Evaluating the Cracking Predicted by the MEPDG using Results from the S.R. 22 Smart Pavement Study

Final Report

Prepared by: Rania E. Asbahan Jennifer K. McCracken Julie M. Vandenbossche University of Pittsburgh Department of Civil and Environmental Engineering Pittsburgh, Pennsylvania 15261 Prepared for: Pennsylvania Department of Transportation, April 2008

ACKNOWLEDGEMENTS

The success of this project would not have been possible without the assistance and

expertise of many individuals. First, the authors gratefully acknowledge the financial and

technical support provided by the Pennsylvania Department of Transportation (PennDOT).

Specifically, the authors would like to thank Ms. Michelle Tarquino of Central Office for

her assistance. The authors would also like to extend a sincere appreciation to the

PennDOT District 12 Office. The effort and support provided by Mr. Joseph Szczur and

Mr. Gary Barber of District 12 are especially appreciated. Finally, the authors would also

like to extend their sincere gratitude to Mr. Michael Dufalla (formerly of the PennDOT

District 12) who provided vision that was critical in the development of the research

objectives and persistence that was essential in turning this research idea into a funded

project.

The contents of this report reflect the views of the author who is responsible for

the facts and accuracy of the data presented herein. The contents do not necessarily

reflect the views or policies of the Pennsylvania Department of Transportation.

i

TABLE OF CONTENTS Page Number List of Figures .................................................................................................................. v List of Tables ................................................................................................................... vi CHAPTER 1: INTRODUCTION................................................................................... 1

1.1.0 Primary Goals of the Smart Pavement Project ...................................... 1

1.1.1 Goals Completed in Phase I ........................................................ 1

1.1.2 Goals Completed in Phase II ....................................................... 1

1.1.3 Primary Goals of Contract 510601/WO-003............................... 2

1.2.0. Project Location and Site Description.................................................... 2

1.3.0. Pavement Structure and Design Details ................................................ 3

1.4.0. Layout of Test Sections ........................................................................... 4

CHAPTER 2: STRESS IN THE SMART PAVEMENT............................................... 6

2.1.0. Introduction.............................................................................................. 6

2.2.0. Dynamic Sensor Locations ...................................................................... 6

2.3.0. Axle Loads and Configurations used for the Field Testing ................... 9

2.4.0. Stress Corresponding to Measured and Predicted Strains ................... 10

2.4.1. Stress along the Transverse Joint (Group 1) ............................... 11

2.4.2. Stress at Midpanel (Group 3) ...................................................... 12

2.5.0. Effect of Environmental and Applied Loads on Stress in the Smart

Pavement ................................................................................................ 13

2.5.1. Effect of Slab Gradient on Stress in the Smart Pavement............ 14

2.5.2. Effect of Load Magnitude on Stress in the Smart Pavement........ 16

2.5.3. Combined Effect of Slab Gradient and Applied Load on Stress in

the Smart Pavement ..................................................................... 18

CHAPTER 3: EVALUTION OF THE FATIGUE CRACKING MODEL OF THE

MEPDG............................................................................................................. 21

ii

3.1.0 Introduction........................................................................................... 21

3.2.0 MEPDG Fatigue Cracking Model ....................................................... 21

3.3.0 MEPDG Inputs ..................................................................................... 24

3.3.1 General Inputs ........................................................................... 24

3.3.2 Environmental and Climatic Inputs ........................................... 25

3.3.3 PCC Material Properties Inputs................................................ 28

3.3.4 ATPB Material Properties Inputs .............................................. 31

3.3.5 Granular Material and Subgrade Properties Inputs ................. 33

3.3.6 Traffic Inputs.............................................................................. 34

3.4.0 Fatigue Damage and Slab Cracking Results ....................................... 36

CHAPTER 4: CONCLUSIONS AND RECOMMENDATIONS............................... 41

References ..................................................................................................................... 43

iii

Figures Figure Title Page Number Figure 1.1. Design thicknesses of the pavement layers [1; 2; 3]. .................................... 3 Figure 1.2. Layout of the Smart Pavement section [1; 2; 3]. . ......................................... 5 Figure 2.1. Sensor layout for Cell 1 and Cell 2. ............................................................ 7 Figure 2.2. Typical dimensions of dynamic strain gages and dynamic pressure cell. ... 8 Figure 2.3. Axle configuration and tire spacing for the Class 6 truck. .......................... 9 Figure 2.4. Axle configuration and tire spacing of the Class 7 truck. .......................... 10 Figure 2.5. Axle configuration and tire spacing of the Class 10 truck. ........................ 10 Figure 2.6. Loading conditions evaluated in the stress analysis for the restrained and

unrestrained slabs. ............................................................................................ 14 Figure 2.7. Axle configuration and tire spacing for the single axle. ............................ 14 Figure 2.8. Effect of environmental and loading conditions on stress in the restrained

slabs. ................................................................................................................. 19 Figure 2.9. Effect of environmental and loading conditions on stress in the unrestrained

slabs. ................................................................................................................. 20 Figure 3.1. PCC strength characteristics over the 20-year design life. ....................... 30 Figure 3.2. Axle configuration and load for modified Class 6 truck. ........................... 34 Figure 3.3. Traffic and number of load application predicted over the 20-year design

life. .................................................................................................................... 35 Figure 3.4. Slab fatigue damage over the 20-year design life. ..................................... 39 Figure 3.5. Slab cracking over the 20-year design life. ................................................ 39 Figure 3.6. Critical stress causing 50 percent slab cracking. . ..................................... 40

iv

Tables Table Page Number Table 1.1. Summary of sensors installed in the Smart Pavement Project [1; 2; 3]........ 4 Table 2.1 Stress for the gage along the transverse joint (Group 1 sensors) during

truck testing. ...................................................................................................... 12 Table 2.2. Stress at midpanel (Group 3 sensors) during truck testing. ........................ 13 Table 2.3. Temperatures in the slab and ATPB during the maximum positive and

negative temperature gradients. ....................................................................... 15 Table 2.4. Effect of slab gradient on critical tensile stress in the restrained slab......... 16 Table 2.5. Effect of slab gradient on critical tensile stress in the unrestrained slab..... 16 Table 2.6. Effect of load magnitude on critical tensile stress in the restrained slabs. .. 17 Table 2.7. Effect of load magnitude on critical tensile stress in the unrestrained

slabs. .................................................................................................................. 17 Table 2.8. Effect of gradient and load magnitude on critical tensile stress in the

restrained slabs. ................................................................................................ 19 Table 2.9. Effect of gradient and load magnitude on critical tensile stress in the

unrestrained slabs. ............................................................................................ 20 Table 3.1. General MEPDG design inputs. .................................................................. 24 Table 3.2. General structure inputs. .............................................................................. 25 Table 3.3. PCC and ATPB temperatures at 2:00 PM during the month of May. .......... 27 Table 3.4. General drainage inputs. .............................................................................. 28 Table 3.5. Mixture properties for the MEPDG.............................................................. 29 Table 3.6. PCC strength characteristics required for Level 1....................................... 30 Table 3.7. General and thermal PCC properties. ......................................................... 31 Table 3.8. Shrinkage-related PCC properties. ............................................................. 31 Table 3.9. General asphalt inputs for SR 22. ................................................................ 32 Table 3.10. Asphalt mix and binder characteristics. ..................................................... 32 Table 3.11. Granular material and subgrade properties. ............................................ 33 Table 3.12. General traffic inputs. ................................................................................ 35 Table 3.13. General traffic inputs. ................................................................................ 36 Table 3.14. Base modulus and dynamic k-value estimated by the guide. ..................... 37 Table 3.15. Base modulus and k-value used for the stress analysis. ............................ 38 Table 3.16. Concrete stress based on the different traffic loading configurations and

base material parameters. ................................................................................ 38 Table 3.17. Fatigue damage and cracking at the end of the 20-year design life. ......... 40

1

CHAPTER 1: INTRODUCTION

1.1.0 Primary Goals of the Smart Pavement Project

The Smart Pavement research project is aimed at the design and construction of more

cost effective concrete pavements. There are four primary objectives for this research:

1. Evaluate the ability of High Performance Paving (HIPERPAV) software to predict

strength gain and early-age stress development.

2. Characterize the seasonal response of a Jointed Plain Concrete Pavement (JPCP) to

environmental and applied loads.

3. Establish inputs for a pavement constructed in Pennsylvania to use in the

Mechanistic-Empirical Pavement Design Guide (MEPDG).

4. Evaluate stress in the pavement by developing and validating/calibrating finite

element models using field measurements.

The approach taken to accomplish these objectives was to construct an instrumented JPCP

section, perform laboratory testing to characterize the material properties of the paving

concrete and finally perform seasonal load testing and surface profile measurements on the

instrumented pavement. The following sections present a summary of the tasks completed

under Phase I and Phase II of the Smart Pavement Project. This report presents the results of

the work completed under Contract 510601/WO-003, which includes characterizing the

stress in Smart Pavement using the validated finite elements developed under Phase II [1].

