Abstract
This paper presents a simple method to determine dynamic modulus master curve of asphalt layers by conducting Falling Weight Deflectometer (FWD) for use in mechanistic-empirical rehabilitation. Ten new and rehabilitated in-service asphalt pavements with different physical characteristics were selected in Khuzestan and Kerman provinces in south of Iran. FWD testing was conducted on these pavements and core samples were taken. Witczak prediction model was used to predict dynamic modulus master curves from mix volumetric properties as well as the bitumen viscosity characteristics. Adjustments were made using FWD results and the in-situ dynamic modulus master curves were obtained. In order to evaluate the efficiency of the proposed method, the results were compared with those obtained by using the developed procedure of the state-of-the-practice, Mechanistic-Empirical Pavement Design Guide (MEPDG). Results showed the proposed method has several advantages over MEPDG including: (1) simplicity in directly constructing in-situ dynamic modulus master curve; (2) developing in-situ master curve in the same trend with the main predicted one; (3) covering the large differences between in-situ and predicted master curve in high frequencies; and (4) the value obtained for the in-situ dynamic modulus is the same as the value measured by the FWD for a corresponding frequency.
Highlights
Structural evaluation of pavements has a major role in any Pavement Management System (PMS) due to the high costs of pavement rehabilitation activities
Dynamic modulus master curve used in the Mechanistic-Empirical Pavement Design Guide (MEPDG) is constructed using dynamic moduli measured in laboratory uniaxial testing on compacted mix samples according to the standard protocols
It was found that this study demonstrates the potential of Artificial Neural Network (ANN) to predict the E(t) and |E*| master curves from single-drop Falling Weight Deflectometer (FWD) deflection time history, the current prediction accuracies are not sufficient to recommend these models for practical implementation
Summary
Structural evaluation of pavements has a major role in any Pavement Management System (PMS) due to the high costs of pavement rehabilitation activities. There are two main categories of methods for determining the dynamic modulus master curves of in-service asphalt layers: 1) Methods using dynamic backcalculation of deflection time history data; and 2) Methods using practical laboratory testing and prediction models. Kutay et al (2011) developed a methodology to backcalculate the |E*| master curve of the asphalt layers using the time history of FWD deflections In this method, a layered viscoelastic forward algorithm in an iterative backcalculation procedure is used in order to determine linear viscoelastic characteristics of asphalt pavements. A new inverse analysis is proposed by Varma et al (2013a) to backcalculate both linear elastic and viscoelastic properties of pavement layers as well as the asphalt mix time-temperature shift factor. This study suggested conducting FWD testing in a set of temperatures to estimate shift factor of asphalt materials (Varma et al 2013b)
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