/QA maximum density difference may not be significant from a

practical point of view. Also, since the contractor is doing more tests than KDOT per lot, the

contractor test results should be higher and lower

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

1 2 3 4 5 6 7 8 9 10 11 12

Fre

qu

ency

(N

um

ber

)

Project Number

Higher Lower

29

FIGURE 2.28 QC and QA Maximum Density Comparison for US-81 in District 2

FIGURE 2.29 QC and QA Maximum Density Comparison for K-383 in District 3

Figure 2.30 shows QC/QA maximum density comparison for US-54 route in Kingman

County. It has the highest number of lots of all projects in District 5 in this study. QC maximum

90.0

90.5

91.0

91.5

92.0

92.5

93.0

93.5

94.0

94.5

95.0

95.5

1 2 3 4 5 6 7 8 9 10 11 12

Ma

xim

um

Den

sity

(%

)

Lot Number

505136222: U81 (Ottawa)-PG 58-28, SR-19A, 3.5 in

QC QA

87.0

88.0

89.0

90.0

91.0

92.0

93.0

94.0

95.0

96.0

97.0

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

Ma

xim

um

Den

sity

(%

)

Lot Number


QC QA

30

density is higher than QA maximum density in six out of 9 lots, which is about 67%. QC/QA

maximum density difference is the highest in lot 1 unlike mean density difference. The smallest

QC/QA maximum density difference has been observed in lot 5 unlike mean density difference.

Lot 4 has the smallest QC maximum density whereas lot 1 has the smallest QA maximum

density.


Figure 2.31 shows QC/QA maximum density comparison for US-54 route in Seward

County. It has the highest number of lots of all projects in District 6 in this study. There were no

QA data for lots 1 and 2. QC maximum density is higher than QA maximum density in one out

of 11 lots that had both QC and QA data, which is about 9%. This result is different from

majority of the projects in which QC maximum density is higher than QA maximum density in

most of the lots. The maximum density difference is the highest in lot 10 and the smallest in lot

13, respectively. Lot 11 has the smallest QC maximum density whereas lot 10 has the smallest

QA maximum density.

86.0

88.0

90.0

92.0

94.0

96.0

98.0

100.0

1 2 3 4 5 6 7 8 9

Ma

xim

um

Den

sity

(%

)

Lot Number


QC QA

31


Figure 2.32 shows summary of QC/QA maximum density comparison for 12 projects in

four KDOT districts. QC maximum density was compared to QA maximum density in a lot. QC

maximum density is higher than QA maximum density for all projects except four. The four

projects in which QC maximum density is lower than QA maximum density in most of the cases

are projects 5, 7, 11, and 12. Projects 5 and 7 are located in District 3. Project 5 is US-283 in

Norton County. Project 7 is US-83 route in Sheridan County. Projects 11 and 12 are located in

District 6. Project 11 is K-27 route in Stanton County whereas project 12 is US-54 route in

Seward County. QA density in lot 1 is missing for K-27 route and QA density for lots 1 and 2 are

missing for US-54 route. In general, lot-by-lot comparison shows that QC maximum density is

higher than the QA maximum density. One of the reasons for this may be due to more data points

taken by contractor, in which maximum values are expected.

2.5.3 Minimum Density Comparison

Minimum (lowest) QC and QA density was selected for each lot for different projects.

Figure 2.32 shows QC/QA minimum density comparison for US-81 route in Ottawa County. It

has the highest number of lots of all projects in District 2 in this study. QC minimum density is

92.0

92.5

93.0

93.5

94.0

94.5

95.0

95.5

96.0

1 2 3 4 5 6 7 8 9 10 11 12 13

Maxim

um

Den

sity

(%

)

Lot Number

506126676: U54 (Seward)-PG 70-22, SM-12.5A, 2 in

QC QA

32

higher than QA minimum density in eight out of 12 lots, which is about 67%. QC/QA minimum

density difference is the highest in lot 6 unlike mean and maximum density difference. QC/QA

minimum density difference is the smallest in lot 2 like mean density difference, but unlike

maximum density difference.

FIGURE 2.32 Summary of QC and QA Maximum Density Comparison

FIGURE 2.33 QC and QA Minimum Density Comparison for US-81 in District 2

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

1 2 3 4 5 6 7 8 9 10 11 12

Freq

uen

cy

(N

um

ber)

Project Number

Higher Lower

88.0

88.5

89.0

89.5

90.0

90.5

91.0

91.5

92.0

92.5

1 2 3 4 5 6 7 8 9 10 11 12

Min

imu

m D

ensi

ty (%

)

Lot Number

505136222: U81 (Ottawa)-PG 58-28, SR-19A, 3.5 in

QC QA

33

Figure 2.34 shows QC/QA minimum density comparison for K-383 route in Decatur

County. It has the highest number of lots of all projects in District 3 in this study. QC minimum

density is higher than QA mean density in 12 out of 16 lots, which is 75% like maximum density

comparison. QC/QA minimum density difference is the highest in lot 8 unlike mean and

maximum density difference. The smallest QC/QA minimum density difference is observed in

lot 2. Lot 1 has the lowest QC/QA minimum density like the mean and maximum density

comparison. QC/QA minimum density difference may not be significant from a practical point of

view.

FIGURE 2.34 QC and QA Minimum Density Comparison for K-383 in District 3

Figure 2.35 shows QC/QA minimum density comparison for US-54 route in Kingman

County. It has the highest number of lots of all projects in District 5 in this study. QC minimum

density is higher than QA minimum density in three out of 9 lots, which is about 33% unlike

mean and maximum density comparison. QC/QA minimum density difference is the highest in

lot 1 like maximum density difference comparison, but unlike mean difference comparison. The

84.0

85.0

86.0

87.0

88.0

89.0

90.0

91.0

92.0

93.0

94.0

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

Min

imu

m D

ensi

ty (%

)

Lot Number


QC QA

34

smallest QC/QA minimum density difference has been observed in lot 6 unlike maximum

density difference. Lot 1 has the smallest QC/QA minimum density.


Figure 2.36 shows QC/QA minimum density comparison for US-54 route in Seward

County. It has the highest number of lots out of all projects in District 6 in this study. There were

no QA data for lots 1 and 2. QC minimum density is higher than QA mean density in six out of

11 lots that had both QC and QA data, which is about 55%. The minimum density difference is

the highest in lot 5 and the smallest in lot 13, respectively. Lot 9 has the smallest QC minimum

density whereas lot 5 has the smallest QA minimum density.

Figure 2.37 shows summary of QC/QA minimum density comparison for 12 projects in

four KDOT districts. QC minimum density was compared to QA minimum density for a lot.

Frequency distribution for each project based on a lot-by-lot minimum density comparison has

been plotted. QC minimum density is higher than QA minimum density for all projects for the

majority of lots except project 9. Project 9 is US-54 route in Kingman County, located in District

5. QC minimum density is higher than QA minimum density in all lots for project 8. Project 8 is

85.0

86.0

87.0

88.0

89.0

90.0

91.0

92.0

93.0

94.0

1 2 3 4 5 6 7 8 9

Min

imu

m D

ensi

ty (%

)

Lot Number


QC QA

35

US-50 route in Reno County, located in District 5. In general, lot-by-lot comparison shows that

QC minimum density is higher than QA minimum density. One reason may be due to more data

points taken by contractor, in which lower values can be expected.


FIGURE 2.37 Summary of QC and QA Minimum Density Comparison

89.0

89.5

90.0

90.5

91.0

91.5

92.0

92.5

93.0

93.5

94.0

94.5

1 2 3 4 5 6 7 8 9 10 11 12 13

Min

imu

m D

ensi

ty

(%)

Lot Number

506126676: U54 (Seward)-PG 70-22, SM-12.5A, 2 in

QC QA

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

1 2 3 4 5 6 7 8 9 10 11 12

Fre

qu

ency

(N

um

ber

)

Project Number

Higher Lower

36

2.5.4 Standard Deviation (STD) for Density Comparison

Figure 2.38 shows QC/QA density comparison for US-81 route in Ottawa County. It has

the highest number of lots of all projects in District 2 in this study. QC STD density is higher

than QA STD density in six out of 12 lots, which is 50%. The STD difference is the highest in lot

6 and the smallest in lot 2, respectively. Again, the contractor STD is expected to be larger than

the KDOT STD because the number of tests done per lot is higher for the contractor.

FIGURE 2.38 QC and QA STD Density Comparison for US-81 in District 2

Figure 2.39 shows QC/QA STD density comparison for K-383 route in Decatur County.

It has the highest number of lots out of all projects in District 3 in this study. QC STD density is

higher than QA STD density in 3 out of 16 lots, which is about 19%. The STD difference is the

highest in lot 8 and the smallest in lot 5, respectively. Lot 9 has the smallest QC STD density

whereas lot 3 has the smallest QA STD density.

Figure 2.40 shows QC/QA STD density comparison for US-54 route in Kingman County.

It has the highest number of lots out of all projects in District 5 in this study. QC STD density is

higher than QA STD density in five out of nine lots, which is about 56%. The STD difference is

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1 2 3 4 5 6 7 8 9 10 11 12

ST

D (%

)

Lot Number

505136222: U81 (Ottawa)-PG 58-28, SR-19A, 3.5 in

QC QA

37

the largest in lot 1 and the smallest in lot 3, respectively. Lot 8 has the smallest QC STD density

whereas lot 4 has the smallest QA STD density.

FIGURE 2.39 QC and QA STD Density Comparison for K-383 in District 3


0.0

0.5

1.0

1.5

2.0

2.5

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

ST

D

(%)

Lot Number


QC QA

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1 2 3 4 5 6 7 8 9

ST

D (%

)

Lot Number


QC QA

38

Figure 2.41 shows QC/QA STD density comparison for US-54 route in Seward County. It

has the highest number of lots out of all projects in District 6 in this study. There were no QA

data for lots 1 and 2. QC STD density is higher than QA STD density in one out of 11 lots that

had both QC and QA data, which is about 9%. The STD difference is the largest in lot 6 and the

smallest in lot 13, respectively. Lot 11 has the smallest QC STD density whereas lot 10 has the

lowest QA STD density.


Figure 2.42 shows summary of QC/QA STD density comparison for 12 projects in four

KDOT districts. QC STD density was compared to QA STD density. QC STD density is higher

than QA STD density for only three projects out of 12, which is 25%. The three projects in which

QC STD density is higher than QA STD density in most of the cases are projects 2, 9, and 10.

Project 2 is US-81 route in Ottawa County, which is located in District 2. Projects 9 and 10 are

located in District 6. Project 9 is US-54 route in Seward County whereas project 10 is US-50

route in Finney County. In general, QC STD density is lower than QA STD density.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

1 2 3 4 5 6 7 8 9 10 11 12 13

ST

D (%

)

Lot Number

506126676: U54 (Seward)-PG 70-22, SM-12.5A, 2 in

QC QA

39

2.5.5 Coefficient of Variation (COV) for Density Comparison

Figure 2.43 shows QC/QA COV density comparison for US-81 route in Ottawa County.