1.1.1 Goals Completed under Phase I

The project consists of two phases. Phase I involved the instrumentation of the Smart

Pavement, the evaluation of the early-age (first 28 days) concrete material properties, the

evaluation of HIPERPAV and an analysis of the early-age (first 28 days) pavement response

characteristics. A summary of these findings can be found in the Phase I report submitted in

October 2005 [2].

1.1.2 Goals Completed in Phase II

The second phase involves characterizing the design inputs for the MEPDG,

characterizing longer-term trends in the response of the slab to environmental and applied

2

loads, and the development of finite element models to estimate pavement performance. A

Phase II Interim Report was published in November 2006 summarizing the results from the

load testing and surface profile measurements for the first year after the pavement was

constructed as well as the one-year material properties of the concrete [3]. The Phase II Final

Report was published in February 2008 summarizing the results from the load testing and

surface profile measurements for the first three years after construction as well as the design

inputs for the MEPDG and validation of the finite element models [1].

1.1.3 Primary Goals of Contract 510601/WO-003

Contract 510601/WO-003 tasks include:

1. Evaluate stress in the Smart Pavement during truck testing using the validated

finite element models.

2. Evaluate the effect of environmental conditions and applied loads on stress in the

pavement.

3. Evaluate the accuracy of calculated stress in the structural models of the MEPDG.

A brief section is first provided that describes the project location, site description

pavement cross-section and design details. A general overview of the location of the

dynamic, environmental and static sensors embedded in the pavement is also included. Only

a brief description of the test section is provided below. A more detailed description can be

found in the Phase I Report [2].

1.2.0 Project Location and Site Description

The location of the Smart Pavement is a 3.4 mile section of U.S. Route 22, along

construction Section B01. The majority of this section runs through the municipality of

Murrysville in Westmoreland County. Murrysville is located approximately 20 miles east of

Pittsburgh.

The test section consists of 14 Portland cement concrete (PCC) slabs, which are

located in the westbound truck lane of US Route 22 between Tarr Hollow Road and School

Road. The test section is located in front of a shopping plaza (Franklin Plaza) on the

westbound side of the roadway and a manufacturing facility (Cleveland Brothers Machinery

Company) on the eastbound side.

3

1.3.0 Pavement Structure and Design Details

The pavement is a jointed plain concrete pavement (JPCP) with 15-ft transverse joints

and 12-ft wide lanes. This section of roadway is crowned with a 2.0 percent transverse slope.

The longitudinal slope along the research section is approximately 2.4 percent. The concrete

medians vary in width from 14.4 ft to 2.0 ft with concrete mountable curbs. The Smart

Pavement section contains 2.6-ft wide concrete curb-and-gutter shoulders at an 8 percent

transverse slope adjacent to the outside lane.

The pavement structure is composed of a 12-in thick PCC layer placed over a 4-in

thick asphalt treated permeable base. The subbase material consists of slag material and is 5-

in thick. Originally, the pavement was to be constructed directly on the subgrade but poor

soil conditions required the removal of 24 in of the subgrade material. The 24 in was

replaced with a gap-graded soil and aggregate mixture [2]. The cross section of the

pavement structure is shown in Figure 1..

Figure 1.1. Design thicknesses of the pavement layers [1; 2; 3].

The test section consists of both restrained and unrestrained slabs. The restrained

slabs contain No. 5 epoxy-coated tie bars placed 2.5 ft apart along the lane/shoulder and

centerline joints. Epoxy coated 1.5-in dowel bars were spaced every 12 in along transverse

joints. The unrestrained slabs do not contain either dowel or tie bars.

Asphalt Treated Permeable Base

PENNDOT 2A Subbase

Cut and Fill

Portland Cement Concrete

Subgrade

4 in

5 in

12 in

24 in

4

1.4.0 Layout of Test Sections

Nearly 400 sensors were installed at various depths throughout the pavement

structure. The sensors were installed in groups of slabs known as “cells”. There are a total

of four cells consisting of three slabs each. The cells are labeled 1 through 4, with numbers

increasing in the westward direction. Cells 1 and 2 contain sensors for measuring dynamic

strains and pressures and Cells 3 and 4 measure both static strains and environmental

conditions. The dynamic strain sensors in Cell 1 are a replicate of the dynamic strain sensor

layout in cell 2. The same is true for Cells 3 and 4 with the exception that Cell 4 also

contains environmental sensors. Figure 1.2 presents the layout of the Smart Pavement

section.

Although the sensor arrangements in these two sets of cells are repetitive, there is one

unique factor that separates them. Cells 2 and 3 are unrestrained by dowel and tie bars while

Cells 1 and 4 contain dowels and tie bars. A summary of the types and quantities of the

dynamic sensors installed in Cells 1 and 2 and environmental sensors installed in Cell 4 is

presented in Table 1.1. Additional information on the instrumentation used for the Smart

Pavement project can be obtained from the Phase I Report [2].

Table 1.1. Summary of sensors installed in the Smart Pavement Project [1; 2; 3].

Sensor Type Sensor Name Qty. Measurement Cell Environmental Thermocouple 60 Temperature 4 Environmental Moisture Sensor 24 Relative Humidity 4 Environmental Time Domain Reflectometer 16 Moisture Content 4

Static Load Vibrating Wire Strain Gage 156 Static Strain 3, 4 Static Load Static Pressure Cell 8 Static Pressure 3, 4

Dynamic Load Dynamic Strain Gage 112 Dynamic Strain 1, 2 Dynamic Load Dynamic Pressure Cell 8 Dynamic Pressure 1, 2

Each cell has its own datalogging equipment that collects data from the sensors in the

cell. Data from the dynamic sensors is collected manually at the time of dynamic loading.

Data from the environmental and static sensors in Cell 3 and 4 are collected automatically.

The datalogger in Cells 3 and 4 automatically retrieves data every 15 minutes. Once per day,

the data on the datalogger is sent via telephone modems to a database located on a computer

housed at the University of Pittsburgh.

5

Figure 1.2. Layout of the Smart Pavement section [1, 2; 3].

CELL 1 CELL 2 CELL 3 CELL 4

14 PANELS @ 15’ = 210’ = 64 m

Westbound Traffic

Cell 4: 3 Restrained Slabs

Cell 3: 3 Unrestrained Slabs

Slabs with Static Strain and Environmental Sensors

Slabs with Dynamic Strain Sensors

Cell 2: 3 Unrestrained Slabs

Cell 1: 3 Restrained Slabs

Power Supply

Phone Supply

Datalogger Enclosure with Remote Communications System

Datalogger Enclosure without Remote Communications System

Phone Cable

Power Cable

Sensor Lead Wires

Coaxial/Fiber Optic Cable Cell 4: 1 Intermediate Slab

2+954 2+890

6

CHAPTER 2: STRESSES IN THE SMART PAVEMENT

2.1.0. Introduction

This chapter presents the evaluation of stresses in the pavement using the finite

element models developed and presented in the Phase II Final Report [1]. These models

were used to:

1. Evaluate stress induced during truck testing

2. Evaluate stress in the pavement for a wider range of temperature, moisture

and support conditions and a wider range of axle loads than those

represented during testing.

This chapter begins with an evaluation of stress corresponding to the measured and

calculated strains presented in chapter 7 of the Phase II Final Report [1]. A comparison

between the strains calculated using the finite element models and the measured strains was

made to validate the finite element models. This validation procedure was performed using

data collected when the gradient of the slab was approximately zero so stresses produced

purely by the applied loads could be isolated from the residual stresses that result as a

function of environmental loads. These calculated stresses, along with the corresponding

measured and calculated strains are presented below.

Next, the finite element models are used to evaluate stress in the pavement for a wider

range of environmental and loading conditions than what was experienced during the data

collection outings. The effects of slab gradient and load magnitude on pavement response

will be evaluated independently and then the combined effect of both parameters will be

considered.

2.2.0. Dynamic Sensor Layout

Figure 2.1 outlines the locations of the dynamic strain gages and dynamic pressure

cells in Cells 1 and 2. Longitudinally oriented gages are located in the wheelpath at the

center of the slab (Group 3) and in the slab corner along the edge (Group 2). The dynamic

strain gages oriented in the transverse direction measure strains in the wheelpath near the

transverse joints (Group 1). As shown in Figure 2.1, the sensor layout for the unrestrained

cell (Cell 2) is almost identical to that of the restrained cell (Cell 1). Figure 2.2 shows the

7

Figure 2.1. Sensor layout for Cell 1 and Cell 2.

