development and evaluation of performance tests to …...itd project no. rp 181 (niatt project no....

Development and Evaluation of Performance Tests to Enhance Superpave Mix Design and Implementation in Idaho

USDOT Assistance No. DTOS59-06-G-00029 (NIATT Project No. KLK479)

ITD Project No. RP 181 (NIATT Project No. KLK483)

Quarterly Progress Report – QR5 For the period

July 1 to September 30, 2008

Submitted to

U.S. Department of Transportation

Ed Weiner, COTR

And

Idaho Transportation Department Ned Parrish, Research Manager

Michael J. Santi, PE, Assistant Material Engineer

UI Research Team Dr. Fouad Bayomy, PI Dr. S. J. Jung, Co-PI Dr. Thomas Weaver, Co-PI Dr. Richard Nielsen, Co-PI Mr. Ahmad Abu Abdo, Graduate Research Assistant Mr. Seung II Baek, Graduate Research Assistant Mr. Prashant Darveshi, Graduate Research Assistant University of Idaho (UI) National Institute for Advanced Transportation Technology (NIATT) Center for Transportation Infrastructure (CTI)

October 22, 2008

1. Introduction

This is the fifth quarter report (quarter one of year 2) of the project which summarizes progress during the period July to September 2008. The focus during this period addressed several tasks as will be discussed in the report. In previous reports, a description of the project objectives and work plan has been presented. These reports are posted on the designated project reports web page at: http://www.webs1.uidaho.edu/bayomy/KLK479-483/QReports.htm. This QR5 report focuses on progress during the 5th quarter of the project. Description of work progress is presented on a task by task basis.

2. Progress by Task

The chart in Table 1 summarizes the progress as % work completed as of September 30, 2008. Table 1 Approximate Level of Work Completed by Task at the end of Quarter 5

Phase / TaskQuarter

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

Task A1 – Review of previous studies and available data 10% 10% 2% 0% 2% 6% 0% 10% 10% 20% 70%

Task A2 – Analytical Analysis 12% 2% 4% 0% 7% 0% 5% 5% 15% 5% 55%Task A3 – Experimental Design, Binder and Agg. Eval. 15% 15% 10% 5% 15% 0% 5% 10% 10% 0% 85%Task A4 – Prep and Evaluation of Asphalt Mixtures 5% 5% 10% 15% 20% 15% 15% 0% 0% 0% 85%

Task A5 – Data Analysis 10% 0% 5% 10% 10% 20% 20% 10% 85%

Task B1 – Literature Review 10% 15% 5% 5% 5% 5% 0% 5% 0% 50%

Task B2 – Finite Element Analysis 5% 5% 5% 5% 15% 5% 2% 3% 5% 10% 5% 65%Task B3 – Development of the Fracture Test Procedure 12% 2% 5% 16% 5% 5% 10% 20% 20% 15% 110%Task B4 – Prep and Evaluation of Asphalt Mixtures 0% 15% 10% 15% 20% 20% 10% 5% 95%

Task B5 – Data Analysis 10% 30% 20% 60%

Task B6 – Reliability Analysis 5% 3% 5% 12% 11% 36%

Task C1 – Development of Implementation Plan 0%

Task C2 – Training Program for ITD Personnel 0%

Tasks A6, B7 and C3 – Quarter Reports for USDOT R1 R2 R3 R4 R5 R6 R7 Final 0%

Task D1: External peer review of the final report 0%

Task D2: Final report review by ITD 0%

Task D3: Final Report Submittal 0%

Phase C: Implementation of Research Products and Training

Reporting

Phase D: Final Report Review and Submittal

Q2 Q3 Q4Calendar Yr 2007

Year 1 Year 2 Year 3

Phase A: Evaluation of Mix Resistance to Deformation

Phase B: Evaluation of Mix Resistance to Fracture and Fatigue Cracking

Tot

al %

T

ask

Com

plet

ed

Q1 Q2 Q3 Q4 Q1Calendar Yr 2008 Calendar Yr 2009

Work performed during the report period in the various project tasks is described below:

