Prediction of Fatigue Life of Rubberized Asphalt Concrete Mixtures Containing Reclaimed Asphalt Pavement Using Artificial Neural Networks

Abstract

Accurate prediction of the fatigue life of asphalt mixtures is a difficult task due to the complex nature of materials behavior under various loading and environmental conditions. This study explores the utilization of an artificial neural network (ANN) in predicting the fatigue life of rubberized asphalt concrete mixtures containing reclaimed asphalt pavement (RAP). Over 190 fatigue beams were made with two different rubber types (ambient and cryogenic), two different RAP sources, four rubber contents (0, 5, 10, and 15%), and tested at two different testing temperatures of 5 and 20°C. The data were organized into nine or 10 independent variables covering the material engineering properties of the fatigue beams and one dependent variable, the ultimate fatigue life of the modified mixtures. The traditional statistical method was also used to predict the fatigue life of these mixtures. The results of this study showed that the ANN techniques are more effective in predicting the fatigue life of the modified mixtures tested in this study than the traditional statistical-based prediction models.