A. Scarpas et al. (Eds.), 7th RILEM International Conference on Cracking in Pavements, pp. 307–316. © RILEM 2012

Preliminary Analysis of Quality-Related Specification Approach for Cracking on Low Volume Hot Mix Asphalt Roads

David J. Mensching1, Leslie Myers McCarthy2, and Jennifer Reigle Albert3

1 Graduate Assistant, Villanova University, 800 East Lancaster Avenue, Villanova, PA, USA 19085 david.mensching@villanova.edu

2 Assistant Professor of Civil Engineering, Villanova University, 800 East Lancaster Avenue, Villanova, PA, USA 19085 leslie.mccarthy@villanova.edu 3 Assistant Professor of Civil Engineering, Pennsylvania State University – Harrisburg, 777 West Harrisburg Pike, Middletown, PA, USA 17057 jaa23@psu.edu

Abstract. During the last twenty years, efforts have been made to implement performance-related specifications (PRS) for hot mix asphalt (HMA) construction in the United States. The National Cooperative Highway Research Program (NCHRP) Project 9-22: Beta Testing and Validation of Hot Mix Asphalt Performance-Related Specifications created software using models similar to those in the interim American Association of State Highway and Transportation Officials (AASHTO) Mechanistic-Empirical Pavement Design Guide (MEPDG). The program predicts an effective dynamic modulus (E*) parameter to determine information pertaining to major distress types. In this study fatigue cracking (bottom-up/alligator cracking) is analyzed. A predicted life difference (PLD) is then calculated between job mix formula (JMF) and as-built conditions, resulting in an assigned pay factor. In this study, a low volume HMA roadway in rural Rhode Island was analyzed using the volumetric-based models. A sensitivity analysis was conducted by varying asphalt contents, in-situ air void targets, and dust-to-asphalt ratios to evaluate their effects on fatigue cracking levels. The aim was to assess the suitability of the software as a tool for pay factor development in Rhode Island. Based on preliminary results, results are significantly sensitive to changes in JMF target in-situ air voids. Future considerations regarding pay factor development for low volume roadway projects include: added costs or savings as a result of implementation, the development of a more simplistic method of computing pay factors, and comparing results with pavement management system (PMS) information on other comparable flexible highway pavements in Rhode Island.

1 Introduction

This paper presents the results of a preliminary analysis involving a potential pay factor specification for traditional fatigue cracking on low volume hot mix asphalt

308 D.J. Mensching, L.M. McCarthy, and J.R. Albert

(HMA) roadways. Through National Cooperative Highway Research Program (NCHRP) Project 9-22: Beta Testing and Validation of HMA PRS, a software program geared towards the comparison of performance predictions for job mix formula (JMF) and as-built HMA characteristics was created for pay factor development [1]. Prediction models based on the Witczak Predictive Equation (WPE) and internally-conceived closed-form solutions (CFS) similar to those featured in the American Association of State Highway and Transportation Officials Mechanistic-Empirical Pavement Design Guide (MEPDG) software predict service life values, which directly relate a predicted life difference between the as-built and JMF lives to a distress-specific pay factor.

This study explored software predictions for alligator cracking pertaining to a full-depth reconstruction HMA project paved in the state of Rhode Island (RI) during the 2010 construction season. Rhode Island is the smallest state in the United States and located in the northeast region between New York City and Boston. The objectives were to utilize a statistical test measure (linear regression t-test) to determine the significance of changes in three critical parameters identified during construction: asphalt content by weight (AC%), dust-to-asphalt ratio (D/A), and target in-situ air voids (AV); and to provide conclusions and recommendations to determine other testing methodologies to further analyze the software’s tendencies. If the volumetric parameters proved significant, further investigation could be conducted by agencies to adjust specification limits or values in an effort to maximize predicted life for the pavement in the fatigue cracking distress mode. A research approach was devised with the intent to perform the following tasks: 1) collect pertinent project data as software inputs, 2) develop statistical significance methodology, 3) conduct a preliminary sensitivity study using three critical volumetric factors identified by the Rhode Island Department of Transportation (RIDOT), and 4) provide recommendations for additional study with the optimal goal being to better understand prediction behavior for specification enhancement.