It has the highest number of lots of all projects in District 2 in this study. QC COV density is

higher than QA COV density in six out of 12 lots, which is 50% like STD comparison. The COV

difference is the highest in lot 6 and the smallest in lot 2, respectively like STD.

FIGURE 2.42 Summary of QC and QA STD Density Comparison

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

1 2 3 4 5 6 7 8 9 10 11 12

Fre

qu

ency

(N

um

ber

)

Project Number

Higher Lower

40

FIGURE 2.43 QC and QA COV Density Comparison for US-81 in District 2

Figure 2.44 shows QC/QA COV density comparison for K-383 route in Decatur County.

It has the highest number of lots of all projects in District 3 in this study. QC COV density is

higher than QA COV density in 2 out of 16 lots, which is about 13%. The COV difference is the

highest in lot 8 and the smallest in lot 5, respectively like STD comparison. Lot 9 has the

smallest QC COV density whereas lot 3 has the smallest QA COV density like STD comparison.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1 2 3 4 5 6 7 8 9 10 11 12

CO

V (%

)

Lot Number

505136222: U81 (Ottawa)-PG 58-28, SR-19A, 3.5 in

QC QA

41

FIGURE 2.44 QC and QA COV Density Comparison for K-383 in District 3

Figure 2.45 shows QC/QA COV density comparison for US-54 route in Kingman

County. It has the highest number of lots out of all projects in District 5 in this study. QC COV

density is higher than QA COV density in five out of nine lots, which is about 56% like STD

comparison. The COV difference is the largest in lot 1 and the smallest in lot 9, respectively. Lot

4 has the smallest QC COV density whereas lot 8 has the smallest QA COV density.

Figure 2.46 shows QC/QA COV density comparison for US-54 route in Seward County.

It has the highest number of lots of all projects in District 6 in this study. There were no QA data

for lots 1 and 2. QC COV density is higher than QA COV density in one out of 11 lots that had

both QC and QA data, which is about 9% like STD comparison. The COV difference is the

largest in lot 6 and the smallest in lot 13, respectively. Lot 11 has the smallest QC COV density

whereas lot 10 has the lowest QA COV density.

0.0

0.5

1.0

1.5

2.0

2.5

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

CO

V (%

)

Lot Number


QC QA

42



Figure 2.47 shows summary of QC/QA COV density comparison for 12 projects in four

KDOT districts. QC COV density was compared to QA COV density. QC COV density is higher

than QA COV density for only three projects out of 12, which is 25%. The three projects in

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1 2 3 4 5 6 7 8 9

CO

V (%

)

Lot Number


QC QA

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

1 2 3 4 5 6 7 8 9 10 11 12 13

CO

V (%

)

Lot Number

506126676: U54 (Seward)-PG 70-22, SM-12.5A, 2 in

QC QA

43

which QC COV density is higher than QA COV density in most of the cases are projects 2, 9,

and 10. Project 2 is US-81 route in Ottawa County, which is located in District 2. Projects 9 and

10 are located in District 6. Project 9 is US-54 route in Seward County whereas project 10 is US-

50 route in Finney County. QC COV is lower than QA COV in all the lots for project 11. Project

11 is K-27 in Stanton County, located in District 6. In general, QC COV density is lower than

QA COV density.

FIGURE 2.47 Summary of QC and QA COV density comparison.

2.6 Comparison of Means

The FHWA technical advisory recommends using the F & t statistical procedures to

compare both variance and means of two data sets. The F-test compares the variances of two data

sets. The objective of this test is to determine whether the differences in the variability of the

contractor’s tests and the department tests are greater than what might be expected if they came

from the same population. On the other hand, the t-test compares the means of two data sets to

assess whether they are statistically different (Elseifi et al. 2009). KDOT uses F-test to determine

equality of variance and then t-test to compare QC and QA means.

0.0

2.0

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1 2 3 4 5 6 7 8 9 10 11 12

Fre

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ency

(N

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)

Project Number

Higher Lower

44

The term analysis of variance (ANOVA) describes a group of inferential statistical tests

whereas a t-test is used in statistics to determine if the means of two groups differ significantly.

ANOVA evaluates the null hypothesis that in a set of k independent samples (where k ≥ 2), all k

samples are drawn from the same population, with the alternate hypothesis that at least two of

the samples are drawn from populations with different mean values. The test statistic computed

is based on the F-distribution. In the case of comparing two means, which is the case for QC and

QA, the t- and the F-tests are equivalent when variances are equal.

The F-test in ANOVA can signify that not all the means of the levels of the classification

variable are the same, but it cannot indicate which means differ from which other means.

Comparison methods for means provide more detailed information about the differences among

the means. Four comparison methods for means have been used in this study.

2.6.1 Fisher’s Least Significant Difference (LSD) Test

Multiple t-tests are used to compare pairs of means. Fisher’s LSD tests is the most

powerful for finding differences between pairs of means since it does not adjust the significance

level needed to achieve significance in order to account for multiple testing. As a result, it has the

greatest chance of resulting in one or more Type I errors.

2.6.2 Tukey’s Honestly Significant Difference (HSD) Test

This test is generally recommended when a researcher plans to make all possible pair-

wise comparisons since it controls the Type I error rate so that it will not exceed the significance

level value pre-specified in the analysis. It maintains an acceptable significance level without an

excessive loss of power.

2.6.3 Student-Newman-Keuls (SNK) Test

This test is similar to and/or more powerful than Tukey’s HSD. However, it does not control

experiment-wise error rate at significance level.

45

2.6.4 Scheffe’s Test

This test is extremely flexible, allowing for any type of comparison. This increased

versatility results in less power to detect differences between pairs of groups. It is the most

conservative of the unplanned comparison procedures. The test specifies a fixed value of

significance level which does not depend on the number of comparisons conducted.

2.6.5 Lot-Wise Means Comparison

Lot-wise means comparison was carried out using four means comparison methods at

three different significant levels. Significance difference was summarized into frequency

distribution for both Superpave and PCC pavements. Quality characteristics for Superpave

pavements include QC air voids, QC smoothness, and QC and QA density. Lot-wise means

comparison was carried out for PCC QC strength and thickness.

2.6.5.1 Superpave Pavements

Figure 2.48 shows lot-wise comparison of QC air void means. Student-Newman-Keuls

(SNK) and Tukey’s Honestly Significant Difference (HSD) show the same results at all

significance levels. There is no significant difference between lot means in most cases for QC air

at all significance levels and for all methods except LSD.

Figure 2.49 shows lot-wise comparison of QC smoothness means. Student-Newman-

Keuls (SNK) and Tukey’s Honestly Significant Difference (HSD) show the same results at all

significance levels. There is significant difference between lot means in most cases at all

significance levels and for all methods for QC smoothness except Scheffe method at 1%

significance level.

Figure 2.50 shows lot-wise comparison of QC density means. Student-Newman-Keuls


significance levels. There is significant difference between lot means in most cases at all

significance levels and for all methods. Scheffe method is the only method that does not show

significant difference between means for some cases at 2.5% and 5%, respectively.

Figure 2.51 shows lot-wise comparison of QA density means. Student-Newman-Keuls


46

significance levels. There is no significant difference between lot means in most cases at all

significance levels and for all methods except LSD. All methods show the same result at 5%

significant level except LSD.

FIGURE 2.48 Lot-Wise Means Comparison for QC Air Voids

FIGURE 2.49 Lot-Wise Mean Comparison for QC Smoothness

0

5

10

15

20

25

30

35

40

45

50

No Yes No Yes No Yes

1 2.5 5

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Similar at Significance Level (%)?

LSD SNK HSD Scheffe

0

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25


1 2.5 5

Fre

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cy (

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mb

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LSD SNK HSD Scheffe

47

FIGURE 2.50 Lot-Wise Means Comparison for QC Density

FIGURE 2.51 Lot-Wise Means Comparison for QA Density

0

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1 2.5 5

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LSD SNK HSD Scheffe

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LSD SNK HSD Scheffe

48

2.6.5.2 PCC Pavements

Figure 2.52 shows lot-wise means comparison for QC PCC strength data. SNK and HSD

show the same results at all significance levels. There is a significant difference between lot

means in most cases using all methods at all significance levels except Scheffe method. This

confirms that Scheffe method is the weakest in detecting significant differences.

Figure 2.53 shows lot-wise means comparison for QC PCC thickness data. SNK and

HSD show the same results at all significant levels. There is significant difference between lot

means in most cases using all methods at all significance levels except using Scheffe’s method.

This confirms that Scheffe method is the weakest in detecting significant differences.

Lot-wise comparison shows that QC and QA means are significantly different in most

cases. As a result, QC/QA comparison should be considered lot-wise instead of KDOT’s current

procedure that combines data from five successive lots for air voids of Superpave pavements and

strength and thickness of PCC pavements. More sublot data may be taken for each lot so that

enough data can be obtained for statistical analysis. Ten QC readings and five QA readings per

lot, similar to current Superpave density data, will be enough for more robust statistical analysis.

This result confirms the study by Benson (1995). It was suggested that within practical

limitations of the type of job, lot size could be expanded tenfold to encompass an entire week’s

production. There would be considerable benefits in terms of reduced staff and equipment

inventory if larger lot sizes are implemented. The increase in risk to buyers and sellers as a result

of slightly higher within-lot variability are not unreasonable.

2.6.6 Sublot-Wise Means Comparison


Figure 2.54 shows sublot-wise mean comparison for QC/QA air voids using four mean

comparison methods at three different significance levels. Sublot-wise QC/QA comparison for

air voids has been done using four QC sublot readings and QA reading as the fifth sub-lot

reading in each lot. Sheffe method shows that there is no significant difference between the

sublot means of QC/QA air voids at 1% significance level. The result shows that significant

difference using LSD method clearly increases with an increase in significance level.

49

FIGURE 2.52 Lot-Wise Means Comparison for PCC Strength

FIGURE 2.53 Lot-Wise Means Comparison for PCC Thickness

0

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1 2.5 5

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LSD SNK HSD Scheffe

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16


1 2.5 5

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LSD SNK HSD Scheffe

50

FIGURE 2.54 Sublot-Wise Means Comparison for QC/QA Air Voids

Figure 2.55 shows sublot-wise mean comparison for QC density using four mean

comparison methods at three different significance levels. There is no significant difference

using all methods except LSD for QC density. The result shows that significant difference using

LSD method clearly increases with an increase in significance level.

Figure 2.56 shows sublot-wise mean comparison for QA density using four mean


using all methods at all significant levels.

Figure 2.57 shows sublot-wise mean comparison for QC/QA using four mean comparison

methods at three different significance levels. Sublot-wise QC/QA density analysis has been

done using 10 QC sublot data and five QA sublot data in each lot. There is no significant

difference using all methods except LSD for QC/QA density. The result shows that significant

difference using LSD method clearly increases with an increase in significance level.