Instrumented Unrestrained Panels (No Dowel and Tie Bars)

Instrumented Restrained Panels (Dowel and Tie Bars)

CELL 1

Dynamic Strain Gage (CE) Dynamic Pressure Cell (DP)

CELL 2

Slab A Slab B Slab C Slab D

Slab A Slab B Slab C

CE09, CE10CE11, CE12CE13 CE14CE15, CE16

CE17, CE18

CE25, CE26CE23, CE24CE21, CE22CE19, CE20 CE27, CE28

CE29, CE30CE31, CE32CE33, CE34

CE35, CE36CE37 CE38CE39 CE40CE41, CE42

CE43, CE44CE44, CE45CE46, CE47CE48, CE49CE51, CE56

CE48, CE49CE52, CE54

CE46, CE47CE44, CE45CE43, CE44

CE35, CE36CE37 CE38CE39 CE40CE41, CE42

CE27, CE28CE29, CE30CE31, CE32CE33, CE34

CE17, CE18CE19, CE20CE21, CE22CE23, CE24CE25, CE26

CE09, CE10CE11, CE12CE13 CE14CE15, CE16

CE07, CE08 CE05, CE06

CE03, CE04 CE01, CE02

CE01, CE02 CE03, CE04 CE05, CE06 CE07, CE08

8

Representative Dimensions – Cells 1 and 2

Dynamic Strain Gage (CE) Dynamic Pressure Cell (DP)

12’ – 0”

15’ – 0”

7’-6”

2’ – 0”

4’ – 0”

4” Sensor Clearance from Panel Edge

(Typical All Sensors)

Figure 2.2. Typical dimensions of dynamic strain gages, and dynamic pressure cells.

3’ – 0”

Sensor Group 1

Sensor Group 2

Sensor Group 3

9

typical dimensions of the sensor layout. The top sensor is 0.5 in below the surface of the

slab and the bottom sensor is located directly on the bottom of the slab, therefore each

location measures strain at the top and bottom of the slab.

2.3.0. Axle Loads and Configurations used for Field Testing

The truck load testing conducted over the dynamic strain gages used three different

axle configurations, as shown in Figure 2.3 through Figure 2.5. The three trucks consisted of

the following:

1. 6-axle semi (FHWA Class 10);

2. 4-axle dump truck with a triple axle in the rear (FHWA Class 7) and

3. 3-axle dump truck with a tandem axle in the rear (FHWA Class 6).

Each truck was loaded with three different loads representing an average, high and overload

condition. Additionally, for each axle and load configuration, the truck made two passes

over the test section. One pass was with the outside wheels passing directly adjacent to the

lane/shoulder edge. The other pass was in the wheelpath, approximately two feet from the

lane/shoulder. Each truck pass was completed at creep speed. The axle that produced the

maximum strain was identified for each truck included in the field study. This critical axle is

highlighted in gray in Figure 2.3 through Figure 2.5 for each truck classification. A finite

element analysis was performed to determine the stress along the transverse joint where the

Group 1 sensors are located and at midpanel where the Group 3 sensors are located. The

result from this analysis is presented in the next section.

Figure 2.3. Axle configuration and tire spacing for the Class 6 truck.

14” and tire width is 8”

11’9” 4’6”

50” 86”

10

Figure 2.4. Axle configuration and tire spacing of the Class 7 truck.

Figure 2.5. Axle configuration and tire spacing of the Class 10 truck.

2.4.0. Stress Corresponding to Measured and Predicted Strains

The Smart Pavement was modeled using finite element. The inputs for the models

were established based on FWD deflection data and material property testing [1; 3]. The

models were then validated using field strain measurements. The stress induced during truck

testing for the critical axle was determined using these validated models. The predicted

stress corresponding to the strain measured using the second sensor from the longitudinal

joint within sensor Group 1 (adjacent to transverse joint) was determined. The predicted

stress corresponding to the first sensor for the unrestrained slabs and third sensor for the

restrained slabs from the longitudinal joint within sensor Group 3 (midpanel) was also

determined.

10’5” 4’5”

82” 46”

14”

31’7” 4’2” 4’2”

14” 8”

14”

11” 4’6”4’4”

8”8”

50” 86”

11

The location of the stress calculated for the Group 1 sensors is 4 in from the

transverse joint in the longitudinal direction and 36 in from the lane/shoulder joint in the

transverse direction. The location of the calculated stress for Group 3 sensors is 90 in from

the transverse joint in the longitudinal direction and 10 in from the lane/shoulder joint in the

transverse direction for the unrestrained slabs and 22 in from the lane/shoulder joint in the

restrained slabs. Additionally, stress was determined for each truck classification (Class 6, 7,

and 10). The results of this analysis are provided below and begin with a discussion of stress

calculated for the Group 1 sensor.

2.4.1. Stress along the Transverse Joint (Group 1 Sensor Location)

Table 2.1 presents the results for the maximum tensile stress calculated at the

transverse joint in the restrained and unrestrained slabs for each truck classification (Class 6,

7, and 10). Again, the stress was determined at location of the strain gage when the critical

axle is directly on top of the gage. for truck loads applied to the Smart Pavement when the

effective gradient in the slab is close to zero. Stress at the transverse joint of the restrained

slab ranged between 20 and 22 psi for the range of axle configurations and axle loads

considered. The corresponding measured strains varied between 4 and 6 microstrain. Stress

in the unrestrained slab ranged between 19 and 35 psi, while the measured strain ranged

between 4 and 8 microstrain. The stress for both slabs is very small since the stress is the

result of just an applied load and not a combination of both an applied and an environmental

load.

Stress in the unrestrained slabs was approximately 37 percent larger than stress in the

restrained slabs, while strain was only 26 percent larger. The fact that the unrestrained slab is

thinner (approximately 2.5 inches) than the restrained slab contributes to the lower stress.

The reduced potential for stress transfer across the joints also results in larger stresses in the

unrestrained slabs. It also must be remembered that comparisons can not be made between

trucks because not all of the strains were measured at the same time of the year.

12

Table 2.1. Stress for the gage along the transverse joint (Group 1 sensors) during truck testing.

GAGE IN SENSOR GROUP 1 RESTRAINED SLABS

Truck Type

Load per Axle (lbs)

Measured Microstrain

Predicted Microstrain

Stress (psi)

Class 6 15,000 6 3 20 Class 7 18,000 4 4 20 Class 10 18,000 4 3 22

UNRESTRAINED SLABS Class 6 15,000 8 5 33 Class 7 18,000 4 3 19 Class 10 18,000 5 5 35

2.4.2. Stress at Midpanel (Group 3 Sensor Location)

Table 2.2 provides the stress determined at midpanel for both the restrained and

unrestrained slabs during truck testing. Again, the stress was determined at location of the

strain gage when the critical axle is directly on top of the gage for truck loads applied to the

Smart Pavement when the effective gradient in the slab is close to zero. The strains in table

2.2 for the unrestrained slab were recorded by the first sensor away from the lane/shoulder

joint. The strains for the restrained slab represent strains measured by the third sensor from

the lane/shoulder joint. Stress at midpanel ranged between 36 and 52 psi for the restrained

slabs while the measured strain varied between 3 and 10 microstrain for the range of axle

loads and vehicle axles considered. Stress in the unrestrained slabs ranged between 40 and

43 psi while corresponding measured strain ranged between 5 and 8 microstrain. As the

strain increases, the predicted strains more closely approximate the measured strains. This

reflects the difficulty in measuring such small strains with the strain gages and data collection

equipment used. Again, the stresses corresponding to the measured strains are quite small

because the testing was performed when the effective gradient in the slabs was close to zero.

Table 2.2. Stress at midpanel (Group 3 sensors) during truck testing.

13

SENSOR GROUP 3 RESTRAINED SLABS

Truck Type

Load Level (lbs)

Measured Microstrain

Predicted Microstrain

Stress (psi)

Class 6 15,000 7 8 41 Class 7 18,000 6 7 36 Class 10 18,000 10 10 52

UNRESTRAINED SLABS Class 6 15,000 5 6 43 Class 7 18,000 7 8 42 Class 10 18,000 8 8 40

2.5.0. Effect of Environmental and Applied Loads on Stress in the Smart Pavement

The stresses determined during the validation of the finite element models in the

previous section were quite small. This is because the stress is the result of only an applied

load and not the combination of both an applied and environmental load. This section

evaluates the effects of both environmental and applied loads on the stress state in both the

restrained and unrestrained slabs. The first analysis will evaluate the effect of temperature

gradients in the Smart Pavement on stress. Three temperature gradient conditions (maximum

positive, maximum negative, and zero) will be investigated for two loading configurations.

These loading configurations are provided in Figure 2.6. The first loading configuration

consists of placing a single axle in the wheelpath (approximately 24 inches from slab edge)

and at midpanel. This is the critical load condition for bottom-up transverse cracking. The

axle configuration and tire spacing of the single axle is provided in Figure 2.7. A tire

pressure of 120 psi was used for all analyses. The second load configuration evaluates stress

when a single axle is placed adjacent to each transverse joint. This represents an axle

spacing that is approximately equal to the joint spacing, which is the critical load condition

for top-down transverse cracking.