2 Background

Development of performance-related specifications (PRS) was initiated in 1988 with the publication of NCHRP Project 10-26 and the formation of a Transportation Research Board (TRB) steering committee, which identified PRS as a matter of high-priority in the area of asphalt research. The Federal Highway Administration (FHWA) then cited PRS to be a High Priority National Area with the objective to “develop and implement specification based on effective predictors of pavement performance with appropriate incentive/disincentive clauses based on those predictors” [2]. Many efforts have been made towards developing a conceptual framework for PRS [3, 4]. In a report by Epps et al. [4], the need for prediction models using mechanistic-empirical theory integrated as part of PRS resulted in a software application entitled HMASpec. HMASpec used a life-cycle cost factor to compute a pay adjustment [5] to construction project final contract amounts.

Preliminary Analysis of Quality-Related Specification Approach 309

Aside from the efforts of the NCHRP Project 9-22 and Westrack projects, there were a few studies in particular that outlined the advantages of widespread PRS implementation and efforts on development of PRS on a more localized level. The advantages of a PRS were reported to vary from minimized life-cycle costs, to the consideration of lot variability, and the incentive/disincentive system that stems from these methods [3]. These factors provide benefits to both transportation agencies and contractors, as transportation agencies can set their own performance limits, obtaining a level of confidence that the pavement will not fail over a given period of time based on predictive models. Contractors could then produce a mix that would provide substantial performance characteristics resulting in an incentive, rather than risk producing an underperforming section resulting in a disincentive payment.

Explorations of localized (state-level) PRS for HMA construction were done in a study by Buttlar and Harrell [6]. The researchers stressed the importance of a framework for the progression to more advanced types of specifications, such as end-result specifications (ERS) and PRS, citing the need for statistical quality assurance (QA) procedures and a development of performance-based pay factors.

A study done for Arizona DOT (ADOT) sought to implement a PRS plan and characterization of HMA performance to be used in models for evaluation of HMA construction jobs in the state [7]. In the report, databases for prediction of the three critical distresses (rutting, alligator cracking, and thermal fracture) were constructed, as well as a framework for the ultimate development of a field validation procedure based on the Asphalt Mixture Performance Tester (AMPT). The study demonstrated that prediction models derived through database creation yield localized calibrations that will produce relevant results. This finding has the potential to be paramount to gaining the confidence of transportation agencies and the paving industry.

Over the course of the last decade, efforts have been made to drastically change the methods in which QA and incentive/disincentive specifications are utilized in transportation agencies. Through NCHRP Project 9-22, the preliminary research-grade software utilized in this study was developed to provide agencies with a resource for the implementation of a quality-related specification. The software has the capability to predict pavement service life as a tool for assigning incentives or disincentives to contractors, based on adherence to specifications and performance standards. Industry experts have expressed the need for performance-based evaluations of asphalt mixtures as a whole, believing that acceptance quality characteristics (AQCs) should be tied more to the overall performance of a pavement as opposed to strictly volumetric-based QA protocols. With the MEPDG selected as the basis for prediction models the software was created to compare service life factors for the as-designed and as-built conditions [1]. An enhanced version of the software program is still under development in NCHRP Project 9-22A: Field Validation of QRSS Version 1.0. It has been designed to operate in a systematic fashion, where JMF analysis is performed before as-built comparisons on pavement performance can be made. The input to the program includes mixture volumetrics, design features, traffic, and sampling data among other items. The output includes stiffness properties and measures of performance

310

such as predicted life dif[1]. This study will utilsignificance of changes as

3 Project Site Descr

In this study, one HMA application of quality-relstudy site for NCHRP Prof Rhode Island-102 (RIsection of the pavement p

Fig. 1. Cross-sec

This portion of the higan annual average daily trinformation from RIDOTthroughway for western Rroadway features one laneassumed design speed of Pavement Management Scrack sealed in 1999, and1987, with 38.1 mm (1.modified binder lift, 127 inch) of A-1-a subbase mtreatment in 1985.