0

10

20

30

40

50

60


1 2.5 5

Fre

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cy (

Nu

mb

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LSD SNK HSD Scheffe

51

FIGURE 2.55 Sublot-Wise Means Comparison for QC Density

FIGURE 2.56 Sublot-Wise Means Comparison for QA Density

0

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1 2.5 5

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LSD SNK HSD Scheffe

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1 2.5 5

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LSD SNK HSD Scheffe

52

FIGURE 2.57 Sublot-Wise Means Comparison for QC/QA Density


Figure 2.58 shows sublot-wise mean comparison for QC PCC strength using four mean

comparison methods at three different significance levels. The results show that significant

difference using all methods increases with an increase in significance level for PCC strength.

All methods, except LSD show no significant difference at 1% significant level.

Figure 2.59 shows sublot-wise mean comparison for QC PCC thickness using four mean


between sublot means using all methods at all significance levels for PCC thickness except LSD

at 5% significance level. This shows that LSD is the most powerful method to detect significant

differences and significant difference increases with an increase in significance level.

0

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12

14


1 2.5 5

Fre

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LSD SNK HSD Scheffe

53

FIGURE 2.58 Sublot-Wise Means Comparison for QC PCC Strength

FIGURE 2.59 Sublot-Wise Means Comparison for QC PCC Thickness

0

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14


1 2.5 5

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LSD SNK HSD Scheffe

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LSD SNK HSD Scheffe

54

2.6.7 Sublot-Wise Comparison of Means

The order of sublot means has been investigated for any trend using QC/QA air voids,

QA density, QC PCC strength and thickness.


The rank varies from 1 (largest mean) to 5 (smallest mean) corresponding to five sublots

in a lot for QC/QA air voids. QA has been taken as sublot 5 in this analysis. Quality assurance

for air voids and QC sublot 1 show for most of the time, the largest and smallest mean,

respectively, as shown in Figure 2.60.

FIGURE 2.60 Sublot-Wise Comparison of Mean Magnitude for QC/QA Air Void

The rank varies from 1 (largest mean) to 5 (smallest mean) corresponding to five sublots

in a lot for QA density. Figure 2.61 shows in most cases, sublots 2 and 3 have the largest and

0

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8

10

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16

18

20

1 2 3 4 5

Fre

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Rank

QC Sublot 1 QC Sublot 2 QC Sublot 3 QC Sublot 4 QA

55

smallest mean, respectively, for QA density. The results show that sublots 1 and 5 are neither the

smallest nor the largest consistently.

FIGURE 2.61 Sublot-Wise Comparison of Mean Magnitude for QA Density


The rank varies from 1 (largest mean) to 4 (smallest mean) corresponding to 4 sublots in

a lot for QC PCC strength. Sublots 1 and 4 have the largest mean in most and fewest cases,

respectively, for QC PCC strength as shown in Figure 2.62.

The rank varies from 1 (largest mean) to 4 (smallest mean) corresponding to 4 sublots in

a lot for QC PCC thickness. Figure 2.63 shows that sublots 3 and 4 have the largest mean with

the same frequency. Sublot 3 has the smallest mean in most cases. These results do not show any

specific trend that the first or the last sublot reading is the largest or the smallest.

0

1

2

3

4

5

6

1 2 3 4 5

Fre

qu

en

cy (

Nu

mb

er)

Rank

Sublot 1 Sublot 2 Sublot 3 Sublot 4 Sublot 5

56

FIGURE 2.62 Sublot-Wise Comparison of Mean Magnitude for QC PCC Strength

FIGURE 2.63 Sublot-Wise Comparison of Mean Magnitude for QC PCC Thickness

0

1

2

3

4

5

6

7

1 2 3 4

Fre

qu

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cy (

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Rank

Sublot 1 Sublot 2 Sublot 3 Sublot 4

0

1

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1 2 3 4

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Sublot 1 Sublot 2 Sublot 3 Sublot 4

57

2.6.8 Effect of Significance Level on Pay Adjustments

Effect of significance level on pay adjustment was investigated using QC/QA air voids

and density data. One project was selected from each district for this investigation. The Excel

spreadsheet of KDOT (2009 version) was used for three different significance levels. The results

are presented for QC/QA air voids and density separately.

2.6.8.1 QC/QA Air Voids

Table 2.5 shows pay adjustment for QC/QA air voids corresponding to the three different

significance levels. One large project from each district was selected for the investigation.

Project size in terms of tonnage varies from 185,329 in District 1 to 222,276 in District 6.

TABLE 2.5

Pay Adjustment for QC/QA Air Voids

District Size (ton) Significant Level

1% 2.50% 5%

1 185,329 67,305 67,305 62,505

2 190,229 65,743 58,073 58,073

3 222,000 132,903 132,903 132,903

4 204,750 4,800 4,800 4,800

5 214,581 39,400 39,400 39,400

6 222,276 222,563 222,563 222,563

Figure 2.64 shows pay adjustment for QC/QA air voids at three different significance

levels in each of KDOT’s six districts. There is difference in pay adjustment only in Districts 1

and 2. Pay adjustments are the same at 1% and 2.5% significance levels for the project in District

1. Pay adjustments are equal at 2.5% and 5% significance levels for the project in District 2.

2.6.8.2 QC/QA Density

Table 2.6 shows pay adjustment for QC/QA density corresponding to the three different

significance levels. Some lots from a project from each district were selected for the

investigation.

58

0

50000

100000

150000

200000

250000

1 2 3 4 5 6

Air

Void

s P

ay A

dju

stm

en

t (d

oll

ar)

District

1% 2.50% 5%

FIGURE 2.64 Pay Adjustment for QC/QA Air Voids

TABLE 2.6

Pay Adjustment for QC/QA Density

District Size (ton) Significant Level

1% 2.50% 5%

1 15970 20949 -1344 -16562

2 21223 22914 22914 22914

3 48429 56591 56591 56591

4 30722 36160 21443 14969

5 37382 53397 53397 53318

6 36257 52112 52112 52112

Figure 2.65 shows pay adjustment for QC/QA density at three different significance

levels in each of KDOT’s six districts. There is difference in pay adjustment only in Districts 1

and 4. Pay adjustments are different at all significant levels in both districts.

59

Even though the differences in pay adjustments at 1% and 2.5% are not significant for the

selected projects or sample lots, it can be significant amount of money when many large projects

are considered. Currently KDOT uses 1% significance level and it is difficult to find significant

difference at this level. It is recommended that 2.5% significance level be used as a compromise

between 1 and 5% at all significance levels for both contractors and KDOT.

-30000

-20000

-10000

0

10000

20000

30000

40000

50000

60000

70000

1 2 3 4 5 6

Den

sit

y

Pay A

dju

stm

en

t (d

oll

ar)

District

1% 2.50% 5%

FIGURE 2.65 Pay Adjustment for QC/QA Density

2.7 F & t Tests Using Superpave Pavement Density Data

Superpave pavement density data was used to investigate whether F-test can be used to

determine significant differences between QC and QA data instead of determining the equality of

variance only. It is to be noted that F and t tests give the same result when two means are

compared and equal variance assumption is valid. Since KDOT deals with two means i.e. QC

and QA, the first part is fulfilled. The main emphasis is when equal variance assumption is not

valid. Students’ t-test has been done for both equal variance and unequal variance cases.

60

Figure 2.66 shows the comparison of t-test results when the variances are equal.

However, computation was done for both equal variances and assumed unequal variance cases.

Again, this test was repeated when the variances were unequal. In this case, computation was

done for unequal variance as well as assuming equal variances. For equal variance case, in 72

cases, there was no significant difference between QC and QA data. However, significant

difference was observed in 15 cases.

FIGURE 2.66 QC/QA Density Significant Difference Test

When variances are unequal, assumptions of equal and unequal variances yield the same

result. Figure 2.67 shows the discrepancy in test results when equal and unequal variances are

used. When the variances are unequal, the discrepancy in test results is the same which shows the

risk to KDOT and the contractor is equal. Overall, the probability of getting wrong results while

using F-test is about 2% (only 2 out of 104 cases were wrong). This shows that F-test can be

used to determine significant difference in means.

0

10

20

30

40

50

60

70

80

Equal Assume Unequal Assume Equal Unequal

Equal Unequal

Fre

qu

en

cy (

Nu

mb

er)

Variance

QC and QA Different QC and QA Similar

61

FIGURE 2.67 Discrepancy of QC/QA Significant Difference Test for Equal and Unequal Variance

2.8 Comparison of Design and Actual Asphalt Content

Figure 2.68 shows comparison of actual and design AC content for US-75 route in

District 1. It is typical case in which actual asphalt content is lower than the design asphalt

content in all sublots.


District 3. It is typical case in which actual asphalt content is higher than the design asphalt

content in all sublots.


Cowley County. This is typical of the situation when actual AC content is lower or higher than

design AC content. In almost all cases, actual asphalt content is lower than the design asphalt

content.

Figure 2.71 shows the summary of actual asphalt content as compared to the design

asphalt content. Actual asphalt content is higher than the design asphalt content in very few

0

1

2

3

4

5

6

Equal Assume Unequal Assume Equal Unequal

Equal Unequal

Fre

qu

en

cy (

Nu

mb

er)

Variance

QC and QA Different QC and QA Similar

62

cases. Actual asphalt content is lower or higher than design in most of the cases, but it is lower

than the actual most points as shown in Figure 2.70.

FIGURE 2.68 Typical Comparisons of Design and Actual AC When Actual Is Lower Than Design

FIGURE 2.69 Typical Comparison of Design and Actual AC When Actual is Lower Than Design

5.20

5.40

5.60

5.80

1 2 3 4 5 6 7 8 9 10 11 12

Bin

der

Co

nte

nt

(%)

Number of Data Points

503082011: US75 (Brown), PG 64-22, SM-19A

Pb-Actual Pb-Design

4.00

4.50

5.00

5.50

6.00

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Bin

de

r C

on

ten

t (%

)


505112113: U36 (Norton), PG 64-28, SM-19A

Pb-Actual Pb-Design

63

FIGURE 2.70 Typical Comparison of Design and Actual AC When Actual is Lower and Higher Than Design

FIGURE 2.71 Summary of Actual and Design AC Content Comparison

4.50

4.70

4.90

5.10

5.30

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Bin

der

Co

nte

nt

(%)


503022225: U77 (Cowley), PG 64-22, SM-19A

Pb-Actual Pb-Design

0

10

20

30

40

50

60

Lower Higher Lower and Higher

Freq

uen

cy (

Nu

mb

er)

Actual AC Content as Compared to Design AC Content

64

It is recommended that the contractor provide mixes that have the average asphalt content

as that in the approved design. Performance tests used to determine mix suitability (Lottman test,

etc.) are performed at the design asphalt content. Therefore, production should provide mixes

with the same properties. Incentives and/or penalties could be applied accordingly. The second

option is to increase minimum VMA if additional asphalt is needed based on the performance of

the pavements.

