Artificial Neural Network Approach to Estimating Stiffness Behavior of Rubberized Asphalt Concrete Containing Reclaimed Asphalt Pavement

Abstract

Accurately predicting the stiffness of asphalt pavements is difficult due to the complex behavior of materials under various loading, pavement structure, and environmental conditions. This study explores the utilization of the artificial neural network (ANN) in predicting the stiffness behavior of rubberized asphalt concrete mixtures with reclaimed asphalt pavement (RAP). A total of 296 asphalt mixture beams were constructed from two different rubber types (ambient and cryogenic), two different RAP sources, and four rubber contents (0, 5, 10, and 15%). All samples were tested at two different testing temperatures of 5 and