Next, the effect of the magnitude of the applied load on stress in the Smart Pavement

is evaluated. This analysis will compare stress in the restrained and unrestrained slabs when

there is no gradient for each loading condition, and for load magnitudes of 12,000, 18,000

and 24,000 lbs. Finally, the effect of combined environmental and applied loads on stress in

the Smart Pavement will be evaluated.

14

Figure 2.6. Loading configurations evaluated in the stress analysis for the restrained and unrestrained slabs.

Figure 2.7. Axle configuration and tire spacing for the single axle.

2.5.1. Effect of Slab Gradient on Stress in the Smart Pavement

The effect of the temperature gradient in the slab on stress in the restrained and

unrestrained slabs is evaluated first. The stress is determined for each load configuration in

the presence of a positive, negative and zero temperature gradient condition. An 18,000 lb

axle load was applied. The largest positive and negative temperature gradient to develop in

the Smart Pavement during the first three years after construction, occurred during the spring

of 2007 and was +2.22 °F/in and -1.72 °F/in, respectively. Table 2.3 provides the

temperature conditions in both the restrained and unrestrained slabs during these gradients.

12”

11”

102”

Load Condition 1

Load Condition 2

15

The temperature throughout the slab and the asphalt treated permeable base (ATPB) during

the zero gradient condition were 104 °F and 90 °F, respectively, since these were the

temperatures at the time the concrete set. A detailed description of the finite elements

models can be found in the Phase II Final Report [1]. Several other parameters remained

constant throughout this analysis and they include:

• modulus of subgrade reaction (k-value), 212 pci

• elastic modulus of the base, 336,000 psi

• load transfer efficiency of the joints

- Restrained – 87%

- Unrestrained – 67%

Table 2.3. Temperatures in the slab and ATPB during the maximum positive and negative temperature gradients.

Temperature in Slab, °F Restrained Unrestrained Location

in Slab Positive Negative Positive Negative Top 87 35 84 37

Middepth 71 48 71 48 Bottom 55 60 58 58 ATPB 57 56 57 56

Table 2.4 and Table 2.5 summarize the stresses determined using the finite element

models for each gradient for both the restrained and unrestrained slabs. The maximum stress

for the first load configuration (when the single axle was placed at midslab) occurred when a

positive gradient was present. The maximum tensile stress at the bottom of the pavement

was 501 psi in the restrained slabs and 362 psi in the unrestrained slabs. These are very high

stresses. Very few load applications could be applied for this combination of temperature

gradient and applied load. Again, this is the peak positive gradient that developed in the slab

throughout the first three years after paving.

The maximum tensile stress to develop at the top of the slab occurred when the

second load configuration was applied along with the maximum negative gradient. The

maximum tensile stress for this loading condition was 517 psi for the restrained slabs and

207 psi for the unrestrained slabs. This is a substantial difference in stress that results from

16

the different restraining conditions indicating that a large portion of the stress that is

generated in the restrained slab is probably induced by the restraint the dowel and tie bars

provide to curling. Evidence of this can be found by comparing the substantially higher

stresses determined for the restrained slabs (Table 2.4) compared to the unrestrained slabs

(Table 2.5) for both positive and negative gradients. The stress varied between 70 and 139 in

the restrained slabs and between 57 and 92 in the unrestrained slabs when no gradient was

present.

Table 2.4. Effect of slab gradient on critical tensile stress in the restrained slab.

CRITICAL TENSILE STRESS, PSI Restrained Slab

Loading Condition

Positive Gradient

Negative Gradient

Zero Gradient

1 501 270 139 2 514 517 70

Table 2.5. Effect of slab gradient on critical tensile stress in the unrestrained slab.

CRITICAL TENSILE STRESS, PSI Unrestrained Slab

Loading Condition

Positive Gradient

Negative Gradient

Zero Gradient

1 362 165 92 2 190 207 57

2.5.2. Effect of Load Magnitude on Stress in the Smart Pavement

The effect of load magnitude on stress in the restrained and unrestrained slabs is

presented in this section. This analysis evaluates stress when no temperature gradient was

present for both load conditions at three different load magnitudes (12,000, 18,000 and

24,000 lbs). The critical stress for each combination of variables is provided in Table 2.8 and

Table 2.9 for the restrained and unrestrained slabs, respectively. Stress in the restrained slabs

ranged between 113 and 162 psi for the loading Condition 1 and between 65 and 70 psi for

the loading Condition 2. In the unrestrained slabs, stress ranged between 63 and 118 psi for

loading Condition 1 and between 38 and 74 psi for loading Condition 2.

17

It was determined that stress in the restrained slabs was approximately 28 percent

larger than stress in the unrestrained slabs when averaged for all the runs. However, when

looking at the difference in stress between the restrained and unrestrained slabs for each load

level, the difference between the two decreases as the load magnitude increases. For

example, during loading Condition 2, stress in the restrained slabs is 42 percent larger then

the unrestrained slabs when a 12,000 lb load is applied. However, when a 24,000 lb load is

applied, the difference in stress between the two slabs types is only 4 percent.

Table 2.6. Effect of load magnitude on critical tensile stress in the restrained slabs.

Restrained Slabs Loading

Condition Load Stress (psi)

1 12,000 113 1 18,000 139 1 24,000 162 2 12,000 65 2 18,000 70 2 24,000 77

Table 2.7. Effect of load magnitude on critical tensile stress in the unrestrained slabs.

Unrestrained Slabs Loading

Condition Load Stress (psi)

1 12,000 63 1 18,000 92 1 24,000 118 2 12,000 38 2 18,000 57 2 24,000 74

2.5.3. Combined Effect of Slab Gradient and Applied Load on Stress in the Smart

Pavement

The combined effect of slab gradient and load magnitude on stress in the restrained

and unrestrained slabs is presented herein. This analysis evaluates stress for the critical

loading condition in the presence of the maximum positive, maximum negative, and zero

18

gradient as the load magnitude is varied between 12,000 and 24,000 lbs.

The results for the restrained slabs are summarized in Table 2.8. The variation of

stress in the restrained slabs can be seen in Figure 2.8. The range of the stress in the

restrained slabs was 82 psi for loads between 12,000 and 24,000 lbs applied using loading

Condition 1 when a +2.22 oF/in was present. The maximum stress developed at the bottom

of the slab at midpanel for this combination of loads. For a –1.72 oF/in gradient and applying

loads between 12,000 and 24,000 lbs using load Condition 2, the stress varied by only 2 psi.

The stress in the slab was close to 500 psi for the restrained slabs when a gradient is present.

At such a high stress level, very few applications can be applied before failure. Fortunately,

gradients this high rarely occur. With no gradient, the stress in the slab varied 49 psi when

load Condition 1 was applied and only 12 psi for load Condition 2.

Table 2.9 provides the variation of stress in the unrestrained slabs due to the

combined effect of environmental and applied loads, while Figure 2.9 shows this variation

graphically. A +2.22 oF/in gradient and load Condition 1 produced a change in stress of 46

psi as the load magnitude varied between 12,000 and 24,000 lbs for the unrestrained slab.

Load Condition 2 in combination with a -1.72 gradient results in a change in stress of 36 psi.

The magnitude of the stress in the unrestrained slabs is substantially lower than the restrained

slabs. This emphasizes the fact that even though dowels help to reduce the deflections at the

joint; they also can contribute to increases in stress in the slab.

19

LC

Table 2.8. Effect of gradient and load magnitude on critical tensile stress in the restrained slabs.

Restrained Slabs

Loading Condition

Load (lb)

Stress (psi)

1 12,000 460 1 18,000 501 1 24,000 542 2 12,000 516 2 18,000 517 2 24,000 518

Effect of Environmental and Loading Conditons on Stress (Restrained Slabs)

0

100

200

300

400

500

600

0 5,000 10,000 15,000 20,000 25,000 30,000

Load (lbs)

Stre

ss (p

si)

Positive (2.2 F/in) Negative (-1.7 F/in)Zero (Loading Cond. 1) Zero (Loading Cond. 2)

Figure 2.8. Effect of environmental and loading conditions on stress in the restrained slabs.

Load Condition 1 Load Condition 2

Load Condition 2 Load Condition 1

20

Table 2.9. Effect of gradient and load magnitude on critical tensile stress in the unrestrained slabs.

