Katherine Petros

Accuracy of Current Complex Modulus Selection Procedure from Vehicular Load Pulse: NCHRP Project 1-37A Mechanistic-Empirical Pavement Design Guide

The Mechanistic-Empirical Pavement Design Guide (MEPDG) uses the complex modulus to simulate the time and temperature dependency of hot-mix asphalt (HMA). To account for the time dependency of HMA, MEPDG recommends calculation of the frequency of the applied load as a function of the vehicle speed and the pavement structure. By this approach, the Odemark method of thickness equivalency is first used to transform the pavement structure into a single-layer system, and it is then assumed that the stress distribution occurs at a constant slope of 45° in the equivalent pavement structure. Concerns were raised that the current MEPDG methodology may be overestimating the frequency, which would result in underconservative distress predictions. Therefore, to evaluate the MEPDG methodology for calculation of the loading time, the results of the MEPDG procedure were compared with those of an advanced three-dimensional (3-D) finite element (FE) approach that simulates the approaching-leaving rolling wheel at a specific speed. The model developed accurately simulated actual tire rib sizes and the applicable contact pressure for each rib. In addition, laboratory-measured viscoelastic properties were incorporated into the FE model to describe the constitutive behavior of HMA. Comparison of these two methods shows that the frequencies calculated on the basis of the MEPDG procedure are greater than the ones determined by the 3-D FE method, which indicates that the loading time determined from MEPDG is not conservative. Ultimately, this would result in underestimation of the pavement response to a load and, therefore, greater errors in calibrations of the pavement response to field distress. Correction factors are thus presented to ensure the correctness of the loading time calculation in MEPDG. Adoption of the proposed factors within the MEPDG software does necessitate a recalibration of the performance models.

Jointed Plain Concrete Pavement Model Evaluation

The mechanistic-empirical pavement design guide developed under NCHRP Project 1-37A employs mechanistic models to calculate pavement primary responses for predicting pavement performance. Therefore, it is imperative that the primary response models be evaluated in relation to their prediction accuracy. In this study, both theoretical accuracy verification and comparison with field measurement were employed to judge the prediction quality of three primary response models commonly used in concrete pavement analysis, namely, ISLAB2000, JSLAB2004, and EverFE2.22. The theoretical evaluation involved simulations, sensitivity trials, and comparisons of calculated slab stress, strain, and deflection responses of a number of hypothetical pavement systems. The field evaluation involved direct comparison of model-simulated responses against those measured from the SHRP Ohio test section. It was found that the agreement between analytical solutions and those obtained by the three analysis programs was affected by the...

Incorporating Traffic Speed Deflection Devices in Structural Evaluation of Flexible Pavement: Methodologies for Pavement Management Application

Pavement management systems of highway agencies in the various states are primarily based on surface condition data. Surface cracking is used as the main indicator of pavement structural condition. However, with effective pavement treatment that intervenes early to preserve and extend the life of pavements and increasingly thicker long-life pavements, surface cracks no longer tell the true structural condition, or health, of the pavement structure. In addition, surface cracks lack an indicator of pavement deterioration. Knowledge of the true pavement structural condition and the rate of deterioration is needed not only for planning of optimal structural rehabilitation activities and future budget needs but also for implementing a performance-based federal-aid program. This paper presents a methodology for interpreting measurements from traffic speed deflection devices (TSDDs) to track flexible pavement structural condition over time and for assessing rehabilitation needs at a network level to address both structural adequacy and surface condition. The paper also demonstrates a methodology for interpreting TSDD measurements to estimate remaining pavement structural capacity. Curvature indexes measured from TSDD were found to be reasonable estimators of pavement structural condition and were used in the demonstration. Horizontal tensile strain, a primary initiator of fatigue damage and cracking, can be estimated from periodic TSDD measurements and used as a leading indicator of pavement deterioration and structural performance. Any differences in pavement structural performance arising from the pavements as-designed versus as-constructed state and its assumed versus actual traffic and climate effects can be assessed and future treatments modified as necessary.

Use of artificial neural networks to detect aggregates in poor-quality X-ray CT images of asphalt concrete

Different laboratory compactors and protocols are employed to simulate field compaction using a reduced representative asphalt mixture specimen. Studies show that the mixture density, air voids, and mechanical properties vary within the results of each compaction method and between different compaction protocols, which may yield to estimates that mislead the design and performance prediction of the asphalt pavement. X-ray Computed Tomography (X-ray CT), a non-destructive technique for generating three-dimensional (3D) imaging of the internal structure of opaque materials, has commonly been used to quantify the air void distribution of asphalt mixtures. However, aggregate location, orientation and aggregate-to-aggregate contact points have rarely been successfully quantified using the same technique mainly because of image noise and poor contrast between the coarser aggregates and the other phases of the asphalt mixture (i.e. the air voids and the blend of the finer fraction aggregates and the asphalt binder). To overcome these shortcomings, an advanced tool has been developed utilizing 3D X-Ray CT image processing and artificial neural networks (ANN) to perform image segmentation and identify the coarser aggregates even in poor contrast X-ray CT images. This paper presents the details of the ANN tool and its application in determining the approximate size and location of the coarse aggregates in asphalt specimens.