4 Test Setup

For proper manipulation ogathering the required inp

D.J. Mensching, L.M. McCarthy, and J.R. Albe

fference (PLD), distress, and pay factors for the projelize E* outputs only in attempts to test the statistics a result of changes in AC%, D/A, or AV.

ription

full-depth reconstruction section was selected for a trilated specification analysis. The project, included as oject 9-22A, was a 1.9 kilometer (km) (1.2 mile) stretc-102) in Foster, Rhode Island. Figure 1 shows a cros

profile of RI-102 based on job specifications:

ction of RI-102 reconstruction project (not to scale)

ghway can be classified as a rural-principal arterial, witraffic (AADT) of 3,500 vehicles per day (vpd), based o

T officials and AADT maps. RI-102 acts as a north-soutRhode Island, traveling through several rural areas. The in each direction and displays a posted speed limit an56 kilometers-per-hour (35 miles-per-hour). The RIDO

Systems (PMS) showed that this stretch of roadway wad a 0.6-km (0.4-mile) segment was fully-reconstructed i.5 inch) of Class I-1 HMA, 38.1 mm (1.5 inch) of mm (5 inch) of a cold-recycled base, and 457 mm (1

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Preliminary Analysis of Quality-Related Specification Approach 311

created for testing, with the desired outputs being effective dynamic modulus (E*) at the JMF and as-built conditions. It is important to note that the featured construction project included two paved HMA layers. For this study, the properties of one pavement lift were changed by one variable at a time. The following JMF ranges or values were used for analysis in each lift, based on specifications obtained from RIDOT and potentially observed QA deviations [8]. This analysis matrix amounted to 16 tests per HMA lift that was varied, specifically:

• AC%: Default, Default ±0.3%, Default ±0.6%, Default ±1.0%; • D/A: Default, 0.60, 0.80, 1.00, 1.20; • AV: Default, 4%, 6%, 8%, 10%, 12%.

After software execution was completed, a statistical analysis procedure was initiated to determine the significance of each parameter for fatigue cracking predictions. Based on the basic assumption that the relationship between each variable, and the delta E* (ΔE*), or as-built E* less JMF E*, for each project, is linear and normally distributed, simple statistical tests were executed. The normality assumption was based off of the WPE, which played a major role in the predictions presented in this study [1]. The assumption of linearity is primarily based off of the principle that incentive/disincentive was calculated by the software in a purely linear equation. Since the output was tied directly to the PLD/pay factor and a weighted average corresponding to tonnage inputs, with no exponential factors being considered, it was assumed that the relationship between E* or ΔE* and JMF parameter is linear. In this study, a linear regression t-test was analyzed to determine the significance of the results at a given confidence level. From this information, a final determination was made regarding the level of sensitivity associated with each input variable.

4.1 Linear Regression t-Test

A linear regression t-test was used to determine whether a parameter was statistically significant with regard to ΔE*. As stated previously, since the underlying assumption is that a linear relationship exists between the dependent and independent variables (alligator cracking ΔE* and JMF parameter, respectively), a t-statistic can be computed to test a hypothesis. A simple flowchart can serve as an insightful guide as to how the following procedure was executed. Figure 2 outlines the statistical methodology applied.

Using a technique similar to the one completed by researchers at West Virginia University [9], the following linear relationship, shown in Eqn. (1), was first assumed to exist, as: 1, 2, … , (1)

In this equation, and are coefficients, where signifies the intercept of the line with the y-axis and dictates the slope. The standard error, , represents the scatter around the linear relationship [9].