Unrestrained Slabs

Loading Condition

Load (lb)

Stress (psi)

1 12,000 338 1 18,000 362 1 24,000 385 2 12,000 188 2 18,000 207 2 24,000 224

Effect of Environmental and Loading Conditons on Stress (Unrestrained Slabs)

0

100

200

300

400

500

600

0 5,000 10,000 15,000 20,000 25,000 30,000

Load (lbs)

Stre

ss (p

si)

Positive (2.2 F/in) Negative (-1.7 F/in)Zero ( Loading Cond. 1) Zero (Loading Cond. 2)

Figure 2.9. Effect of environmental and loading conditions on stress in the unrestrained

slabs.

Load Condition 1

Load Condition 2 Load Condition 1 Load Condition 2

21

CHAPTER 3: EVALUATION OF THE FATIGUE CRACKING MODEL OF THE

MEPDG

3.1.0 INTRODUCTION

This chapter presents the evaluation of the accuracy of the calculated stress in the

fatigue cracking model of the MEPDG. Fatigue damage is accumulated over the entire

pavement life due to the combined effects of environmental and traffic loads.

To evaluate the fatigue cracking model, the MEPDG is used to predict damage for an

unrestrained 12-in thick jointed plain concrete pavement over a 20-year design life. The

model is based on the pavement structure properties of the Smart Pavement, which were

determined in the Phase II Final Report [1]. Since fatigue takes into account the combined

effects of environmental and traffic loads, and to facilitate the evaluation of this model,

constant climatic conditions are used in this model. This will help isolate the effect of traffic

loading alone on a concrete slab with a constant temperature and moisture profile through it.

In addition, to simplify the analysis of the cracking model and the evaluation of concrete

stress, one type of traffic loading is applied on the pavement during the 20-year design life.

Moreover, the same pavement structure, subjected to the same temperature and

moisture conditions and the same traffic loading is then analyzed using the finite element

program, Illislab, to determine the resulting stresses in the concrete. The stress is evaluated

based on the base properties and stiffness of the underlying layers that are estimated by the

MEPDG and those estimated based on the results of the calibrated finite element models

presented in the Phase II Final Report [1]. The resulting fatigue damage and slab cracking

based on both sets of data are compared and the critical stress to cause slab failure is

estimated.

This chapter begins with an overview of the fatigue cracking model used in the

MEPDG. Then the inputs used to model the pavement structure in the MEPDG are

presented, and finally, the results of the fatigue damage and slab cracking are presented.

3.2.0 MEPDG FATIGUE CRACKING MODEL

The fatigue damage due to the combined effect of environmental and traffic loads is

accumulated according to Miner's damage hypothesis by summing the damage over the

22

entire design period. When the estimated value of accumulated damage is small, the

pavement structure is not expected to have physical distresses. When the accumulated

damage is large, physical distresses can be expected [4].

Several key factors are taken into account in the fatigue cracking model. They

include the following:

− Traffic load and number of applications,

− Slab curvature at the time of loading, which is affected by the climatic

conditions,

− PCC material properties, and

− Base material and subgrade soil properties.

The critical traffic loading condition varies depending on the slab curvature. When

the slab curvature is downward (positive gradient), the critical traffic loading at midpanel

results in high tensile stress at the slab bottom. When the slab curvature is upward (negative

gradient), the critical traffic loading at the slab edges results in high tensile stress at the slab

top.

The PCC material properties influence the strength of the concrete at the time of

loading. The base material and subgrade soil properties are used to characterize the subgrade

k-value needed for the design analysis. The subgrade k-value is obtained through a

conversion process, which transforms the actual pavement structure into an equivalent

structure that consists of the PCC slab, base, and an effective dynamic k-value. The “E-to-k”

conversion is performed internally in the MEPDG software as a part of input processing.

The effective k-value used in this Guide is a dynamic k-value, which should be distinguished

from the traditional static k-values used in previous design procedures. The dynamic k-value

is typically considered to be approximately twice that of the static k-value.

The general expression for fatigue damage accumulation is presented in equation 3-1.

∑=nmlkji

nmlkji

Nn

FD,,,,,

,,,,, (Equation 3-1)

Where: FD = Total fatigue damage (top-down or bottom-up)

ni,j,k,l,m,n = Applied number of load applications at condition i, j, k, l, m, n.

Ni,j,k,l,m,n = Allowable number of load applications at condition i, j, k, l, m, n.

23

i = Age (accounts for change in PCC modulus of rupture, layer bond condition,

deterioration of shoulder LTE)

j = Month (accounts for change in base and effective dynamic modulus of subgrade

reaction)

k = Axle type (single, tandem, and tridem for bottom-up cracking; short, medium, and

long wheelbase for top-down cracking)

l = Load level (incremental load for each axle type)

m = Temperature difference

n = Traffic path

Each load application (ni,j,k,l,m,n) is identified by the axle type k at load level l that

passed through traffic path n at a specific age i during month j. A temperature difference m

is present at the time the load is applied. The allowable number of load applications is the

number of load cycles at which fatigue failure is expected (corresponding to 50 percent slab

cracking) and is a function of the applied stress and PCC strength. The allowable number of

load applications is determined using the field calibrated fatigue model presented in equation

3-2.

4371.0).()log( 2

,,,,,1,,,,, += c

nmlkji

inmlkji

MRCNσ

(Equation 3-2)

Where: Ni,j,k,l,m,n = Allowable number of load applications at condition i, j, k, l, m, n

MRi = PCC modulus of rupture at age i, psi

σi,j,k,l,m,n. = Applied stress at condition i, j, k, l, m, n

C1 = Calibration constant = 2.0

C2 = Calibration constant = 1.22

The final calibrated model, which shows percentage of slabs with transverse cracks of

all severities in a given traffic lane, provides the amount of transverse cracking. This model

is represented by equation 3-3. The model is used for predicting both bottom-up and top-

down cracking. The total amount of cracking is determined using equation 3-4.

98.111

−+=

FDCRK (Equation 3-3)

24

Where: CRK = Predicted amount of bottom-up or top-down cracking (fraction).

FD = Fatigue damage calculated using equation 3-1.

TCRACK=(CRKBottom-up+CRKTop-down-CRKBottom-Up.CRKTop-down).100% (Equation 3-4)

Where: TCRACK = Total cracking (percent).

CRKBottom-up = Predicted amount of bottom-up cracking (fraction).

CRKTop-down = Predicted amount of top-down cracking (fraction).

3.3.0 MEPDG INPUTS

The MEPDG was used to model the Smart Pavement to determine the amount of

fatigue damage and slab cracking sustained under a repeated load. For this, the pavement

design inputs were established using Level 1, 2 and 3 data. Details on how these inputs were

defined are provided in the Phase II Final Report [1]. Whenever available, Level 1 inputs are

used in the analysis since they provide the highest level of accuracy. The inputs used in the

analysis are summarized below.

3.3.1 GENERAL INPUTS

The general design inputs define the analysis period, type of design, pavement

construction month and traffic opening month, as presented in Table 3.10.

Table 3.10. General MEPDG design inputs.

Input Parameter Value

Design life 20 years

Construction month August-04

Traffic opening month September-04

Type of Design JPCP

The pavement consists of an undoweled 12-in PCC slab resting on the following

layers: a 4-in asphalt treated permeable base (ATPB), a 5-in PennDOT 2A subbase and 24 in

of fill. The general structural design inputs are provided in Table 3.11. In this analysis, the

built-in construction gradient in the slab is established as zero.

25

Table 3.11. General structure inputs.

Input Parameter Value

Permanent curl/warp 0 °F

Joint spacing 15 feet

Sealant type Liquid

Base type Asphalt treated

Erodobility index very erosion resistant (2)

PCC-base interface full friction contact

Loss of full friction 229 months

3.3.2 ENVIRONMENTAL AND CLIMATIC INPUTS

The environmental factors significantly affect performance of JPCP. Factors such as

precipitation, temperature, and moisture determine the shape and critical stresses of a

concrete slab, which affects performance. In the MEPDG, the environmental analysis is

performed by the Enhanced Integrated Climatic Model (EICM) which simulates changes in

the pavement and subgrade materials that are caused by seasonal changes in environmental

conditions [4].

The EICM predicts temperature, resilient modulus adjustment factors, pore water

pressure, water content, frost and thaw depth, frost heave, and drainage throughout the entire

pavement structure. The output from the EICM is used by the structural response models and

performance prediction models of the MEPDG to evaluate the performance of the trial design

pavement over the design life. When the MEPDG uses the damage accumulation model, the

design analysis period is divided into monthly time increments to analyze the proposed

pavement structure. Each month is then subdivided into 2-hour periods to establish the

temperature profiles in the slab. For each time increment, the equivalent linear temperature

difference through the concrete slab is accounted for in increments of 2°F for both positive

(daytime) and negative (nighttime) top-to-bottom temperature differences. In addition, all

other factors that affect pavement response and damage are held constant within each time

increment; they include: concrete strength and modulus, base modulus, subgrade modulus

and joint load transfer across transverse and longitudinal joints. For each time increment,

26

critical stresses, strains and deflections are determined along with damage accumulated

during that time increment. The fatigue damage due to the combined effect of environmental

and traffic loads is accumulated according to Miner's damage hypothesis by summing the

damage over the entire design period. When the estimated value of accumulated damage is

small, the pavement structure is not expected to have physical distresses. When the

accumulated damage is large, physical distresses can be expected [4]. The fatigue damage

model is discussed in more detail in section 3.2.0.