65

Chapter 3: Practical Performance Model and Composite Index

3.1 General

Burati et al. (2003) concluded that percent within limit (PWL) is well suited as a

statistical measure of quality since it has been well studied, statistically unbiased, suitable for

both normal and distribution-free (attributes) applications, and works equally well for single-

sided or double-sided specifications. Percent within limit (PWL) has been used to develop

composite and practical performance models. Acceptable quality level (AQL) has been taken as

90 percent within limit for different quality characteristics for both Superpave and PCC

pavements whereas different rejectable quality levels (RQL) have been used partly to investigate

the effect of different RQL on the models and partly based on the effect of each quality

characteristics on the performance of the pavement. The expected life (EL) was taken as 10 and

20 years for Superpave and PCC pavements, respectively when PWL=90 for all quality

characteristics whereas EL was taken as 5 and 10 years when one of the quality characteristics is

at RQL level. These values can be updated based on actual performance data and experience of

the agency. Practical performance models and composite index were developed for Superpave

and PCC pavements using different quality characteristics.

3.2 Practical Performance Model

One of the first steps in developing a mathematical model is the choice of model form.

Since most quality characteristics have points of diminishing returns, a model with an “S” shape

may be appropriate (Weed 2006). Practical performance model of the form shown by Equation

3.1 has been developed. Expected life (EL) was used as a measure of performance (dependent

variable) whereas different quality characteristics (variables) for both Superpave and PCC

pavements were used as independent variables. Different shape factors (C) were assumed and

simultaneous equations were solved using Excel for the model coefficients.

Equation 3.1

66


Practical performance models that include two, three, four, and five quality

characteristics were developed. Microsoft excel was used to solve simultaneous equations in

order get model coefficients.

3.2.1.1 Two Quality Characteristics

Practical performance model (PPM) that includes air voids and in-place density was

developed using data in Table 3.1. Different values of shape factors were tried. The model was

checked whether it returns precisely the values used to develop it. It was also checked at extreme

values (PWL=100 and PWL=0), and examined how extra quality in some variables can offset the

deficient quality in other variables while still producing design life of 10 years.

TABLE 3.1

Data for PPM for Superpave Pavements (Two Variables, V=2)

Percent Within Limit (PWL) for Various Quality Measures Expected Life

(years) Air Voids (VA) Density (DEN)

90 90 10.0

50 90 5.0

90 40 5.0

3.2.1.1.1 Checking the Model (Shape Factor, C=1)

The model was checked to make sure that it returns precisely the values used to develop

it. Table 3.2 shows that the model returns the values used to develop it, which is shown in Table

3.1.

TABLE 3.2

Test of Derivation of PPM for Superpave Pavements (V=2 and C=1)



90 90 10.0

50 90 5.0

90 40 5.0

67

A second test is to check at the extremes, an area in which many models break down. The

extremes in this case occur when the individual PWL values are all either 100 or zero percent.

These results are presented in Table 3.3. When PWL is 100 in both quality characteristics, the

model predicts that the typical expected life of 10 years will be extended to approximately 14

years. It certainly falls within the experience of many agencies. At the other extreme, the model

predicts an expected life less than a year. The model predicts an expected life of 3.4 years when

PWL is 100 and 0 for air voids and in-place density, respectively. When PWL is 0 and 100 for air

voids and in-place density, respectively, the model predicts an expected life of 2.4 years. At this

stage, there is nothing to indicate the model is unsatisfactory, but several additional tests are

required.

TABLE 3.3

Test of Extremes of PPM for Superpave Pavements (V=2 and C=1)



100 100 13.7

100 0 3.4

0 100 2.4

0 0 0.6

The third test is designed to examine how extra quality in some characteristics can offset

deficient quality in others while still producing the design life of 10 years. This is an inherent

feature in most design methods, and is believed to be an appropriate feature in any model of

multiple quality characteristics. However, there would be concern if the model produced a

sufficiently low level of quality in any individual characteristic that did not seem consistent with

achieving the intended design life, even though the other characteristics were at excellent levels.

Table 3.4 may not suggest that the model may have such a shortcoming, but other shape

factors: 0.15, 0.25, 0.5, 0.75, 1.5, and 2 were considered to for the consistency of the models

with other models which include more than two quality characteristics. Results from shape factor

0.5 are presented since it was found more reasonable.

68

TABLE 3.4 Test of Offsetting Property of PPM for Superpave Pavements (V=2 and C=1)



90 90 10.0

82 100 10.0

100 77.5 10.0

3.2.1.1.2 Checking the Model (Shape Factor, C=0.5)

As before, the first test of this model is to check that it correctly returns the values of

expected life that were used to derive it, which it does. The next check is to repeat the series of

tests shown in Table 3.4. The equivalent results, obtained with the revised model, are presented

in Table 3.5. The values in Table 3.5 are not that much different from those in Table 3.4, but

shape factor of 0.5 was used in the final model for consistency with other models that include

more than two variables.

TABLE 3.5

Test of Offsetting Property of PPM for Superpave Pavements (V=2 and C=0.5)



90 90 10.0

82.5 100 10.0

100 78 10.0

The next test is to revisit Table 3.3 to check the values obtained at the extremes of PWL =

100 and PWL = 0. These are presented in Table 3.6 where it is seen that the inclusion of the

exponential “C” term has given the revised model a “diminished returns” effect by reducing the

maximum predicted life from the previous value of about 14 years to a value of 13 years.

Further test was conducted. Both quality measures decline together. Table 3.7 shows a

very logical progression as the results range from the maximum expected life of about 13 years

for excellent quality down to the minimum of less than a year for extremely poor quality. It is

69

believed that most pavement engineers would consider this to be reasonably representative of

field experience.

TABLE 3.6

Test of Extremes of PPM for Superpave Pavements (V=2 and C=0.5)



100 100 13.0

100 0 1.4

0 100 0.7

0 0 0.1

TABLE 3.7

Test of Progressively Poorer Quality for Superpave Pavements (V=2 and C=0.5)



100 100 13.0

95 95 11.4

90 90 10.0

85 85 8.7

80 80 7.6

75 75 6.6

70 70 5.7

65 65 4.9

60 60 4.1

55 55 3.5

50 50 2.9

45 45 2.5

40 40 2.0

35 35 1.6

30 30 1.3

25 25 1.0

20 20 0.8

15 15 0.6

10 10 0.4

5 5 0.3

0 0 0.1

70

The final PPM is shown by Equation 3.2. The model is used to better understand the

consequences of either exceeding or falling short of the desired quality levels, and to provide a

logical and defensible basis for the adjusted pay schedules that are an integral part of PPM. This

model can be validated and/or improved based on Hamburg wheel tests at Kansas State

University or KDOT’s experience or actual performance data or a combination.

Equation 3.2

3.2.1.2 Three Quality Characteristics

Practical performance model that includes three quality characteristics, air voids, in-place

density, and Smoothness, was developed using data in Table 3.8. Smoothness was included in the

model development if in case KDOT wants to include smoothness in the future. However,

previous studies have recommended not including smoothness with other quality characteristics

since the effect of initial smoothness has been assumed to be independent of the effects of the

other variables (Weed 2000). Different values of shape factors were tried. The model was

checked whether it returns precisely the values used to develop it. It was also checked at extreme

values (PWL=100 and PWL=0), and examined how extra quality in some variables can offset the


TABLE 3.8

Data for PPM for Superpave Pavements (Three Variables, V=3)


(years) Air Voids (VA) Density (DEN) Smoothness (SM)

90 90 90 10.0

50 90 90 5.0

90 40 90 5.0

90 90 35 5.0

71



it. Table 3.9 shows that the model returns the values used to develop it, which are shown in Table

3.8.



These results are presented in Table 3.10. When PWL= 100 in all three quality characteristic, the

model predicts that the typical expected life of 10 years will be extended to 15.5 years. This is an

appreciable increase, but it certainly falls within the experience of many agencies. At the other

extreme, the model predicts an expected life less than a year. The model predicts about a year

when one of the quality characteristics has PWL=100 and the rest have PWL=0. Although not a

frequent occurrence, most highway agencies have experienced this result at one time or another.

At this stage, there is nothing to indicate the model is unsatisfactory, but several additional tests

are required. TABLE 3.9




90 90 90 10.0

50 90 90 5.0

90 40 90 5.0

90 90 35 5.0

TABLE 3.10




100 100 100 15.5

100 0 0 1.1

0 100 0 0.8

0 0 100 0.7

0 0 0 0.2

72







Table 3.11 suggests that the model may have such a shortcoming. For example, if

PWLVA=PWLDEN=100, and PWLSM=65.5, the model predicts a design life of 10 years. This

finding has raised doubts about the efficacy of the model when shape factor is one. It is now

appropriate to consider other shape factors. Shape factors: 0.15, 0.25, 0.5, 0.75, 1.5, and 2 were

considered. Results from shape factor 0.5 are presented since it was found more reasonable.

TABLE 3.11

Test of Offsetting Property of PPM for Superpave Pavements (V=3 and C=1)



90 90 90 10.0

74.5 100 100 10.0

100 68.5 100 10.0

100 100 65.5 10.0




tests shown in Table 3.11 that led to the rejection of the simpler model. The equivalent results,

obtained with the revised model, are presented in Table 3.12. The values in Table 3.12 seem

more reasonable than those obtained in Table 3.11 even though the values are not far apart.

73

TABLE 3.12 Test of Offsetting Property of PPM for Superpave Pavement (V=3 and C=0.5)



90 90 90 10.0

77 100 100 10.0

100 70 100 10.0

100 100 66 10.0

The next test is to revisit Table 3.10 to check the values obtained at the extremes of PWL

= 100 and PWL = 0. These are presented in Table 3.13 where it is seen that the inclusion of the


maximum predicted life from the previous value of about 15.5 years to value of about 14 years.

TABLE 3.13




100 100 100 14.3

100 0 0 0.2

0 100 0 0.1

0 0 100 0.1

0 0 0 0.0

Further test was conducted. All quality measures decline together. Table 3.14 shows a




field experience.

The final PPM including the three quality characteristics for superpave pavements is

shown by Equation 3.3. The model is used to better understand the consequences of either

exceeding or falling short of the desired quality levels, and to provide a logical and defensible

74

basis for the adjusted pay schedules that are an integral part of PPM. This model can be validated

and/or improved based on Hamburg wheel tests at Kansas State University or KDOT’s

experience or actual performance data or a combination.

Equation 3.3

3.2.1.3 Four Quality Characteristics

Practical performance model (PPM) for Superpave pavements that includes four quality

characteristics, air voids, in-place density, asphalt content, and voids in mineral aggregate

(VMA), was developed using data in Table 3.15. Different values of shape factors were tried.

The model was checked whether it returns precisely the values used to develop it. It was also

checked at extreme values (PWL=100 and PWL=0), and examined how extra quality in some

variables can offset the deficient quality in other variables while still producing design life of 10

years.

TABLE 3.14 Test of Progressively Poorer Quality for Superpave Pavements (V=3 and C=0.5)



100 100 100 14.3

95 95 95 12.0

90 90 90 10.0

85 85 85 8.3

80 80 80 6.8

75 75 75 5.6

70 70 70 4.6

65 65 65 3.7

60 60 60 3.0

55 55 55 2.3

50 50 50 1.8

45 45 45 1.4

40 40 40 1.1

35 35 35 0.8

30 30 30 0.6

25 25 25 0.4

20 20 20 0.3

15 15 15 0.2

10 10 10 0.1

5 5 5 0.1

0 0 0 0.0

75

TABLE 3.15 Data for PPM for Superpave Pavements (Four Variables, V=4)


(years) Air Voids (VA) Density (DEN) Asphalt Content (AC) VMA

90 90 90 90 10

50 90 90 90 5

90 40 90 90 5

90 90 30 90 5

90 90 90 20 5



it. Table 3.16 shows that the model returns the values used to develop it, shown in Table 3.15.