1

http://www.webs1.uidaho.edu/bayomy/KLK479-483/QReports.htm

Phase A: Evaluation of Mix Resistance to Deformation Task A1 – Review of previous studies and available data Since the end of the fourth quarter, no additional literature review has been completed. The work completed in this task is estimated at about 70%. Task A2 – Analytical Analysis Finite element analyses have been performed to model asphalt pavement behavior using a viscoplastic constitutive model. The viscoplastic model parameters have been back calculated using dynamic modulus, E-star, test results. A correlation between the primary model parameter and testing conditions has been developed. A summary of the analyses and results is presented in Appendix A. The work completed in this task is estimated at about 55% Task A3 – Experimental Design, Binder and Aggregate Evaluation Binder testing: All binder testing is now completed, and has been reported in previous quarter reports. During this quarter, no additional experiments or evaluations were completed. The work completed in this task is estimated to be about 85%. Task A4 – Preparation and Evaluation of Asphalt Mixtures Testing and evaluation of asphalt mixtures has focused on fatigue fracture testing and is discussed under Task B4. Image analysis and MnRoad sample preparation is still pending. Gyratory Stability and E-star and Flow number Testing have previously been completed. Work completed in this task is still estimated by about 85% Task A5 – Data Analysis Data analysis consisted of assessing rutting using the MEPDG with E-star values obtained using the predictive relationship developed as part of this project, measured E-star values, and E-star values from predictive relationships by Witczak (1996, 2006). The results of these analyses are presented in Appendix B and show that the predictive relationship for E-star developed for this project results in predicted rutting values very similar to predicted rutting values using measured E-star values. The rutting predicted using the E-star values obtained from the Witczak predictive relationships differed significantly from the rutting obtained using measured E-star values. A summary of the data analysis is presented in Appendix B. Work completed in this task is estimated by about 85%

2

Phase B: Evaluation of Mix Resistance to Fracture and Fatigue Cracking Task B1 – Literature Review on Fracture The reviewed literature was presented in QR2 and QR. No further review was conducted in this quarter. The work completed in this task is still estimated to be about 50%. Task B2 – Finite Element Analysis Work in this task overlaps two phases (Task A2 and Task B2). Use of FE under task A2 focuses on the constitutive modeling under the numerical analysis. The work developed using the ABAQUS software package is reported under task A2 and is presented in Appendix A. Under task B2, the use of FE analysis addresses the simplified fatigue model. The effort spent in this task, so far, is estimated by about 65% of the work level in this task. Task B3 – Development of the Fracture Test Procedure A review of the literature shows there is not a single accepted method for cyclic fatigue fracture testing of asphalt pavement. Therefore, cyclic fatigue fracture tests are being conducted using a range of test conditions consistent with procedures reported in the literature. The ultimate goal is to correlate cyclic fatigue fracture testing results with static test results. Thus, in the future, only static testing procedures will be required to evaluate the fatigue fracture behavior of asphalt pavement. Cyclic fatigue fracture tests have been performed at loading frequencies from 0.1 Hz to 5 Hz at load ranges from 20% to 70% of the maximum static sample strength. A summary of cyclic fatigue fracture testing is presented in Appendix C. The work completed in this task so far is estimated at about 110%, indicating that this task is taking more effort than anticipated. The original task time was planned over 9 months. We suggest expanding the time for this task to end of March 2009 (total of 13 months). Hence, the % completed in this task would be about adjusted to 77% Task B4 – Prep and Evaluation of Asphalt Mixtures Preparation of asphalt samples for fatigue and fracture are nearly complete. The work completed in this task so far is estimated at about 95%. Tasks B5 – Data Analysis . Results from cyclic fatigue fracture testing and static testing are being analyzed to develop a method for predicting fatigue and fracture behavior of asphalt pavement using only static tests. Analysis of the tests conducted to date is presented in Appendix C. The work completed in this task so far is estimated at about 60%.

3

4

Task B6 – Reliability Analysis Reliability analyses over the past quarter have focused on MEPDG analyses using predictive relationships for E-star. A summary of the reliability work over the past quarter is presented in Appendix B. The work completed in this task so far is estimated by about 36%.

3. Summary

The main outcomes that have been achieved during this quarter can be summarized as follows: 1. Viscoplastic model parameters can be predicted at temperatures of 4 and 21 degrees C at

various loading frequencies. These correlations with correlations at other temperatures is anticipate to provide a reliable method for assessing deformation characteristics of asphalt pavements using the finite element method.