In this project, the climatic data is held constant throughout the design life. This will

help isolate the effect of climatic changes on the development of stress in the concrete slab

and provides a means for evaluating the fatigue damage and slab cracking model. A site-

specific weather station with the following constant conditions was created for this project

using the Integrated Climatic Model (ICM) [5]:

− Hourly air temperature: 80˚F

− Hourly precipitation: 0 in

− Hourly wind speed: 0 mph

− Hourly percentage sunshine: 1 percent

− Hourly relative humidity: 95 percent

The above values were selected to minimize daily and seasonal temperature and

moisture changes in the slab. The temperature of 80˚F was also selected as the zero-stress

temperature in the concrete and the reference temperature in the asphalt base to help reduce

environmental-related stress. The precipitation was set at zero and the hourly relative

humidity was set at 95 percent to minimize variations in the concrete moisture gradient. The

MEPDG assumes a constant relative humidity of 95 percent throughout the depth of the slab

accept for the top 2 in of the slab. The top 2 in fluctuates as a function of the ambient

relative humidity. By establishing a constant ambient relative humidity of 95 percent, the

upper 2 in of the concrete will maintain a constant relative humidity of 95 percent throughout

the depth of the slab. This allows the pavement structure to be loaded while the slab is not

warped.

The hourly sunshine was set to 1 percent to avoid daily and seasonal changes in the

temperature gradient that are caused by exposure to the sun. However, the ICM

automatically calculates the solar radiation factor based on the project location. As a result,

27

the pavement structure was found to undergo some temperature changes throughout the 20-

year design life. To make sure the temperature distribution in the slab was kept constant

throughout the design life, the pavement was only loaded during the same one-hour period

everyday for one month. (Note: The MEPDG reduces the analysis period to less than one-

month increments during spring-thaw. Since the ambient temperature was kept constant at

80˚F, the subgrade never froze. Therefore, it can be safely assumed that the analysis period

was one month for every month of the year.)

The pavement was modeled using a zero temperature gradient in the slab and a

positive gradient in the slab. It was found that the MEPDG predicts no damage or cracking

for the slab when the loads are applied when a zero temperature gradient is present in the

slab. As a result, the traffic was applied when a positive temperature gradient is present in

the slab. A positive temperature difference of 13.8˚F is selected for this study. This is

equivalent to a temperature gradient of +1.15˚F/in across the slab. This gradient was selected

because it is suitable for the estimation of slab stresses using Illislab. It has been shown that

when using a large positive gradient, Illislab tends to overestimates slab curvature when

compared to curvature measured using a Dipstick. This results in an overestimation of the

stress [6]. This gradient was found to occur daily at 2:00 PM during the month of May. And

the corresponding temperatures in the slab and underlying base layer are presented in Table

3.12.

Table 3.12. PCC and ATPB temperatures at 2:00 PM during the month of May.

Location Temperature (˚F)

PCC slab:

- Top 123.9

- Middepth 117.0

- Bottom 110.1

ATPB 111.0

Groundwater-related inputs also play a significant role in the overall accuracy of the

foundation/pavement moisture contents. The depth to the water table for this project was

identified from the results of soil borings performed near the test section. According to the

28

boring log, the water table for the test section at stations 95+145 and 97+11 is 9 ft below the

surface of the soil.

Other drainage inputs affecting the infiltration of water into the pavement and the

drainage of the pavement that were used are discussed in the Phase II Final Report [3] and

are summarized in Table 3.13.

Table 3.13. General drainage inputs.

Input Parameter Value

Type of infiltration Minor

Cross slope 2.0 %

Longitudinal slope 2.4 %

Lane width 12 feet

Edge drain trench width 6 in

Cross-section geometry Crowned

3.3.3 PCC MATERIAL PROPERTIES INPUTS

PCC material properties play a significant role in the performance of slabs in

response to environmental and applied loads and are very important inputs in the distress

prediction models of the MEPDG. PCC properties can be classified under three major

conceptual groups:

• Strength/mechanical behaviour: Modulus of elasticity, Poisson’s ratio,

modulus of rupture, indirect tensile strength, compressive strength, PCC unit

weight and coefficient of thermal expansion.

• Shrinkage: Ultimate shrinkage, reversible shrinkage, time to reach 50 percent

ultimate shrinkage.

• Thermal behaviour: Thermal conductivity, specific heat and surface short

wave absorptivity.

Most of the input variables within the first group vary with PCC age in the short and

long term. Due to the incremental nature of the distress prediction models used in the

MEPDG, a time dependent variation of these properties is considered throughout the design

life. The PCC material property inputs required for the MEPDG are presented in this section.

29

The MEPDG requires PCC mix-related inputs for modeling material behavior. These

are provided in Table 3.14. It is assumed that the concrete sets at a temperature equal to

80°F, as previously discussed in section 3.3.2.

Table 3.14. Mixture properties for the MEPDG.

Input Parameter Value

Cement type Type I

Cementitious material content 588 lb/yd3

Water/cement ratio 0.44

Aggregate type Limestone

PCC-zero stress temperature 80 °F

The PCC modulus of elasticity, Ec, greatly effects deflections and stresses throughout

the pavement structure. Laboratory testing was performed at the University of Pittsburgh in

accordance with ASTM C469 to determine the modulus of elasticity at concrete ages of 1

day, 3, 7, 28 and 365 days. For Level 1 characterization, the modulus of elasticity is needed

at several ages (7, 14, 28, and 90 days). The lab-determined values were plotted using a

logarithmic regression of the test data and the long-term modulus ratio to predict the modulus

of elasticity at any point over the design life of the pavement. The MEPDG recommends a

maximum value of 1.2 for the 20-year to 28-day Ec ratio unless more accurate information is

available. The modulus of elasticity required for Level 1 inputs are presented in Table 3.15

This flexural strength has a significant effect on the cracking potential of PCC slabs.

Similarly to the modulus of elasticity, laboratory testing was performed at the University of

Pittsburgh in accordance with AASTHO T97 to determine the modulus of rupture at concrete

ages of 1 day, 3, 7, 28 and 365 days. For Level 1 characterization, the flexural strength is

needed at several ages (7, 14, 28, and 90 days). The lab-determined values were plotted

using a logarithmic regression of the test data and the long-term modulus ratio to predict the

flexural strength at any point over the design life of the pavement. The MEPDG

recommends a maximum value of 1.2 for the 20-year to 28-day modulus ratio. The modulus

of rupture required for Level 1 inputs are presented in Table 3.15 and Figure 3.10.

30

Table 3.15. PCC strength characteristics required for Level 1.

Age

(days)

Modulus of

Elasticity (psi)

Modulus of

Rupture (psi)

7 3.1 x 106 878

14 3.3 x 106 888

28 3.7 x 106 939

90 4.69 x 106 1025

0

200

400

600

800

1000

1200

0 5 10 15 20Age (years)

Mod

ulus

of R

uptu

re (p

si)

0

1

2

3

4

5

6

Mod

ulus

of E

last

icity

(x

106 p

si)Modulus of Rupture Modulus of Elasticity

Figure 3.10. PCC strength characteristics over the 20-year design life.

General properties of the concrete include unit weight and Poisson’s ratio. And the

thermal properties include the coefficient of thermal expansion, surface short-wave

absorptivity, thermal conductivity and heat capacity. These properties were established for

the Smart Pavement in the Phase II Report [1] and are summarized in Table 3.16.

31

Table 3.16. General and thermal PCC properties.

Input Parameter Value

Unit Weight (lb/ft3) 143.4

Poisson’s Ratio 0.17

Coefficient of Thermal Expansion (/°F) 5.9 x 10-6

Surface Short-Wave Absorptivity 0.85

Thermal Conductivity ( ( )( )( )FhrftBtu o ) 1.25

Heat Capacity ( ( )( )FftBtu o ) 0.24

The drying shrinkage of concrete is a long term process that influences strains in the

material. Drying shrinkage can increase crack susceptibility and joint opening, which affects

the performance of JPCP pavements. The MEPDG requires the following inputs related to

concrete shrinkage: ultimate shrinkage strain, time required to develop 50 percent of the

ultimate shrinkage strain, and anticipated amount of reversible shrinkage. These properties

were also established for the Smart Pavement in the Phase II Report [1] and are summarized

in Table 3.17.

Table 3.17. Shrinkage-related PCC properties.