TABLE 3.16




90 90 90 90 10

50 90 90 90 5

90 40 90 90 5

90 90 30 90 5

90 90 90 20 5



These results are presented in Table 3.17. When percent within limit 100 in all four quality

characteristic, the model predicts that the typical expected life of 10 years will be extended to

approximately 17 years. This is an appreciable increase, but it certainly falls within the

experience of many agencies. At the other extreme, the model predicts an expected life of less

than a year. Although not a frequent occurrence, most highway agencies have experienced this

result at one time or another. The model predicts expected life less than a year when one of the

76

quality characteristics has PWL=100 and the rest have PWL=0. At this stage, there is nothing to

indicate the model is unsatisfactory, but several additional tests are required.







Table 3.18 suggests that the model may have such a shortcoming. All four characteristics

may be suspect, but the worst is probably VMA. For example, if PWLVA=PWLDEN= PWLAC

=100, and PWLVMA=47, the model predicts a design life of 10 years. This finding has raised

doubts about the efficacy of the model when shape factor is one. It is now appropriate to consider

other shape factors. Shape factors: 0.15, 0.25, 0.5, 0.75, 1.5, and 2 were considered. Results from

shape factor 0.5 are presented since it was found more reasonable.

TABLE 3.17




100 100 100 100 16.9

100 0 0 0 0.5

0 100 0 0 0.4

0 0 100 0 0.3

0 0 0 100 0.2

0 0 0 0 0.1

77

TABLE 3.18 Test of Offsetting Property of PPM for Superpave Pavements (V=4 and C=1)



90.0 90.0 90.0 90.0 10.0

69.5 100.0 100.0 100.0 10.0

100.0 62.0 100.0 100.0 10.0

100.0 100.0 54.5 100.0 10.0

100.0 100.0 100.0 47 10.0






more acceptable than those obtained in Table 3.18 even though the difference is not that

significant from a practical point of view.

TABLE 3.19




90.0 90.0 90.0 90.0 10.0

73.0 100.0 100.0 100.0 10.0

100.0 65.5 100.0 100.0 10.0

100.0 100.0 57.5 100.0 10.0

100.0 100.0 100.0 48.5 10.0




78

maximum predicted life from the previous value of about 17 years to a possibly more realistic

value of about 15 years.

TABLE 3.20




100.0 100.0 100.0 100.0 15.21

100.0 0.0 0.0 0.0 0.08

0.0 100.0 0.0 0.0 0.04

0.0 0.0 100.0 0.0 0.02

0.0 0.0 0.0 100.0 0.02

0.0 0.0 0.0 0.0 0.00





field experience.

The final performance model is shown by Equation 3.4. The model is used to better

understand the consequences of either exceeding or falling short of the desired quality levels, and

to provide a logical and defensible basis for the adjusted pay schedules that are an integral part of

PPM. This model can be validated and/or improved based on Hamburg wheel tests at Kansas

State University or KDOT’s experience or actual performance data or a combination.

Equation 3.4

79

TABLE 3.21 Test of Progressively Poorer Quality for Superpave Pavements (V=4 and C=0.5)



100.0 100.0 100.0 100.0 15.2

95.0 95.0 95.0 95.0 12.4

90.0 90.0 90.0 90.0 10.0

85.0 85.0 85.0 85.0 8.0

80.0 80.0 80.0 80.0 6.4

75.0 75.0 75.0 75.0 5.1

70.0 70.0 70.0 70.0 4.0

65.0 65.0 65.0 65.0 3.1

60.0 60.0 60.0 60.0 2.4

55.0 55.0 55.0 55.0 1.8

50.0 50.0 50.0 50.0 1.4

45.0 45.0 45.0 45.0 1.0

40.0 40.0 40.0 40.0 0.8

35.0 35.0 35.0 35.0 0.5

30.0 30.0 30.0 30.0 0.4

25.0 25.0 25.0 25.0 0.3

20.0 20.0 20.0 20.0 0.2

15.0 15.0 15.0 15.0 0.1

10.0 10.0 10.0 10.0 0.1

5.0 5.0 5.0 5.0 0.0

0.0 0.0 0.0 0.0 0.0

3.2.1.4 Five Quality Characteristics

Practical performance model (PPM) that includes five variables, air voids, in-place

density, smoothness, asphalt content, and voids in mineral aggregate (VMA), was developed

using data in Table 3.22. Different values of shape factors were tried. The model was checked

whether it returns precisely the values used to develop it. It was also checked at extreme values

(PWL=100 and PWL=0), and examined how extra quality in some variables can offset the


80

TABLE 3.22 Data for PPM for Superpave Pavements (Five Variables, V=5)

Percent Within Limit (PWL) for Various Quality Measures Expected

Life (years) Air Voids

(VA)

Density

(DEN)

Smoothness

(SM)

Asphalt Content

(AC) VMA

90 90 90 90 90 10.0

50 90 90 90 90 5.0

90 40 90 90 90 5.0

90 90 35 90 90 5.0

90 90 90 30 90 5.0

90 90 90 90 20 5.0



it. Table 3.23 shows that the model returns the values used to develop it, Table 3.22.



These results are presented in Table 3.24. When PWL=100 in all five quality characteristics, the

model predicts that the typical expected life of 10 years will be extended to about 19 years. This

is an appreciable increase. At the other extreme, the model predicts an expected life of less than a

year. Although not a frequent occurrence, most highway agencies have experienced this result at

one time or another. At this stage, there is nothing to indicate the model is unsatisfactory, but

several additional tests are required.

81

TABLE 3.23 Test of Derivation of PPM for Superpave Pavements (V=5 and C=1)



(VA)

Density

(DEN)

Smoothness

(SM)

Asphalt Content

(AC) VMA

90 90 90 90 90 10.0

50 90 90 90 90 5.0

90 40 90 90 90 5.0

90 90 35 90 90 5.0

90 90 90 30 90 5.0

90 90 90 90 20 5.0

TABLE 3.24




(VA)

Density

(DEN)

Smoothness

(SM)

Asphalt Content

(AC) VMA

100 100 100 100 100 19.2

100 0 0 0 0 0.2

0 100 0 0 0 0.1

0 0 100 0 0 0.1

0 0 0 100 0 0.1

0 0 0 0 100 0.1

0 0 0 0 0 0.0







82

Table 3.25 suggests that the model may have such a shortcoming. All five characteristics may be

suspect, but the worst is probably VMA in which PWLVMA=34 and PWL=100 for the rest

predicts a design life of 10 years. This finding has raised doubts about the efficacy of the model

when shape factor is one. It is now appropriate to consider other shape factors. Shape factors:

0.15, 0.25, 0.5, 0.75, 1.5, and 2 were considered. Results from shape factor 0.5 are presented

since it was found more reasonable.

TABLE 2.35

Test of Offsetting Property of PPM for Superpave Pavements (V=5 and C=1)



(VA)

Density

(DEN)

Smoothness

(SM)

Asphalt Content

(AC) VMA

90 90 90 90 90 10.0

62.5 100 100 100 100 10.0

100 53.0 100 100 100 10.0

100 100 48.0 100 100 10.0

100 100 100 43.5 100 10.0

100 100 100 100 34.0 10.0






more acceptable than those obtained in Table 3.25.




maximum predicted life from the previous value of about 19 years to a value of about 17 years.



83



field experience.

TABLE 3.26



Life

(years)

Air Voids

(VA)

Density

(DEN)

Smoothness

(SM)

Asphalt Content

(AC) VMA

90 90 90 90 90 10.0

67.0 100 100 100 100 10.0

100 58.5 100 100 100 10.0

100 100 54.0 100 100 10.0

100 100 100 49.0 100 10.0

100 100 100 100 39.0 10.0

TABLE 3.27

Test of Extremes of PPM Models for Superpave Pavements (V=5 and C=0.5)


Life

(years)

Air Voids

(VA)

Density

(DEN)

Smoothness

(SM)

Asphalt Content

(AC) VMA

100 100 100 100 100 16.8

100 0 0 0 0 0.0

0 100 0 0 0 0.0

0 0 100 0 0 0.0

0 0 0 100 0 0.0

0 0 0 0 100 0.0

0 0 0 0 0 0.0

The final performance model is shown by Equation 3.5. The model is used to better

understand the consequences of either exceeding or falling short of the desired quality levels, and

to provide a logical and defensible basis for the adjusted pay schedules that are an integral part of

84

PPM. This model can be validated and/or improved based on Hamburg wheel tests at Kansas

State University or KDOT’s experience or actual performance data or a combination.

Equation 3.5

TABLE 3.28

Test of Progressively Poorer Quality for Superpave Pavements (V=5 and C=0.5)



(VA)

Density

(DEN)

Smoothness

(SM)

Asphalt Content

(AC) VMA

100 100 100 100 100 16.8

95 95 95 95 95 13.0

90 90 90 90 90 10.0

85 85 85 85 85 7.6

80 80 80 80 80 5.8

75 75 75 75 75 4.3

70 70 70 70 70 3.2

65 65 65 65 65 2.4

60 60 60 60 60 1.7

55 55 55 55 55 1.2

50 50 50 50 50 0.9

45 45 45 45 45 0.6

40 40 40 40 40 0.4

35 35 35 35 35 0.3

30 30 30 30 30 0.2

25 25 25 25 25 0.1

20 20 20 20 20 0.1

15 15 15 15 15 0.0

10 10 10 10 10 0.0

5 5 5 5 5 0.0

0 0 0 0 0 0.0

3.2.2 PCC Pavements

Not all individual quality measures are equally suitable for incorporation into a composite

measure. Measures that are best suited are those that jointly affect performance in such a way

that higher quality in one tends to offset deficiencies in the others, within practical limits.

Another requirement is that they be convenient to measure in association with each acceptance

85

lot. The example involving strength and thickness of rigid pavement is well suited (Weed 2000).

Practical performance models that include two and three quality characteristics were developed.

3.2.2.1 Two Quality Characteristics

Practical performance model (PPM) for Portland cement concrete (PCC) pavements that

includes thickness and strength was developed using data in Table 3.29. Different values of shape

factors were tried. The model was checked whether it returns precisely the values used to

develop it. It was also checked at extreme values (PWL=100 and PWL=0), and examined how

extra quality in one variable can offset the deficient quality in other variable while still producing

design life of 20 years.

TABLE 3.29

Data for PPM for PCC Pavements (Two Variables, V=2)


(years) Thickness (TH) Strength (ST)

90 90 20.0

50 90 10.0

90 40 10.0



it. Table 3.30 shows that the model returns the values used to develop it, shown in Table 3.29.