2. The predictive relationship for estimating E-star using basic pavement material properties has been show to provide very good estimates of pavement performance (rutting and cracking) compared to using measured E-star values implemented in the MEPDG.

3. Results from the fatigue fracture testing shows similar displacements for the same level of strain energy. This indicates that the a static test can be used to assess the fatigue fracture behavior of asphalt pavement.

Appendices Appendix A: Viscoplastic Analyses of Asphalt Pavements (Task A2) Appendix B: Data and Reliability Analysis (Tasks A5 and B6) Appendix C: Fracture Test Procedure and Data Analysis (Tasks B3 and B5)

Appendix A Viscoplastic Analyses of Asphalt Pavements (Prepared by: T. Weaver and P. Darveshi)

Constitutive models (stress‐strain relationships) are used in finite element analysis programs to calculate the response of materials under various loadings and boundary conditions. Powerful constitutive models have already been developed showing some success in modeling asphalt material behavior. The limitation of these models is that they require a large number of parameters to define the material behavior. A relatively simple viscoplastic model which requires only a few model parameters has shown promise for assessing asphalt pavement behavior. The goal of the viscoplastic analyses of asphalt pavements has focused on developing a correlation between the viscoplastic model parameters and easily defined physical variables. Results of our progress to date are reported below.

Results of Finite Element Analyses

Viscoplastic analyses of asphalt pavement have consisted of modeling E* tests conducted at the University of Idaho. The purpose of the analyses was to back‐calculate the model parameters that result in a good comparison of measured and predicted pavement response. This data has then been used to investigate relationships between the viscoplastic model parameters and pavement properties. To date, model parameters have been back calculated for two different field mixes (Mix 1 and Mix 6) at three different temperatures for a total of 23 analyses. These two mixes were chosen to investigate the influence mix gradation has on the back‐calculated model parameters. Mix 1 is a coarse mix while Mix 6 is a fine mix. Both mixes had the same binder grade. Mix properties are listed in Table 1 and the gradation of the aggregates is shown in Figure 1. The E‐star tests analyzed using the finite element procedure are listed in Table 2 with the testing temperature, frequency of loading, and the back‐calculated model parameter A. In addition to the model parameter A, the viscoplastic model requires two additional parameters, m and n, as well as the pavement modulus of elasticity and Poisson’s ratio. The pavement modulus of elasticity input into the analyses was obtained from the E‐star tests. Poisson’s ratio was assumed to be equal to 0.3, and the values of m and n were found to be constant at 0.08 and ‐0.045, respectively. The results in Table 2 are plotted in Figure 2. It is anticipated that the predictive relationship developed from this project can be used in the future for estimating the elastic modulus that is used in the model.

Predictive Relationship for Model Parameters

Initially, we hypothesized that the model parameter A would be a function of the binder properties, aggregate properties, compaction of the aggregate and binder, frequency of loading, and temperature of the material. As illustrated in Figure 1, the parameter A appears to be a function of only temperature

QR5_Appendix A: Viscoplastic Analysis of Asphalt Pavements - Page 1

and frequency of loading. Using the data in Table 1, regression analyses have been performed to determine the relationship between the model parameter A and frequency of loading at temperatures of 4 and 21 degrees Celsius. There were not enough results from analyses at 38 degrees C for a regression analysis. Results of the regression analyses are shown in Figure 2. The regression analyses show a high coefficient of determination at each temperature independent of other mix properties.

Summary and Conclusions

Results from the finite element analyses show that the parameter A used in the viscoplastic model can be reasonably predicted knowing the asphalt pavement temperature and frequency of loading. Additional analyses are required to develop relationships at other temperatures of interest. These correlations coupled with the model developed as part of this project for predicting E‐star will ultimately be used to perform finite element based deformation analyses on asphalt pavement without the need for extensive and expensive laboratory testing for material property determination.