Input Parameter Value

Ultimate shrinkage (microstrain) 945

Time to Develop 50% Ultimate Shrinkage (days) 10

Reversible Shrinkage (% of ultimate shrinkage) 50

3.3.4 ATPB MATERIAL PROPERTIES INPUTS

The general asphalt inputs are needed for prediction of thermal cracking in the HMA

layer. The general inputs are presented in Table 3.18. The reference temperature, or

temperature at the time of set, was assumed to be equal to 80°F, similar to the temperature at

set for the PCC material, as previously discussed in section 3.3.2.

32

Table 3.18. General asphalt inputs for SR 22.

Input Parameter Value

Reference temperature (°F) 80

Effective binder content (%) 2.5

Air voids (%) 8.5

Total unit weight (lb/ft3) 148

Poisson's ratio 0.35

Thermal Conductivity

(Btu/(ft)(hr)(°F)) 0.62

Heat Capacity (Btu/(lb)(°F)) 0.31

The asphalt mix and binder inputs are also needed to develop a master curve that

relates the dynamic modulus of the asphalt to various temperatures. The dynamic modulus is

the primary stiffness property of interest for asphalt materials. This parameter is a function

of many parameters including: age, binder stiffness, aggregate gradation, binder content, air

voids, and rate of loading. At input Level 3, the asphalt is characterized through sieve

analysis. The required inputs include the cumulative percent of material retained on the 3/4

in, 3/8 in, and #4 sieves and the percent passing the #200 sieve. The asphalt mix and binder

characteristics were determined for the ATPB of the Smart Pavement [1] and are shown in

Table 3.19.

Table 3.19. Asphalt mix and binder characteristics.

Input Parameter Value

Cumulative % Retained 3/4" sieve 28

Cumulative % Retained 3/8" sieve 67.5

Cumulative % Retained #4 sieve 84

% Passing #200 sieve 3

Asphalt Superpave Binder Grading PG64-22

33

3.3.5 GRANULAR MATERIAL AND SUBGRADE PROPERTIES INPUTS

The material inputs required for characterization of unbound granular materials and

the subgrade of a pavement structure include pavement response model material inputs,

EICM material inputs, and other material inputs. The pavement response material inputs

include determination of the resilient modulus and Poisson’s ratio of the various materials.

These inputs are used to characterize the behaviour of the material when subjected to

stresses. The material properties associated with the EICM include grain size distribution,

the Atterberg limits, specific gravity, and hydraulic conductivity. The final classification of

unbound material properties are those required for the design solution, such as the coefficient

of lateral pressure. The pavement response and other material inputs were identified for the

subbase and subgrade of the Smart Pavement in the Phase II report and are summarized in

Table 3.20.

Table 3.20. Granular material and subgrade properties.

2A-Subbase Fill Subgrade

Modulus of Resilience (psi) 19,500 19,500 4,500

Poisson’s Ratio 0.40 0.40 0.40

Coefficient of Lateral Pressure 0.50 0.50 0.50

P200 5% 8% 77%

D60 (in) 0.462 1.063 0.0006

Plasticity Index, PI 10 6 11

Specific Gravity, Gs 2.68 2.69 2.73

Maximum Dry Density, γd max (lb/ft3) 121.6 121.0 110.7

Optimum Gravimetric Water Content, wopt 11.8 12.2 17.2

Saturated Hydraulic Conductivity, ksat (ft/hr) 0.60 2.97 5.68x10-6

Dry Thermal Conductivity, K

(BTU/(ft)(hr)(°F)) 0.20 0.30 0.18

Dry Heat Capacity, Q

(BTU/(lb)(°F)) 0.18 0.18 0.18

34

3.3.6 TRAFFIC INPUTS

Traffic data is required for estimating loads applied to the pavement and frequency of

the applied loads throughout the design life. The load and frequency of loading affect the

stress exerted on the pavement during each load application and the fatigue damage sustained

by the pavement over the design life.

Since the traffic will be modeled using the pavement finite element analysis program

Illislab to determine the concrete stress when the load is applied, a simple truck configuration

is selected. The type of truck used to load the pavement was restricted to a Class 6 truck. A

Class 6 truck typically has 1 single axle and 1 tandem axle, as previously presented in

Chapter 2. However, to simplify the calculations for the fatigue damage and slab cracking

model, a modified Class 6 truck was selected, with the truck load evenly carried by 2 tandem

axles. In addition, to maximize the slab damage, the highest truck load for tandem axles

(82,000 lbs) was selected. The modified truck configuration is shown in Figure 3.11.

Figure 3.11. Axle configuration and load for modified Class 6 truck.

To maximize the expected damage on the slab, a large traffic volume is selected,

along with a high truck growth rate, as summarized in Table 3.21. The roadway is a four-

lane urban major arterial divided by a concrete median. The posted speed limit is 35 miles

per hour, with several traffic signals and business entrances occurring along the roadway.

Since the traffic consists of an 82,000-lb load applied on 2 tandems over the design life, the

total number of load applications is obtained by multiplying the traffic by a factor of 2.

Figure 3.12 shows the projected traffic and the expected number of load applications over the

20-year design life when the pavement was loaded with a truck having the axle configuration

and loads represented in Figure 3.12.

Load (lbs): 82,000 82,000 0

35

Table 3.21. General traffic inputs.

Input Parameter Value

One-way average annual daily truck traffic (AADTT) 25,000

Number of lanes in the design direction 2

Percent trucks in design direction 100

Percent trucks in design lane 100

Vehicle operational speed (MPH) 35

Growth rate (%) 9.5 Compound

0

250,000,000

500,000,000

750,000,000

1,000,000,000

0 5 10 15 20Age (years)

No.

of R

epet

ition

s.

Traffic Load Repetitions

Figure 3.12. Traffic and number of load application predicted over the 20-year design life.

The MEPDG offers the user the flexibility of applying the traffic at a specific month

of the year and a specific time of day. The pavement was modeled using a zero temperature

gradient in the slab and a positive gradient in the slab. It was found that the MEPDG predicts

no damage or cracking for the slab when the loads are applied when a zero temperature

gradient is present in the slab. As a result, the traffic was applied when positive temperature

gradients are present in the slab. These were found to correspond to the month of May at

2:00 PM. The gradient measured during this time was presented in section 3.3.2.

36

The general traffic inputs primarily provide information necessary for calculating

pavement response. These inputs define axle load configuration and provide loading details

such as: traffic wander, design lane width, and mean wheel location. The default values

provided by the MEPDG are used, as shown in Table 3.22. It was initially planned to use a

wander standard deviation of zero, however, the MEPDG does not allow that. Therefore, a

minimum value of 1 in was used instead.

Table 3.22. General traffic inputs.

Input Parameter Value

Average axle width (edge-to-edge) outside dimensions (ft) 8.5

Dual tire spacing (in) 12

Tire Pressure (psi) 120

Tandem axle spacing (in) 51.6

Average axle spacing (ft) 18

Mean wheel location from the pavement marking (in) 18

Traffic wander standard deviation (in) 1

3.4.0 FATIGUE DAMAGE AND SLAB CRACKING RESULTS

The fatigue damage and the slab cracking were predicted by the MEPDG based on

the inputs presented in the previous section. The guide predicted a bottom-up fatigue

damage of 0.11 and a corresponding 1.25 percent slab cracking over the 20-year design life.

The Smart Pavement was modeled using Illislab to determine the concrete stress due

to the applied load and environmental conditions defined above. These finite element models

were developed based on the results from the material property testing and FWD deflection

testing. The models were validated by comparing measured strains with those predicted

using the finite element models [1]. These validated models provide a unique opportunity to

accurately establish the stress in a pavement structure. This stress can then be used, along

with the equations presented above, to determine the fatigue damage and percent cracking

after a pre-established number of loadings.

Two sets of values for the elastic modulus for the ATPB and the static k-value have

been established and were used in the Illislab stress analysis. The first were obtained based

37

on the backcalculated values determined using the falling weight deflectometer deflection

data collected for the Smart Pavement. The backcalculated values were based on the summer

conditions, because they correspond to the season when the slab temperature is closest to the

conditions we are modeling using the MEPDG [1]. The MEPDG also estimates the modulus

of elasticity of the base layer and the dynamic modulus of subgrade reaction representing the

composite stiffness of all the layers beneath the base. The k-values and the elastic moduli

calculated by the MEPDG are presented in Table 3.23 for the first year after construction.

These are seasonal values and get repeated throughout the 20-year design life. The values

estimated for the month of May using the MEPDG were used since traffic is only being

applied during that month. The static k-value was obtained by dividing the dynamic k-value

from the MEPDG by a factor of 2, as recommended by the literature [7]. The modulus of

elasticity of the base layer and the static k-values used in the stress analysis are presented in

Table 3.24.

Table 3.23. Base modulus and dynamic k-value estimated by the guide.