TABLE 3.30

Test of Derivation of PPM for PCC Pavements (V=2 and C=1)



90 90 20.0

50 90 10.0

90 40 10.0

86



These results are presented in Table 3.31. When PWL=100 in both quality characteristic, the


is an appreciable increase, but it certainly falls within the experience of many agencies. At the

other extreme, the model predicts an expected life of about a year. The model predicts an

expected life of about 7 years when PWL for thickness is 100 and PWL for strength is 0. The

model predicts an expected life of about 5 years when PWL for thickness is 0 and PWL for

strength is 100. At this stage, there is nothing to indicate the model is unsatisfactory, but several

additional tests are required.

TABLE 3.31

Test of Extremes of PPM for PCC Pavements (V=2 and C=1)



100 100 27.3

100 0 6.8

0 100 4.8

0 0 1.2

The third test is designed to examine how extra quality in one characteristic can offset

deficient quality in the other while still producing the design life of 20 years. This is an inherent




achieving the intended design life, even though the other characteristic was at excellent levels.

Table 3.32 may not suggest that the model may have such a shortcoming, but other shape factors

were considered for consistency with other models. Shape factors: 0.15, 0.25, 0.5, 0.75, 1.5, and

2 were considered. Results from shape factor 0.5 are presented since it was found more

reasonable.

87

TABLE 3.32 Test of Offsetting Property of PPM for PCC Pavements (V=2 and C=1)



90 90 20.0

82.0 100 20.0

100 77.5 20.0




tests shown in Table 3.32. The equivalent results, obtained with the revised model, are presented

in Table 3.33. The values in Table 3.33 do not seem to differ significantly from those in Table

3.32.

TABLE 3.33

Test of Offsetting Property of PPM for PCC Pavements (V=2 and C=0.5)



90 90 20.0

83 100 20.0

100 78 20.0




maximum predicted life from the previous value of about 27 years to value of about 26 years.

The values are not significantly different from a practical point of view.

88

TABLE 3.34 Test of Extremes of PPM for PCC Pavements (V=2 and C=0.5)



100 100 25.9

100 0 2.9

0 100 1.5

0 0 0.2

Further test was conducted. Both quality measures decline together. Table 2.35 shows a




field experience. TABLE 3.35

Test of Progressively Poorer Quality for PCC Pavements (V=2 and C=0.5)



100 100 25.9

95 95 22.8

90 90 20.0

85 85 17.5

80 80 15.2

75 75 13.2

70 70 11.3

65 65 9.7

60 60 8.3

55 55 7.0

50 50 5.9

45 45 4.9

40 40 4.0

35 35 3.3

30 30 2.6

25 25 2.1

20 20 1.6

15 15 1.2

10 10 0.8

5 5 0.5

0 0 0.2

89

The final PPM is shown by Equation 3.6. The model is used to better understand the

consequences of either exceeding or falling short of the desired quality levels, and to provide a

logical and defensible basis for the adjusted pay schedules that are an integral part of PPM. This

model can be improved based on KDOT’s experience or actual performance data.

Equation 3.6

3.2.2.2 Three Quality Characteristics

Air content is the property that is traditionally measured, as screening tests, to determine

the durability (Diwan et al. 2003, Schell and Konecny 2003). Practical performance model that

includes three quality characteristics, thickness, strength, and air content, was developed using

data in Table 3.36. Air content was included in the model development if in case KDOT wants to

include air content as a surrogate for durability of PCC pavements.

Different values of shape factors were tried. The model was checked whether it returns

precisely the values used to develop it. It was also checked at extreme values (PWL=100 and

PWL=0), and examined how extra quality in some variables can offset the deficient quality in

other variables while still producing design life of 20 years.

TABLE 3.36

Data for PPM for PCC Pavements (Three Variables, V=3)


(years) Thickness (TH) Strength (ST) Air Content (VA)

90 90 90 20.0

50 90 90 10.0

90 40 90 10.0

90 90 30 10.0

90



it. Table 3.37 shows that the model returns the values used to develop it, which are shown in

Table 3.36.

TABLE 3.37

Test of Derivation of PPM for PCC Pavements (V=3 and C=1)



90 90 90 20.0

50 90 90 10.0

90 40 90 10.0

90 90 30 10.0



These results are presented in Table 3.38. When PWL= 100 in all three quality characteristic, the


is an appreciable increase, but it certainly falls within the experience of many agencies. At the

other extreme, the model predicts an expected life less than a year. The model predicts about two

years when one of the quality characteristics has PWL=100 and the rest have PWL=0. Although

not a frequent occurrence, most highway agencies have experienced this result at one time or

another. At this stage, there is nothing to indicate the model is unsatisfactory, but several

additional tests are required.

91

TABLE 3.38 Test of Extremes of PPM for PCC Pavements (V=3 and C=1)



100 100 100 30.7

100 0 0 2.4

0 100 0 1.7

0 0 100 1.4

0 0 0 0.4







Table 3.39 suggests that the model may have such a shortcoming. For example, if

PWLTH=PWLST=100, and PWLVA=63, the model predicts a design life of 20 years. This finding

has raised doubts about the efficacy of the model when shape factor is one. It is now appropriate

to consider other shape factors. Shape factors: 0.15, 0.25, 0.5, 0.75, 1.5, and 2 were considered.

Results from shape factor 0.5 are presented since it was found more reasonable.






more reasonable than those obtained in Table 3.39 even though the values are not far apart.

92

TABLE 3.39 Test of Offsetting Property of PPM for PCC Pavements (V=3 and C=1)



90 90 90 20.0

75.0 100 100 20.0

100 69.0 100 20.0

100 100 63.0 20.0

TABLE 3.40

Test of Offsetting Property of PPM for PCC Pavements (V=3 and C=0.5)



90 90 90 20.0

77.0 100 100 20.0

100 70.5 100 20.0

100 100 63.5 20.0




maximum predicted life from the previous value of about 31 years to value of about 28 years.





field experience.

93

TABLE 3.41 Test of Extremes of PPM for PCC Pavements (V=3 and C=0.5)



100 100 100 28.3

100 0 0 0.6

0 100 0 0.3

0 0 100 0.2

0 0 0 0.0

TABLE 3.42

Test of Progressively Poorer Quality for PCC Pavements (V=3 and C=0.5)



100 100 100 28.3

95 95 95 23.9

90 90 90 20.0

85 85 85 16.7

80 80 80 13.8

75 75 75 11.4

75 50 45 5.7

65 65 65 7.6

60 60 60 6.1

55 55 55 4.9

50 50 50 3.9

45 45 45 3.0

40 40 40 2.3

75 50 45 5.7

30 30 30 1.3

25 25 25 1.0

20 20 20 0.7

15 15 15 0.4

10 10 10 0.3

5 5 5 0.1

0 0 0 0.0

The final PPM including the three quality characteristics for PCC pavements is shown by

Equation 3.7. The model is used to better understand the consequences of either exceeding or

falling short of the desired quality levels, and to provide a logical and defensible basis for the

94

adjusted pay schedules that are an integral part of PPM. This model can be validated and/or

improved based on KDOT’s experience or actual performance data or a combination.

Equation 3.7

3.2.3 Pay Schedule

The performance model serves two purposes. One is to better understand the

consequences of either exceeding or falling short of the desired quality levels, and the other is to

provide a logical and defensible basis for the adjusted pay schedules that are an integral part of

PRS. Ideally, the purpose of the pay schedule is to provide incentive to the contractor to produce

the desired levels of quality. Majority of highway agencies often include an additional incentive

in the form of small bonus payments to contractors whose extra attention to quality control has

produced work that substantially exceeds the acceptable quality levels. At the other extreme,

when the desired levels of quality are not achieved, it is the purpose of the pay schedule to

recoup for the highway agency the anticipated future losses resulting from poor performance

(Weed 2006).

To justify such an approach, there must be a link between quality received and economic

gain or loss to the highway agency. Perhaps the most logical and consistent way to establish this

link is through the use of life-cycle-cost analysis (Weed 2006). Equation for pay adjustment

based on life-cycle-cost analysis was published by previous researchers (Weed 2001, Burati et al.

2003). The authors assumed for the derivation of Equation 3.8 that moderate deficiencies of

construction are not repaired but, instead, lead to premature failure and an earlier scheduling of

the next overlay. KDOT could readily obtain values for the constant terms in this equation.

Equation 3.8

in which

PA = appropriate pay adjustment for pavement or overlay (same units as C),

C = present total cost of resurfacing,

95

DL = design life of pavement or overlay,

EL= expected life of pavement or overlay (independent variable),

OL= expected life of successive overlays (typically 10 years), and

R = (1 + INF) / (1 + INR) in which INF is the long-term annual inflation rate and INT is

the long-term annual interest rate, both in decimal form.

Equations (3.2-3.7) link quality to performance. They are used to predict the expected life

(EL) used in Equation 3.8. Equation 3.8 links performance to economic gain or loss. Combining

the two equations to link quality to economic effect provide a solid analytical basis for the pay

schedule (Weed 2003).

3.3 Composite Index

To demonstrate the practicality of the composite quality measure, a complete acceptance

procedure must be specified. This includes the acceptable quality level (AQL), the rejectable

quality level (RQL), the retest provision, and the pay schedule. Composite index was developed

for both superpave and PCC pavements. Microsoft excel was used to solve simultaneous

equations. Composite index without and with cross-product of quality characteristics were

considered separated.


Composite index that includes two, three, four, and five supepave quality characteristics

was developed separately. Composite index with cross-product of the quality characteristics was

not developed since it became so cumbersome.

3.3.1.1 Two Quality Characteristics without Cross-Product

Composite index (PWL*) was developed in terms of air voids (VA), and in-place density

(DEN). The coefficients were obtained using the data in Table 3.1. The magnitudes of the

coefficients reflect the effect of the variables on the long term performance of the pavements.

The coefficients may be modified based on Hamburg wheel test results at Kansas State

University or field performance or agency’s experience or a combination. Composite index

varies from zero to 100%. The final model developed is shown in Equation 3.9.

96

Equation 3.9

To determine comparable value of PWL* associated with the AQL, the values of

PWLVA=PWLDEN=90 are substituted into Equation 3.9 to obtain PWL* = 90 as the AQL.

Therefore, the pay equation must produce a pay adjustment of zero at PWL* = 90. To determine

the value of PWL* associated with the RQL, any combination of values that give 5- year-life can

be substituted into Equation 3.2. For example, entering PWLVA=79 and PWLDEN=80 into

Equation 3.9 produces PWL*=66. Similarly, any combination of values that gives the 7.5-year-

life gives retest provision. Using PWLVA=79 and PWLDEN=80 gives PWL*=79.5. Assuming a

simple linear pay equation will be sufficient, the pay schedule given by Equation 3.10 was

derived.

Equation 3.10

in which

PA = lot pay adjustment ($ / lane kilometer), and

PWL* = composite quality measure.

It can be seen that when PWL* is at the AQL value of 90, Equation 3.10 produces a pay

adjustment of zero. Similarly, when PWL* equals the RQL value of 66, the pay reduction of -

$24,000 / lane kilometer (-$38,640 / lane mile) is obtained. For truly excellent quality, PWLVA=

PWLDEN=100, PWL* =100, the pay equation awards a maximum bonus of $10,000 / lane

kilometer ($16,100/ lane mile). At the other extreme, when PWLVA= PWLDEN=0, PWL* =0, the

pay equation assigns the maximum pay reduction of -$90,000 / lane kilometer (-$ 144,900/ lane

mile). In between, all pay adjustments are related to performance in that all quality levels that

give any particular life will receive the same level of payment.