312

Fig. 2. Statistical analysis mcracking

D.J. Mensching, L.M. McCarthy, and J.R. Albe

methodology for the evaluation of parametric impacts on fatigu

ert

ue

Preliminary Analysis of Quality-Related Specification Approach 313

Based on the values obtained during the sensitivity analysis, a regression equation will be computed along with a plot of the analysis outputs to test a hypothesis using a t-statistic. In order to achieve a level of significance for the data, a null hypothesis was derived. For this study, the null hypothesis, H0, was that the slope, , of the regression equation is zero, meaning that the variable, , has no statistically significant impact on the results achieved. If the alternative hypothesis, H1, was supported through rejection of the null, this implies that the parameter, , has a statistically significant linear impact on the results, , for a given confidence level, α, which will be set at an initial value of 0.05 (95% confidence). The null and alternative hypotheses for the t-test are shown below:

H0: = 0 H1: 0

The t-statistic was calculated by dividing the least squares estimate by the standard error for , as shown in Eqn. (2): (2)

The calculated t-statistic was compared to the t-critical value from a basic t-table for the given α-value, using a two-tailed approach, and degrees of freedom, 2, which will be the number of samples less two. If the t-statistic exceeded the t-critical value, the null hypothesis could be rejected. Eqn. (3) shows the inequality previously described: | | 2 , 2 (3)

4.2 Normalization Technique

Each statistically significant JMF parameter was normalized so that a comparison could be made to determine the degree of significance of each parameter. In this case, normalization occurs when the input (i.e. AC%) was divided by the mid-range value of the AC% variation, in this example, the original JMF AC% [9]. For D/A, the input values would be normalized when divided by 0.80. In the scenario regarding AV, the mid-range value for normalization would be 8.0%. The normalized input value would then be plotted on the x-axis, with ΔE* included on the y-axis. Since all three variables could potentially be plotted in one location for a particular project, the slope of this line will compare the degree of sensitivity for each JMF parameter [9]. In the case that only one parameter was found to be statistically significant for a particular project, a comparison for level of sensitivity could not be completed and a subjective determination would be made based on the slope of the normalized line. For full-depth reconstruction conditions, two separate analyses would be run, one for each constructed HMA (surface and binder) lift.

314 D.J. Mensching, L.M. McCarthy, and J.R. Albert

5 Discussion of Test Results

In order to obtain replicates for a specific parameter, data for each construction lot were gathered for each of the 31 software runs, resulting in a total of 165 data points. As defined in NCHRP Project 9-22A, five constant tonnage lots were created for software analysis, per lift. This allowed for the test to capture lot variation, as asphalt construction has often been shown to vary from batch-to-batch. The output, alligator cracking E*, was predicted at an effective temperature and effective frequency using a Monte Carlo simulation on the WPE based on historical standard deviations and project-specific values to represent the statistical means required for proper simulation [1]. The as-built E* was then subtracted from the JMF E* to obtain a ΔE* value. Based on preliminary software executions, ΔE* may be a contributing element in the pay factor calculation, as the degree of quality is related largely to the stiffness of the mixture. Statistical procedures were then executed on ΔE* to determine significance as attributed to a particular volumetric factor (AC%, D/A, or AV). At the time of publication, the pay factors cannot be disclosed because the software is still part of an active NCHRP research project.

For the alligator cracking module, the attributes of the HMA binder lift represent most of the prediction results. Therefore, when AC%, D/A, and AV values were varied for the HMA surface lift, there was virtually no change in ΔE*. Any changes in ΔE* were likely due to variations as a result of the Monte Carlo simulations in the software.

However, in the case of varied HMA binder lift characteristics, changes were noted. After the statistical analysis was completed, it was found that AC% is not significant to ΔE*, while D/A was very close to the significance threshold at a 95% confidence level, and AV was found to be statistically significant. Table 1 displays the t-test results for the binder layer in RI-102.

Table 1. Linear regression t-test results for RI-102 binder (19.0 mm) layer

AC% D/A AV 33 23 28

1.444 -2.049 9.763 , 2.035 2.069 2.048

Result NOT

SIGNIFICANT NOT

SIGNIFICANT SIGNIFICANT

Upon determination of statistical significance, the normalization technique was

not required since only one parameter was statistically significant. In order to evaluate the correlation present between the statistically significant variable (AV) and ΔE*, a plot of the average ΔE* at each tested AV level was constructed. Figure 3 shows a strong (R-squared 0.999) linear relationship between ΔE* and AV in that when JMF AV is increased the JMF E* will decrease, leading to an increased ΔE*.