Table 1 Field Mix Properties for Finite Element Analyses

Field Mix ID 1 6

Class SP4 SP3

ESALs >30x106 3 ‐ 30x106

N‐design 125 100

Mix Proportion

Gmm 2.449 2.581

AV% 4% 4%

Sample wt,gr 4700 ‐

Binder Properties

PG 70‐28 70‐28

pb% 4.90% 5.90%

Gb 1.021 1.036

Mix Temp, F 330 328

Comp. Temp, F 305 305

Aggregate Properties

Gsb 2.586 2.822

Gse 2.639 ‐

Absorption 1.3% ‐

% passing, Sieves 25 mm (1") 98% 100%

19 mm (3/4") 86% 100%

12.5 mm (1/2") 73% 96%

9.5 mm (3/8") 64% 87%

4.75 mm (#4) 41% 58%

2.36 mm (#8) 27% 36%

1.18 mm (#16) 18% 22%

600 um (#30) 13% 17%

300 um (#50) 10% 13%

150 um (#100) 5% 8%

75 um (#200) 4.0% 6.4%


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0.010.101.0010.00

Percen

t Passing

Grain Size, mm

Mix 1

Mix 6

Figure 1 Aggregate grain size distribution of field mix 1 and 6


Table 2 Results of Viscoplastic Finite Element Analyses

Mix

Temperature Frequency Back‐Calculated A °C Hz

Field Mix 1

4 0.1 2.50E‐074 0.5 1.00E‐064 1 1.50E‐064 5 4.00E‐064 10 5.00E‐064 25 9.00E‐06

21 0.1 2.50E‐0721 0.5 1.50E‐0621 1 3.50E‐0621 5 1.50E‐0521 10 3.00E‐0538 0.5 6.00E‐0738 1 1.00E‐06

Field Mix 2

4 0.1 1.50E‐074 0.5 1.00E‐064 1 9.00E‐074 5 3.00E‐064 10 5.00E‐064 25 8.00E‐06

21 0.1 3.50E‐0721 1 3.50E‐0621 5 1.20E‐0521 10 2.50E‐05


0.00E+00

5.00E‐06

1.00E‐05

1.50E‐05

2.00E‐05

2.50E‐05

3.00E‐05

3.50E‐05

0 5 10 15 20 25 30

Back‐Calculated Parameter A

Frequency of Loading, Hz

Mix 1, T = 4C

Mix 6, T = 4 C

Mix 1, T = 21 C

Mix 6, T = 21 C

Figure 2 Back‐calculated parameter A from Field Mixes 1 and 6 at Temperatures of 4 and 21 degrees C vs the loading frequency

y = 3E‐07x + 1E‐06R² = 0.9394

y = 3E‐06x + 3E‐07R² = 0.9821

0.00E+00

5.00E‐06

1.00E‐05

1.50E‐05

2.00E‐05

2.50E‐05

3.00E‐05

3.50E‐05

0 5 10 15 20 25 30

Back‐Calculated A

Frequency of Loading, Hz

Mix 1 and 6, T = 4 C

Mix 1 and 6, T = 21 C

Figure 3 Regression analyses for estimating the parameter A at Temperatures of 4 and 21 degrees C vs. at loading frquencies from 0 to 25 Hz.


Appendix B MEPDG Results and Reliability Analysis (Prepared by: A. Abu Abdo, F. Bayomy, and R. Nielson)

To verify if predicted E* can be used in the MEPDG instead of actual E* test data, MEPDG trial runs were conducted for Mix 1 and Mix 2 at optimum condition using E* from the proposed model and E* from pavement test data. In addition to actual and proposed model E* values, MEPDG Level‐3 E*inputs (Witczak’s model 1996) and Witczak’s revised model (2006) were used for comparison. MEPDG results (Error! Reference source not found.) showed that the permanent deformation (rutting) predicted by the proposed model E* values were closer to rutting predictions based on actual pavement test E* values. Level‐3 inputs (Witczak’s model 1996) yielded very high rutting results in contrast to Witczak’s (2006) model, which yielded very low rutting values. The average percent error was determined to be 12.6%, 86.6%, and 60.3% for permanent deformation and 12.4%, 34.6%, and 43.6% for alligator cracking when proposed, Level 3, and Witczak’s (2006) model were compared respectively to results from actual E* values.