3.3.1.2 Two Quality Characteristics with Cross-Product

Data in Table 3.43 was used to develop expected life for two quality characteristics.

Cross-product was included to investigate the difference between only addition and the one

97

which has cross-product. Microsoft excel was used to solve four simultaneous equations. The

final expected life model that includes air voids and in-place density is shown by Equation 3.11.

The model predicts an expected life of 0.25 year when PWL=0 for both quality characteristics.

Equation 3.11 was converted into composite index in terms of PWL* as shown in Equation 3.12.

Equation 3.12 gives PWL* ranging from 0 to 100%.

Equation 3.11

Equation 3.12

TABLE 3.43 Data for Composite Index for Superpave Pavements (Variables, V=2)



90 90 10.0

50 90 5.0

90 40 5.0

45 45 2.5


PWLVA=PWLDEN=90 are substituted into Equation 3.12 to obtain PWL* = 80.5 as the AQL.

Therefore, the pay equation must produce a pay adjustment of zero at PWL* = 80.5. To

determine the value of PWL* associated with the RQL, any combination of values that give 5-

year-life can be substituted into Equation 3.11. For example, entering PWLVA=PWLDEN=64 into

Equation 3.12 produces PWL*=39.5. Similarly, any combination of values that gives the 7.5-

year-life gives retest provision. Using PWLVA=80 and PWLDEN=75.5 gives PWL*=59.5.

Assuming a simple linear pay equation will be sufficient, the pay schedule given by Equation

3.13 is derived.

98

Equation 3.13

in which


PWL* = composite index.

It can be seen that when PWL* is at the AQL value of 80.5, Equation 3.13 produces a pay

adjustment of zero. Similarly, when PWL* equals the RQL value of 39.5, the pay reduction of -


PWLDEN=100, PWL* =100, the pay equation awards a maximum bonus of $19,500 / lane

kilometer ($31,395/ lane mile). At the other extreme, when PWLVA= PWLDEN=0, PWL* =0, the

pay equation assigns the maximum pay reduction of -$80,500 / lane kilometer (-$129,605 / lane



3.3.1.3 Three Quality Characteristics without Cross-Product

Composite index (PWL*) was developed in terms of air voids (VA), in-place density

(DEN), and smoothness (SM). The coefficients were obtained using the data in Table 3.9. The

magnitudes of the coefficients reflect the effect of the variables on the long term performance of

the pavements. The coefficients may be modified based on field performance and/or agency’s

experience. Composite index varies from zero to 100%. The final model developed is shown in

Equation 3.14.

Equation 3.14

To determine the comparable value of PWL* associated with the AQL, the values of

PWLVA=PWLDEN= PWLSM =90 are substituted into Equation 3.14 to obtain PWL* = 90 as the

AQL. Therefore, the pay equation must produce a pay adjustment of zero at PWL* = 90. To


99

year-life can be substituted into Equation 3.3. For example, entering PWLVA=75 and PWLDEN=

PWLSM =70 into Equation 3.14 produces PWL*=72. Similarly, any combination of values that

gives the 7.5-year-life produces retest provision. Using PWLVA= PWLDEN=80, and PWLSM =89

gives PWL*=82.5. Assuming a simple linear pay equation will be sufficient, the pay schedule

given by Equation 3.10 was derived.

When PWL* equals the RQL value of 72, the pay reduction of -$18,000 / lane kilometer

(-$ 28,908/ lane mile) is obtained. For truly excellent quality, PWLVA= PWLDEN= PWLSM =100,

PWL* =100, the pay equation awards a maximum bonus of $10,000 / lane kilometer ($16,100/

lane mile). At the other extreme, when PWLVA= PWLDEN= PWLSM =0, PWL* =0, the pay

equation assigns the maximum pay reduction of -$90,000 / lane kilometer (-$144,900 / lane



3.3.1.4 Three Quality Characteristics with Cross-Product

Data in Table 3.44 was used to develop expected life for three variables. Cross-product

was included to investigate the difference between only addition and the one which has cross-

product. Microsoft excel was used to solve eight simultaneous equations. The final expected life

model that includes air voids (VA), in-place density (DEN), and smoothness (SM) is shown by

Equation 3.15. The model predicts an expected life of 0.60 year when PWL=0 for all three

quality characteristics. Equation 3.15 was converted into composite index in terms of PWL* as

shown in Equation 3.16. Equation 3.16 gives PWL* ranging from 0 to 100%.

Equation 3.15

100

Equation 3.16

101

TABLE 3.44 Data for Composite Index for Superpave Pavements (Variables, V=3)



90 90 90 10

50 90 90 5

90 40 90 5

90 90 35 5

70 45 35 2.5

45 65 25 2.5

30 50 65 2.5

40 40 55 2.5


PWLVA=PWLDEN= PWLSM =90 are substituted into Equation 3.16 to obtain PWL* = 72.5 as the

AQL. Therefore, the pay equation must produce a pay adjustment of zero at PWL* = 72.5. To


year-life can be substituted into Equation 3.15. For example, entering PWLVA=68 and PWLDEN=

PWLSM =70 into Equation 3.16 produces PWL*=34.5. Similarly, any combination of values that

gives the 7.5-year-life produces retest provision. Using PWLVA=83 and PWLDEN= PWLSM =80

gives PWL*=53. Assuming a simple linear pay equation will be sufficient, the pay schedule


Equation 3.17

in which





102

$38,000 / lane kilometer (-$ 61,180 / lane mile) is obtained. For truly excellent quality, PWLVA=

PWLDEN= PWLSM =100, PWL* =100, the pay equation awards a maximum bonus of $27,500 /

lane kilometer ($44,275/ lane mile). At the other extreme, when PWLVA= PWLDEN= PWLSM =0,

PWL* =0, the pay equation assigns the maximum pay reduction of -$72,500 / lane kilometer (-

$116,725 / lane mile). In between, all pay adjustments are related to performance in that all

quality levels that give any particular life will receive the same level of payment.

3.3.1.5 Four Quality Characteristics without Cross-Product


(DEN), asphalt content (AC), and voids in mineral aggregates (VMA). The coefficients were

obtained using the data in Table 3.15. The magnitudes of the coefficients reflect the effect of the

variables on the long term performance of the pavements. The coefficients may be modified

based on field performance and/or agency’s experience. Composite index varies from zero to

100%. The final model developed is shown in Equation 3.18.

Equation 3.18


PWLVA=PWLDEN= PWLAC =PWLVMA =90 are substituted into Equation 3.18 to obtain PWL* =

90 as the AQL. Therefore, the pay equation must produce a pay adjustment of zero at PWL* =

90. To determine the value of PWL* associated with the RQL, any combination of values that

give 5- year-life can be substituted into Equation 3.4. For example, entering PWLVA=PWLDEN=

PWLAC =60 and PWLAC =65 into Equation 3.18 produces PWL*=61. Similarly, any combination

of values that gives the 7.5-year-life produces retest provision. Using PWLVA= PWLDEN= PWLAC

=75, and PWLVMA =74 gives PWL*=75. Assuming a simple linear pay equation will be

sufficient, the pay schedule given by Equation 3.10 is derived.




PWLDEN= PWLAC = PWLVMA =100, PWL* =100, the pay equation awards a maximum bonus of

103

$10,000 / lane kilometer ($16,100/ lane mile). At the other extreme, when PWLVA= PWLDEN=

PWLAC = PWLVMA =100, PWL* =0, the pay equation assigns the maximum pay reduction of -

$90,000 / lane kilometer (-$ 144,900/ lane mile). In between, all pay adjustments are related to

performance in that all quality levels that give any particular life will receive the same level of

payment.

3.3.1.6 Four Quality Characteristics with Cross-Product

Expected life model for Superpave pavements that include four quality characteristics

was developed. Cross-product was included to investigate the difference between only addition

and the one which has cross-product. Microsoft excel was used to solve 15 simultaneous

equations. The final expected life model that includes air voids (VA), in-place density (DEN),

asphalt content (AC), and VMA predicts an expected life of 2.20 year when PWL=0 for all four

quality characteristics. The equations for expected life and composite index were not included

since they were cumbersome.


PWLVA=PWLDEN= PWLAC =PWLVMA =90 are substituted into PWL* equation to obtain PWL*

= 63 as the AQL. Therefore, the pay equation must produce a pay adjustment of zero at PWL* =

63. To determine the value of PWL* associated with the RQL, any combination of values that

give 5- year life can be substituted into expected life equation. For example, entering PWLVA=73

and PWLDEN= PWLAC =PWLAC =70 into PWL* equation produces PWL*=22.5. Similarly, any

combination of values that gives the 7.5-year life produces retest provision. Using PWLVA=80,

PWLDEN=81.5, and PWLAC =PWLVMA =85 gives PWL*=43. Assuming a simple linear pay

equation will be sufficient, the pay schedule given by Equation 3.19 was derived.

Equation 3.19

in which



104




PWLDEN= PWLAC = PWLVMA =100, PWL* =100, the pay equation awards a maximum bonus of

$37,000 / lane kilometer ($59,570/ lane mile). At the other extreme, when PWLVA= PWLDEN=

PWLAC = PWLVMA =100, PWL* =0, the pay equation assigns the maximum pay reduction of -

$63,000 / lane kilometer (-$101,430 / lane mile). In between, all pay adjustments are related to

performance in that all quality levels that give any particular life will receive the same level of

payment.

3.3.1.7 Five Quality Characteristics with Cross-Product


(DEN), smoothness (SM), asphalt content (AC), and voids in mineral aggregates (VMA). The

coefficients were obtained using the data in Table 3.22. The magnitudes of the coefficients reflect

the effect of the variables on the long term performance of the pavements. The coefficients may

be modified based on Hamburg wheel tests at Kansas State University, field performance, and/or

KDOT’s experience. Composite index varies from zero to 100%. The final model developed is

shown in Equation 3.20.

Equation 3.20


PWLVA=PWLDEN= PWLSM =PWLAC =PWLVMA =90 are substituted into Equation 3.20 to obtain

PWL* = 90 as the AQL. Therefore, the pay equation must produce a pay adjustment of zero at

PWL* = 90. To determine the value of PWL* associated with the RQL, any combination of

values that give 5-year life can be substituted into Equation 3.5. For example, entering

PWLVA=PWLDEN= PWLSM =PWLAC =65 and PWLVMA =70 into Equation 3.20 produces

105

PWL*=66. Similarly, any combination of values that gives the 7.5-year life produces retest

provision. Using PWLVA= PWLDEN= PWLSM =PWLAC =85, and PWLVMA =83 gives

PWL*=84.5. Assuming a simple linear pay equation will be sufficient, the pay schedule given by

Equation 3.10 was derived.



