Brian K Diefenderfer

Analysis of Virginia-Specific Traffic Data for Use with Mechanistic-Empirical Pavement Design Guide

This study developed traffic inputs for use with the Guide for the Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures (MEPDG) in Virginia and sought to determine whether the predicted distresses showed differences between site-specific and default traffic inputs for flexible pavements. The predicted distresses based on site-specific inputs from eight Interstate weigh-in-motion sites in Virginia were compared with predicted distresses by means of MEPDG default traffic inputs. These comparisons were performed with the use of a normalized-difference statistic for each site-specific traffic input and the coefficient of variation for each pavement distress model. In addition, the practical significance was considered from the difference in the predicted time to failure between site-specific and default traffic inputs. The analysis showed that the effect of the site-specific traffic inputs was generally not statistically significant when the uncertainty of the distress models was considered. However, the site-specific axle load spectra inputs showed a practically significant increase in the predicted time to failure compared with the default traffic inputs. From this study, it is recommended that users of the MEPDG consider collecting site-specific axle load spectra data for analysis of flexible pavements. In addition, analysis of flexible pavements may be performed by using the default inputs for the remaining traffic parameters. Given the mixed conclusions in previous studies, it may be necessary for each user or agency to conduct a similar evaluation for its local combination of traffic, materials, climate, and typical pavement structures.

Assessment of Continuous Pavement Deflection Measuring Technologies

This report documents the results of a study that evaluated current technologies implemented in continuous deflection measuring devices. The research team assessed the demand and the potential value of continuous deflection devices for supporting pavement renewal decisions, and identified the technologies best suited for effectively supporting the most critical decisions identified by the potential users through a survey of state departments of transportation. The study produced a catalogue of existing continuous deflection measuring technologies and their characteristics. The capabilities of two devices, that are or showed the most promise to be ready for production use, were reviewed and practical applications for supporting pavement management decision-making identified and illustrated. The Rolling Wheel Deflectometer (RWD) and Traffic Speed Deflectometer (TSD) were selected for further study as these were identified as being in or close to production mode and met the criteria of measurement speed, applied loads and data collection frequency. However, detailed evaluation was only possible for the TSD because the RWD was not available for testing. The research confirmed that, in general, the technology can provide adequate repeatability for network-level data collection and can be used to support critical network-level pavement management business processes. However, the study also showed that the technology is only just maturing and identified possible improvement to make it even more useful and practical.

Rutting Resistance of Asphalt Concrete Mixtures That Contain Recycled Asphalt Pavement

This study evaluated the rutting resistance of plant-produced asphalt concrete (AC) mixtures in the laboratory. Nineteen plant-produced AC mixtures were used; these mixtures contained reclaimed asphalt pavement (RAP) amounts that ranged from 0% to 25%. Tests on the mixtures included the dynamic modulus (|E*|) test at multiple temperatures and the flow number (FN) test at 54°C to characterize stiffness and rutting resistance, respectively. Mixtures that contained no RAP showed |E*| values comparable to those that contained 25% RAP in most cases. For most of the 19 mixtures tested, mixtures with lower FNs either contained no RAP, contained 25% RAP, or had PG 64-22 as the design binder grade. Mixtures that contained moderate amounts of RAP (10% and 15%), regardless of design binder grade, had higher FNs than mixtures with either high or low RAP amounts. Statistical analysis showed that the RAP amount was the most significant factor to affect rutting resistance in the mixtures studied. A linear inverse relationship between RAP and FN appeared to describe the data well. As the RAP amount increased, a downward trend occurred in both effective binder content (Pbe) and rutting parameter (G*/sin δ). The effect of RAP on FN was unexpected, because it showed the rutting resistance to decrease with increased RAP. Possible reasons might have been the use of softer asphalt binder in mixtures with higher RAP and the observed decrease in both Pbe and G*/sin δ with increased RAP amounts. More rutting-related mechanistic studies are needed of AC mixtures that contain RAP.

Asphalt Material Characterization in Support of Mechanistic-Empirical Pavement Design Guide Implementation in Virginia

The procedure proposed in the Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures (referred to as MEPDG) heavily depends on the characterization of the fundamental engineering properties of paving materials. This paper presents the results of a project aimed at the characterization of hot-mix asphalt (HMA) in accordance with the procedure established by MEPDG to support its implementation in Virginia. The project examined the dynamic modulus, the main HMA material property required by MEPDG, as well as creep compliance and tensile strength, which are needed to predict thermal cracking. Loose samples of 11 mixes (four base, four intermediate, and three surface mixes) produced with PG 64-22 binder were collected from different plants across Virginia. Representative samples underwent testing for maximum theoretical specific gravity, asphalt content by the ignition oven method, and gradation of the reclaimed aggregate. Specimens for the various tests were then prepared by use of the Superpave® gyratory compactor. The test results showed that the dynamic modulus is sensitive to the mix constituent properties (aggregate type, asphalt content, percentage of recycled asphalt pavement, etc.) and that even mixes of the same type (SM-9.5A, IM-19.0A, and BM-25.0) had different measured dynamic modulus values. The Level 2 dynamic modulus prediction equation reasonably estimated the dynamic modulus measured; however, it did not capture some of the differences between the mixes found in the measured data.