Preliminary Analysis of Quality-Related Specification Approach 315

This preliminary analysis provided some level of indication that sensitivity of changes to certain mix volumetrics or construction parameters can be captured ahead of production with the featured software. However, there are several areas for additional study efforts. A very high R-squared value can present some over emphasis in that five data points are featured, but since only one variable was significant, the normalization technique utilized was not fully functional. There was no comparison variable to assess the degree of significance. These shortcomings would suggest that additional testing is needed.

Fig. 3. Plot for AV-ΔE* relationship in the HMA binder lift

6 Conclusions and Recommendations

Based on the test results, AV is a statistically significant parameter impacting traditional fatigue cracking predictions in the quality-related specification software. These preliminary results show a strong level of statistical significance with regard to this parameter, providing users with knowledge towards expected pay factor values. The results also show a linear relationship between ΔE* and JMF parameter. Owner agencies can use this information to plan accordingly for specification development, with a focus on AV, due to its influence on stiffness and service life predictions for traditional fatigue cracking applications. However, given the complexity of dynamic modulus predictions, further analysis is warranted to obtain a more comprehensive understanding of the software’s interactions and, ultimately, pay factor assignment. The results of this study are valuable in that they present opportunities for future investigations, as this preliminary analysis shows that particular JMF values relay a direct and notable change in stiffness values, and hence PLD/pay factor. Recommendations for further study include: 1) analysis of multiple HMA paving projects, 2) a factorial analysis performed to vary major input categories, instead of specific volumetric values, 3) nonlinear regression techniques to formulate alternate prediction models

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316 D.J. Mensching, L.M. McCarthy, and J.R. Albert

which can then be used in a more simplistic form by contractors and transportation agencies, and 4) a well-developed study on pay factor variance as a result of changes in as-built characteristics which will benefit the transportation and contracting industries for implementation. Judging by the level of importance associated with stiffness values throughout the program, a procedure seeking to relate stiffness directly to service life with a small number of variables appears to be an optimal choice for future endeavors. By relating stiffness, major input categories (structural design, traffic, etc.), and service life, software users can predict a service life parameter, without embarking on a full mixture design process or E* testing sweep. The future enhancements of the quality-related specification process could present cost benefits, and further the successful implementation of a quality-related specification concept for asphalt pavements.

References

[1] Moulthrop, J., Witczak, M.W., et al.: National Cooperative Highway Research Program. NCHRP Report 704: Beta Testing and Validation of HMA PRS (2011)

[2] Chamberlin, W.P.: National Cooperative Highway Research Program. NCHRP Synthesis of Highway Practice 212: Performance-Related Specifications for Highway Construction and Rehabilitation (1995)

[3] Jeong, M.G.: Implementation of a Simple Performance Test Procedure in a Hot Mix Asphalt Quality Assurance Program. Ph.D. Dissertation, Arizona State University (2010)

[4] Epps, J.A., Hand, A., Seeds, S., et al.: National Cooperative Highway Research Program. NCHRP Report 455: Recommended Performance-Related Specification for Hot Mix Asphalt Construction: Results of the Westrack Project (2002)

[5] Hand, A.J., Martin, A.E., Sebaaly, P.E., Weitzel, D.: Evaluating Field Performance: Case Study Including Hot Mix Asphalt Performance-Related Specifications. American Society of Civil Engineers Journal of Transportation Engineering 130(2), 251–260 (2004)

[6] Buttlar, W.G., Harrell, M.: Development of End-Result and Performance-Related Specifications for Asphalt Pavement Construction in Illinois, Crossroads 2000 Proceedings, pp. 195–202. Iowa State University and Iowa Department of Transportation, Ames (1998)

[7] Witczak, M.W.: Development of Performance-Related Specifications for Asphalt Pavements in the State of Arizona, Report FHWA-SPR-08-402-2, Arizona Department of Transportation (2008)

[8] Rhode Island Department of Transportation Materials Section, Full-Depth Reclamation of Rt. 102. Contract-Specific Specification (2010)

[9] Reigle, J.A.: Development of an Integrated Project-Level Pavement Management Model Using Risk Analysis. Ph.D. Dissertation, West Virginia University (2000)