Developing Non- Linear Relationship Among Factors Affecting the Rutting Susceptibility of Asphalt Mixtures Using Two Parameter Weibull Distribution

Abstract

This paper illustrates a novel approach to determine the probabilistic rutting in asphalt mixtures based on Weibull distribution statistical approach. In this paper, thirty wearing course asphalt mixtures prepared using different binder grades and aggregate sources, are analysed. Cooper Wheel tracker Test (CWTT)and Asphalt pavement analyser (APA) have been used to ascertain the rutting potential of asphalt mixtures with varying binder grade and aggregate source. Mixtures are then ranked based on their rutting susceptibility. The obtained data is processed using two parametric Weibull approach. Scale and shape parameters are figured out by multiple regression technique. Test results are analysed using goodness of fit test. Results indicate that rut depth predicted from two parameter Weibull distribution technique corelates well with the actual rut depth with R2 value of 0.88 for both cooper wheel tracker and asphalt pavement analyser test. It has been found that Weibull distribution bests fit the NHA-A, NHA-B and SP-B graded mixtures with NRL 60/70 at 50°C. A good correlation has been observed between Cooper Wheel tracker and Asphalt pavement analyser as translated by R2 of 0.86. This research is important in a sense that several agencies have adopted rutting as a failure criteria for flexible pavements. By mathematically modelling the rutting response, predicting the rutting behaviour of mastics becomes simpler and more efficient in approach.

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“…Thanks to its versatility and relative simplicity, the Weibull distribution is also widely used in material engineering for the analysis of different kinds of materials, including metals and their alloys [8,9], ceramics [20], plastic materials [21,22], and asphalt mixtures [23]. Nowadays, research in the area of composite materials is very extensive, in connection with statistical predictions and probability models based on the Weibull distribution [24][25][26].…”