$24,000/lane-kilometer (-$38,640/lane-mile) is obtained. For truly excellent quality, PWLVA=

PWLDEN= PWLSM =PWLAC = PWLVMA =100, PWL* =100, the pay equation awards a maximum

bonus of $10,000 / lane kilometer ($16,100/ lane mile). At the other extreme, when PWLVA=

PWLDEN= PWLSM =PWLAC = PWLVMA =100, PWL* =0, the pay equation assigns the maximum

pay reduction of -$90,000/lane kilometer (-$144,900/ lane-mile). In between, all pay adjustments

are related to performance in that all quality levels that give any particular life will receive the

same level of payment.

Expected life model and composite equation that include cross-product of quality

characteristics were not developed for five quality characteristics since it became too

cumbersome.

3.3.2 PCC Pavements

Composite index for PCC pavements that includes two and three quality characteristics

was developed. Composite index without and with cross-product of quality characteristics was

considered.

3.3.2.1 Two Quality Characteristics without Cross-Product

Composite index (PWL*) was developed in terms of thickness (TH) and strength (ST).

The coefficients were obtained using the data in Table 3.29. The magnitudes of the coefficients

reflect the effect of the variables on the long term performance of the pavements. The

coefficients may be modified based on field performance and/or agency’s experience. Composite

index varies from zero to 100%. The final model developed is shown in Equation 3.21.

Equation 3.21

106


PWLTH=PWLST=90 are substituted into Equation 3.21 to obtain PWL* = 90 as the AQL.

Therefore, the pay equation must produce a pay adjustment of zero at PWL* = 90. To determine

the value of PWL* associated with the RQL, any combination of values that give 10-year life

can be substituted into Equation 3.6. For example, entering PWLTH=65 and PWLST=67 into

Equation 3.21 produces PWL*=66. Similarly, any combination of values that gives the 15-year

life produces retest provision. Using PWLTH=79 and PWLST=80 gives PWL*=79.5. Assuming a

simple linear pay equation will be sufficient, the pay schedule given by Equation 3.10 was

derived.

It can be seen that when PD* is at the AQL value of 90, Equation * produces a pay


$24,000/lane-kilometer ($-38,640/lane-mile) is obtained. For truly excellent quality, PWLTH=

PWLST=100, PWL* =100, the pay equation awards a maximum bonus of $10,000/lane-kilometer

($16,100/lane-mile). At the other extreme, when PWLTH= PWLST=0, PWL* =0, the pay equation

assigns the maximum pay reduction of -$90,000/lane-kilometer (-$144,900/lane-mile). In

between, all pay adjustments are related to performance in that all quality levels that give any

particular life will receive the same level of payment.

3.3.2.2 Two Quality Characteristics with Cross-Product

Data in Table 3.45 was used to develop expected life that includes two quality

characteristics for PCC pavements. Cross-product was included to investigate the difference

between only addition and the one which has cross-product. Microsoft excel was used to solve

four simultaneous equations. The final expected life model that includes thickness (TH) and

strength (ST) is shown by Equation 3.22. The model predicts an expected life of 0.50 year when

PWL=0 for both quality characteristics. Equation 3.22 was converted into composite index in

terms of PWL* as shown in Equation 3.23. Equation 3.23 gives PWL* ranging from 0 to 100%.

Equation 3.22

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Equation 3.23

TABLE 3.45 Data for Composite Index for PCC Pavements (Two Variables, V=2)



90 90 20

50 90 10

90 40 10

45 45 10


PWLTH=PWLST=90 are substituted into Equation 3.23 to obtain PWL* = 80.5 as the AQL.

Therefore, the pay equation must produce a pay adjustment of zero at PWL* = 80.5. To


year-life can be substituted into Equation 3.21. For example, entering PWLTH=64 and

PWLST=63.5 into Equation 3.23 produces PWL*=39.5. Similarly, any combination of values that

gives the 15-year life produces retest provision. Using PWLTH=80 and PWLST=75.5 gives

PWL*=60. Assuming a simple linear pay equation will be sufficient, the pay schedule given by

Equation 3.24 was derived.

Figure 3.24

in which





$41,000/lane-kilometer (-$66,010/lane-mile) is obtained. For truly excellent quality, PWLTH=

PWLST=100, PWL* =100, the pay equation awards a maximum bonus of $19,500/lane-kilometer

108

($31,395/lane-mile). At the other extreme, when PWLTH= PWLST=0, PWL* =0, the pay equation

assigns the maximum pay reduction of -$80,500/lane-kilometer (-$129,605/lane-mile). In

between, all pay adjustments are related to performance in that all quality levels that give any


3.3.2.3 Three Quality Characteristics without Cross-Product

Composite index (PWL*) was developed in terms of thickness (TH), strength (ST), and air

content (VA). The coefficients were obtained using the data in Table 3.36. The magnitudes of the

coefficients reflect the effect of the variables on the long term performance of the pavements.

The coefficients may be modified based on field performance and/or agency’s experience.

Composite index varies from zero to 100%. The final model developed is shown in Equation

3.25.

Equation 3.25


PWLTH=PWLST= PWLVA =90 are substituted into Equation 3.25 to obtain PWL* = 90 as the



year-life can be substituted into Equation 3.7. For example, entering PWLTH=72, PWLST=72.5,

and PWLVA =70 into Equation 3.25 produces PWL*=71.5. Similarly, any combination of values

that gives the 15-year life produces retest provision. Using PWLST= 82.5, PWLST=83, and

PWLSM =80 gives PWL*=82.0. Assuming a simple linear pay equation will be sufficient, the

pay schedule given by Equation 3.10 was derived.

When PWL* equals the RQL value of 71.5, the pay reduction of -$18,500/lane-kilometer

(-$29,785/lane-mile) is obtained. For truly excellent quality, PWLTH= PWLST= PWLVA =100,

PWL* =100, the pay equation awards a maximum bonus of $10,000/lane-kilometer ($16,100/

lane-mile). At the other extreme, when PWLTH= PWLST= PWLVA =0, PWL* =0, the pay

equation assigns the maximum pay reduction of -$90,000/lane-kilometer (-$144,900/lane-mile).

109

In between, all pay adjustments are related to performance in that all quality levels that give any


3.3.2.4 Three Quality Characteristics with Cross-Product

Data in Table 3.46 was used to develop expected life for three variables. Cross-product

was included to investigate the difference between only addition and the one which has cross-

product. Microsoft excel was used to solve eight simultaneous equations. The final expected life

model that includes thickness (TH), strength (ST), and air content (VA) is shown by Equation

3.26. The model predicts an expected life of about 2.1 years when PWL=0 for all three quality

characteristics. Equation 3.26 was converted into composite index in terms of PWL* as shown in

Equation 3.27. Equation 3.27 gives PWL* ranging from 0 to 100%.

Equation 3.26

Equation 3.27

110

TABLE 3.46 Data for Composite Index for PCC Pavements (Variables, V=3)


(years) Thickness (TH) Strength (ST) Air Voids (VA)

90 90 90 20.0

50 90 90 10.0

90 40 90 10.0

90 90 30 10.0

70 45 35 5.0

45 65 25 5.0

30 50 65 5.0

40 40 55 5.0


PWLTH=PWLST= PWLVA =90 are substituted into Equation 3.27 to obtain PWL* = 72 as the



year life can be substituted into Equation 3.26. For example, entering PWLTH=PWLST= 70, and

PWLVA =64.5 into Equation 3.27 produces PWL*=32. Similarly, any combination of values that

gives the 15-year life produces retest provision. Using PWLTH=PWLST=81, and PWLVA =80.5

gives PWL*=52. Assuming a simple linear pay equation will be sufficient, the pay schedule


Equation 3.28

in which





111

$40,000/lane-kilometer (-$64,400/lane-mile) is obtained. For truly excellent quality, PWLTH=

PWLST= PWLVA =100, PWL* =100, the pay equation awards a maximum bonus of

$28,000/lane-kilometer ($45,080/lane-mile). At the other extreme, when PWLTH= PWLST=

PWLVA =0, PWL* =0, the pay equation assigns the maximum pay reduction of -$72,000/lane-

kilometer (-$115,920/lane-mile). In between, all pay adjustments are related to performance in

that all quality levels that give any particular life will receive the same level of payment.

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Chapter 4: Conclusions and Recommendations

4.1 Conclusions

Based on this study, the following conclusions can be made:

Moving average control chart analysis does not clearly show any systematic bias

in QC/QA data for Superpave and PCC pavements in Kansas.

QC mean, minimum and maximum density values are higher than QA mean,

minimum, and maximum density whereas as QC Standard Deviation and COV for

density are lower than QA Standard Deviation and COV for density. Lot-wise

comparison shows that QC/QA means are significantly different in most cases.

One reason for these differences could be due to varying number of tests per lot

done by the contractor and KDOT (contractor does more tests).

The number of cases with a significant difference in means increases with an

increase in significance level. However, statistical analysis did not show any

specific trend in sublot mean data as far as order of data collection is concerned.

F-test can be used to determine significant difference in means. The consequence

has equal impact on both KDOT and the contractor.

Currently majority of Superpave projects in Kansas are built with asphalt content

lower than design. Asphalt content can be included in pay adjustment. However,

provisions should be made in which contractors may be penalized for too much

asphalt to avoid flushing or too low asphalt to avoid dry mixes.

The performance model and composite index for both Superpave and PCCP

pavements can be derived with multiple quality characteristics based on percent

within limits (PWL). However, composite index with cross-product of quality

characteristics gives a more realistic pay adjustment.

4.2 Recommendations

Based on this study, the following recommendations can be made:

There was no QC smoothness data to compare with QA smoothness data in this

study. It is recommended that KDOT collects some QA smoothness data to verify

113

QC smoothness data in the future. The frequency of QA smoothness data

collection needs to be established too.

Ten QC readings and five QA readings per lot will be sufficient for statistical

analysis. Bigger lot size or smaller sublots can be used based on economy and

convenience though smaller sublots are recommended from statistical point of

view.

Under current KDOT practices, QC mean, minimum and maximum density

values are higher whereas Standard Deviation and COV for QC density are lower.

It is recommended that a procedure be developed to collect QC and QA data that

have better statistical agreement. It is also recommended that selected projects be

required that pavement cores be the only basis for acceptance to see if better QC

and QA data agreement can be obtained. As an alternative, nuclear devices that

have GPS and continuous data recording capabilities be used to measure densities.

It is recommended that KDOT encourage the contractors to produce Superpave

mix at an asphalt content that equals or exceeds the asphalt content used in the

approved design.

Pay adjustment at 1% significance level is less than or equal to the pay adjustment

at other significant levels. It is recommended that 2.5% be used as significance

level as a compromise between 1 and 5% instead of current 1%.

Khanum et al. (2006) concluded that current KDOT PWL specifications for PCC

pavement construction are more sensitive to the concrete strength than to the PCC

slab thickness. This shows that PCCP strength higher than the specified strength

will result in large bonus payment whereas the gain in performance due to the

higher PCC strength may not be significant. It is recommended to make

adjustments to the current combined pay equation for PCCP that includes strength

and thickness to deemphasize the strength component.

It is recommended to validate and/or improve practical performance models and

composite index based on laboratory tests, field performance, and/or experience

before starting to use for pay adjustment.

114

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