Pavement Age

Month Year

Month Base E

(ksi)

Dynamic k

(psi/in)

1 0.08 September 233.2 266

2 0.17 October 304.9 266

3 0.25 November 406.5 265

4 0.33 December 504.3 265

5 0.42 January 519.5 265

6 0.5 February 422 265

7 0.58 March 318.6 265

8 0.67 April 238.5 265

9 0.75 May 193.2 264

10 0.83 June 173.4 264

11 0.92 July 175.6 264

12 1 August 195 264

38

Table 3.24. Base modulus and k-value used for the stress analysis.

Base E (ksi)

Dynamic k-value (psi/in)

Static k-value (psi/in)

MEPDG-estimated 193.2 264 132 Backcalculated 322.0 -- 190

Since traffic was applied with a wander standard deviation of 1 in, the stress due to

each application is different depending on the exact location of the truck. Therefore, for each

set of base and foundation parameters, Illislab was used to estimate the stress due to the

location of the truck loading at three wheel locations: at 18 in and 18 in ± 1 in from the lane

marking (or 24 in and 24 in ± 1 in from the slab edge). In total, the concrete stress was

determined for 6 different cases, and the results are presented in Table 3.25. Based on the

table, the stress estimated based on the MEPDG Ebase and k-values is 17 percent larger than

the stress estimated based on the calibrated values.

Table 3.25. Concrete stress based on the different traffic loading configurations and base material parameters.

Truck Loading distance from

slab edge

Truck Loading distance from

pavement marking

Stress based on MEPDG Ebase and

k-value (psi)

Stress based on Calibrated Ebase and k-value (psi)

23 17 268 228

24 18 265 225

25 19 264 226

The concrete stress was used in the fatigue damage and cracking model equations

presented in section 3.2.0. The results are compared in Figure 3.13 and Figure 3.14. In these

figures, the curves are labeled according to the source of the Ebase and k-value and the

location of the traffic loading. B refers to the backcalculated values and G refers to the

MEPDG-estimated values, while the 17, 18 and 19 refer to the location of the truck loading

from the pavement marking line. The figures show that using the stress estimated based on

the MEPDG foundation parameters largely overestimates the slab damage. The total damage

at the end of the 20-year design life is summarized in Table 3.26. The results indicate that

the slab damage increases drastically when the stress in the concrete increases from 225 psi

39

to 265 psi. This implies that the critical stress level causing the slab to crack beyond 50

percent falls between 225 and 265 psi.

0

0.25

0.5

0.75

1

1.25

1.5

0 5 10 15 20Age (years)

Fatig

ue D

amag

e

B-17 (228 psi) B-18 (225 psi) B-19 (226 psi)G-17 (268 psi) G-18 (265 psi) G-19 (264 psi)Predicted MEPDG

Figure 3.13. Slab fatigue damage over the 20-year design life.

0

10

20

30

40

50

60

70

0 5 10 15 20Age (years)

Slab

Cra

ckin

g, %

.

B-17 (228 psi) B-18 (225 psi) B-19 (226 psi)G-17 (268 psi) G-18 (265 psi) G-19 (264 psi)Predicted MEPDG

Figure 3.14. Slab cracking over the 20-year design life.

40

Table 3.26. Fatigue damage and cracking at the end of the 20-year design life.

Fatigue

Damage Slab Cracking (%)

Predicted by the MEPDG 0.11 1.25

Based on foundation parameters from

the MEPDG 0.97 – 1.39 48.55 – 65.76

Based on backcalculated foundation

parameters 0.01 – 0.02 0.02 – 0.04

The percentage of slab cracking was calculated for different stress levels and the

relationship is plotted in Figure 3.15. The figure shows that a concrete stress of 264 psi

results in a 50 percent slab cracking, which indicates slab failure. This curve shows that

there is a large change in percent cracking for very small changes in stress between 250 psi

and 268 psi. This helps to explain the vast differences in the predicted performance reported

in table 3.17 even though the calculated stresses summarized in Table 3.25 were all within 43

psi. It is also emphasized the need to accurately characterize the stress.

0

10

20

30

40

50

60

70

220 230 240 250 260 270 280Stress (psi)

Slab

Cra

ckin

g (%

) .

Figure 3.15. Critical stress causing 50 percent slab cracking.

41

CHAPTER 4: CONCLUSIONS AND RECOMMENDATIONS

A list of the conclusions from this study is summarized below. This is followed by

recommendations for future research.

CHAPTER 2: STRESS IN THE SMART PAVEMENT

• The largest positive gradient that has developed in the Smart Pavement since it was

constructed was +2.22 oF/in and the largest negative gradient is -1.72 oF/in. Vehicle

loads applied when these gradients are present produce very large stresses, especially

for the restrained slabs. Fortunately these gradients were only present in the slab for a

very short period of time.

• Stress in the restrained slabs was approximately 28 percent larger than stress in the

unrestrained slabs when averaged for all the runs. However, when looking at the

difference in stress between the restrained and unrestrained slabs for each load level,

the difference between the two decreases as the load magnitude increases.

CHAPTER 3: EVALUATION OF THE FATIGUE CRACKING MODEL OF THE MEPDG

• The stress estimated based on the MEPDG modulus of elasticity of the base and k-

values is 17 percent larger than the stress estimated using FWD deflection data.

• Using the stress estimated based on the MEPDG foundation parameters largely

overestimates the slab damage.

• The slab damage increases drastically when the stress in the concrete increases from

255 psi to 265 psi. This emphasizes the need to accurately characterize the stress.

• The MEPDG predicted 1 percent cracking. When the finite element model validated

using the strain measurements was used to predict the stress to be used in the

performance prediction models, 0 percent cracking was predicted. This was based on

using moduli of the support layers that were backcalculated using FWD deflection

data. About 45 to 65 percent cracking was predicted when the moduli calculated

using the MEPDG was used in the finite element models to predict stress.

42

The following recommendations were developed based on the findings from this study:

• The finite element models developed in this research should be used in future

research to evaluate stress in the pavement for a wider range of temperature and

moisture conditions, support conditions, and a wider range of vehicle loads and

configurations.

• Further research is needed in evaluating the accuracy of stress prediction in the

fatigue cracking model of the MEPDG. The faulting model should also be evaluated.

• Currently there is very little knowledge in regards to understanding the time it takes

for the moisture content in the middle portion of the slab to stabilize. After three

years, the moisture content still has not stabilized. It would be of great benefit to

continue collecting moisture data until this has occurred. It would also be of benefit

to continue collecting static strain gage and temperature data so that the effects of the

moisture content on slab deformation can be characterized. It is suggested that the

data be collected for an additional two years.

43

REFERENCES

1. McCracken, J.K., R.E. Asbahan, and J.M. Vandenbossche, (February 2008), “S.R. –

22 Smart Pavement Phase II: Response Characteristics of a Jointed Plain Concrete Pavement to Applied and Environmental Loads; Phase II Final Report,” Pennsylvania Department of Transportation and the Federal Highway Administration, University of Pittsburgh, Department of Civil and Environmental Engineering, Pittsburgh, Pennsylvania.

2. Wells, S. A., B.M. Phillips, and J.M. Vandenbossche, (June 2005), “S.R.-22 Smart

Pavement Phase I: Early-Age Material Properties and Pavement Response Characteristics for Jointed Plain Concrete Pavements; 28-Day Report Final Revision,” Submitted to the Pennsylvania Department of Transportation and the Federal Highway Administration, University of Pittsburgh, Department of Civil and Environmental Engineering, Pittsburgh, Pennsylvania.

3. Asbahan, R., J. McCracken, and J.M. Vandenbossche, (November 2006), “S.R.-22

Smart Pavement Phase II: One-Year Material Properties and Pavement Response Characteristics for Jointed Plain Concrete Pavements,” Pennsylvania Department of Transportation and the Federal Highway Administration, University of Pittsburgh, Department of Civil and Environmental Engineering, Pittsburgh, Pennsylvania.

4. ARA, Inc., ERES Consultants Division, (March 2004), “Guide for Mechanistic-

Empirical Design of New and Rehabilitated Pavement Structures. Final Report,” National Cooperative Highway Research Program, Transportation Research Board, National Research Council, Champaign, Illinois.

5. Dempsey, B. J., W. A. Herlach, and A. J. Patel, (1985), The Climatic-Material-

Structural Pavement Analysis Program, FHWA/RD-84/115, Vol.3., Final Report, Federal Highway Administration, Washington D.C.

6. Vandenbossche, J.M., (November 2003), “Interpreting Falling Weight Deflectometer

Results for Curled and Warped Portland Cement Concrete Pavements,” Civil Engineering, University of Minnesota, Doctor of Philosophy Dissertation.

7. American Association of State Highway and Transportation Officials, (1993),

“AASHTO Guide for Design of Pavement Structures,” AASHTO, Washington, D.C.