0

1

2

3

4

5

6

7

8

0 50 100 150 200 250 300

Rutting, mm

Months

E*‐ Actual

E*‐ Proposed Model

E*‐Witczak Model 1996


0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 50 100 150 200 250 300

Rutting, mm

Months

E*‐ Actual




0

1

2

3

4

5

6

0 50 100 150 200 250 300

Alligator Cracking, %

Months

E*‐ Actual




0

0.5

1

1.5

2

2.5

0 50 100 150 200 250 300

Alligator Cracking, %

Months

E*‐ Actual




a) Mix 1 b) Mix 2

Figure 1: Results of MEPDG Trial Runs

QR5_Appendix B: Data and Reliability Analysis - Page 1

In addition, MEPDG trial runs were conducted for 6 field mixes. As shown in Figure 2, pavement distresses (e.g. permanent deformation, alligator cracking, and longitudinal cracks) predicted with 50% reliability by the proposed model were closer to those based on the actual E* than distresses based on E* from the MEPDG Level‐3 analysis (Witczak’s 1996 Model).

0

1

2

3

4

5

6

7

8

9

10

Mix 1 Mix 2 Mix 3 Mix 4 Mix 5 Mix 6

Rutting, mm

E*‐Measured

E*‐Proposed Model


0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Mix 1 Mix 2 Mix 3 Mix 4 Mix 5 Mix 6Alligator Cracking, %

E*‐Actual



a) Permanent Deformation b) Alligator Cracking

0

5

10

15

20

25

Mix 1 Mix 2 Mix 3 Mix 4 Mix 5 Mix 6

Longitudinal Cracking, (m

/km)

E*‐Actual

E*‐Proposed Model


c) Longitudinal Cracks

Figure 2: Field Mixes Results of MEPDG Trial Runs

QR5_Appendix B: Data and Reliability Analysis - Page 2

Appendix C Fatigue Fracture Testing and Data Analysis (Prepared by: S.J. Jung and S. Baek)

Currently, cyclic testing procedures are not standardized for assessing the fatigue fracture behavior of asphalt pavements. The purpose of the cyclic fatigue fracture testing for this project is to correlate the cyclic test results with result from static fracture tests. The ultimate goal of the fatigue fracture testing and data analysis is to prove that static testing can be used to appropriately assess the fatigue fracture behavior of asphalt pavements. Until now, total sixty eight samples of both cyclic and static have been tested with several PG grades with the same asphalt content. The PG grades are following: PG 60‐22, PG 60‐28, PG 60‐34, PG 70‐22, PG 70‐28 & PG 70‐34 with 4.9% of AC. Cyclic test conditions were based on test parameters in Table 1 which have been utilized by other researchers. The cyclic test was controlled with force ranges (20‐40%, 30‐40%, 30‐50% and 30‐70% of maximum strength of the sample) to understand the correlation between the static and cyclic fatigue test. Table 1 Test conditions

Temperature (°C) Strain rate (10‐6 units/s)

Frequency (Hz) Monotonic Cyclic

Daniel [3] 5, 20 5, 12, 20 10, 30, 500, 1500 1, 10 Lundstrom [4] 0,10,20 100, 200, 400, 800 10 Medani [5] 5, 15, 20, 25, 30 for n 10‐50 H. J. Lee [6] 25 5

The static test was conducted with displacement control on different samples to compare the static test results with cyclic test. To compare the outcome of the both tests, stain energy was determined for comparison of static and cyclic with 0.1 Hz, 1 Hz and 5 Hz from the test results with several PG grade and same asphalt content.

During the cyclic test, fracture propagation was able to be detected by using the digital scope camera. Figure 1 illustrates the fracture growth during an experiment. Aggregates in the sample influence fracture behavior preventing travel through the aggregate.

Figure 2 and Figure 3 illustrate that strain energy from both cyclic loading and static tests have a similar trend within the reasonable range.

Static and cyclic tests indicated that results were significantly controlled by the testing parameters including loading range, type of mixture, size distribution of aggregate, geometry of the sample. Especially loading range controls duration of cyclic test which may be able to interpret the outcome of experiment with strain energy. The outcome of controlled parameter (Figure 2) illustrates better

QR5_Appendix C: Fracture Fatigue Testing and Data Analysis - Page 1

correlation between displacement and strain energy than outcome of uncontrolled tests which plot relationship of all test data range (Figure 3).

a) Initial testing state b) Crack initiated at the tip of notch

c) After testing state

Figure 1 Static test sample with 50*10‐6 in/sec


Figure 2 Cyclic loading (1 Hz and 0.1 Hz)


Figure 3 Displacement vs. strain energy of all tested data

development and evaluation of performance tests to …...itd project no. rp 181 (niatt project no....

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