Application of Artificial Neural Networks for Analysis of Flexible Pavements under Static Loading of Standard Axle

In this study, an artificial neural network was developed in order to analyze flexible pavement structure and determine its critical responses under the influence of standard axle loading. In doing so, more than 10000 four-layered flexible pavement sections composed of asphalt concrete layer, base layer, subbase layer, and subgrade soil were analyzed under the impact of standard axle loading. Pavement sections were analyzed by means of multilayered elastic analysis theory and critical responses of pavement including maximum horizontal principal tensile strain at the bottom of asphalt layer and maximum vertical compressive strain on the top of subgrade were computed in each case. Then, a Feed-Forward back propagation neural network was served to predict these responses. The results show that the artificial neural network can be used as a powerful and accurate tool to predict the critical response of flexible pavements. Application of artificial neural networks for pavement analysis reduces the analysis time and can be used as a quick tool for predicting fatigue and rutting lives of different pavement sections and so in optimum design of pavement structure.