In-Place Pavement Recycling on I-81 in Virginia

During the 2011 construction season, the Virginia Department of Transportation completed an in-place pavement recycling project to rehabilitate a 3.66-mi section of pavement on southbound I-81 in Augusta County near Staunton. In 2008, the directional traffic volume was 23,000 vehicles per day with 28% being trucks (85% of the truck traffic consisted of five- and six-axle tractor trailer combination vehicles). This section of the Interstate showed structurally related deterioration at the pavement surface, had a low structural capacity, and had a history of frequently recurring maintenance. The construction project used three in-place pavement recycling techniques (full-depth reclamation, cold central-plant recycling, and cold in-place recycling) and a unique lane-closure schedule to accomplish the work. A construction contract with a value of

Comparison of Nuclear and Nonnuclear Pavement Density Testing Devices

7.64 million and a time frame of approximately 8 months was awarded in December 2010. The in-place recycling portion of the work was completed in fewer than 20 workdays spanning 6 weeks. This construction work represents the first time in the United States that those three recycling techniques were combined in one project on the Interstate system, and the project showed that the construction techniques can be used on higher-volume facilities. The construction techniques and the traffic management plan are described, and the results of acceptance testing and initial field testing are discussed. Suggestions for future study based on lessons learned during this project are offered.

Developing a Network-Level Structural Capacity Index for Asphalt Pavements

The density of a hot-mix asphalt (HMA) pavement is an important factor for assessing pavement quality. Sufficient density is an essential characteristic of a well-constructed pavement and will lower its potential for distress. Traditionally, pavement density is determined by laboratory measurements of core samples and in situ readings via nuclear density gauges. Recently, nonnuclear devices that measure the electromagnetic properties of pavements have been developed. Nonnuclear devices have an inherent advantage over nuclear density gauges in that stringent monitoring of the user and extra security precautions are not required. However, the literature is unclear whether the readings from these devices are equivalent to those from nuclear density gauges. In this study, in-place pavement density readings from nonnuclear and nuclear density gauges were compared with laboratory-measured density values from core samples taken from the same location. Density readings from both gauges were compared with density measurements from the core samples for percent difference, correlation analysis, and hypothesis testing. However, the effect of moisture in the HMA was found to have a significant impact on density readings from the nonnuclear gauge. For seven of eight projects when a regression-based correction incorporating the qualitative moisture index was applied to the nonnuclear gauge density readings, the densities were better correlated to core density measurements than were densities from a traditionally used nuclear density gauge. On the basis of this study, the nonnuclear density gauge is more suitable than the nuclear density gauge for measuring pavement density on dense-graded HMA, provided that the readings are corrected using the qualitative moisture index.

Network-Level Falling Weight Deflectometer Testing: Statistical Determination of Minimum Testing Intervals and Number of Drop Levels on Virginia’s Interstate System

This paper presents a network-level structural capacity indicator for asphalt pavements in the state of Virginia. A literature review revealed that several network-level structural capacity indexes have been proposed, and a number of states use structural capacity measures in their network-level decision processes. Some decision methods and structural indexes are compared in this paper using network-level deflection data collected using the falling weight deflectometer and distress data from tests conducted on Virginia interstates. One index that is based on the structural number concept, the Structural Capacity Index, is found to produce network-level decisions that most closely match project-level work done by the Virginia Department of Transportation during the 2008 construction season. The index was adopted, and its sensitivity to various input parameters was determined. Furthermore, the impact of the structural capacity of the pavement on the service life of a pavement maintenance treatment was clearly established in this paper. Equations to define the service life of a corrective maintenance treatment as a function of the structural condition of the pavement are also presented in this paper.

Evaluation of Traffic-Speed Deflectometers

A project was initiated to collect network-level falling weight deflectometer (FWD) data along Virginias Interstate system. The FWD data collection aims to build a comprehensive database of deflection data and associated structural analysis to be used for future implementation of the Mechanistic–Empirical Pavement Design Guide. Virginias current network-level evaluation protocol requires that pavements be tested every tenth of a mile at four drop levels, with three deflection basins (repetitions) collected at each drop level. To test the more than 2,000 lane miles of Interstate system, it was necessary to determine if the number of test points per mile and the number of load levels used could be reduced to increase the testing production rate and reduce the high cost of testing and traffic control. FWD testing was initiated on the entire length of I-77 and on portions of I-64 and I-81 by using the current testing protocol with only two deflection basins collected at each drop level. A comprehensive statistical analysis was performed on subgrade moduli, effective structural number, and layer moduli calculated by using the AASHTO and the ELMOD methods, respectively. The results of this study document that network-level testing at 10 points per mile can be reduced to three points per mile, and the four FWD drop levels can be reduced to only one drop level, with two deflection basins collected at this load level, without statistically compromising the quality of the data collected. However, the recommendation was made to collect data at multiple load levels for future in situ characterization of material stress sensitivity.

Development of dynamic modulus master curves for hot-mix asphalt with abbreviated testing temperatures

Continuous deflection-measuring devices, or continuous deflectometers, are increasingly being used to support project-level and network-level pavement management decisions. Continuous deflectometers are nondestructive pavement evaluation devices that measure pavement deflections caused by a moving load. Some continuous deflectometers can measure with little to no traffic control; this feature makes them more advantageous to use than stationary devices, such as the falling weight deflectometer. The current technologies implemented in different types of deflectometers are discussed, and the most promising devices for supporting network-level pavement management decisions are identified. In that respect, two devices (the rolling wheel deflectometer and the traffic speed deflectometer) have shown promising results and are being evaluated further under Project R06 (F) of SHRP 2.