Prediction of Indirect Tensile Strength of Intermediate Layer of Asphalt Pavements Using Artificial Neural Network Model

Abstract

The repair method for pavements should be selected considering the structural capacity of sublayers, in addition to the conditions observed at the pavement surface, to reduce the recurrence of distress in the repaired area. However, it is practically impossible to include the structural capacity of sublayers in the database of the pavement management system (PMS) because this would require additional tests in all expressway sections. Therefore, an artificial neural network model for predicting the indirect tensile strength (ITS) of the intermediate layer of all asphalt pavement sections in an expressway was developed in this study, taking the international roughness index, rut depth, surface distress, and equivalent single axle load as independent variables. The ITS of specimens cored from target sections was measured in the laboratory, and the PMS data for the target sections were collected. The ITS was predicted by conducting a feedforward process prior to the training step. When the error between the predicted and measured ITSs exceeded the allowable error, the model was repetitively trained using the resilient backpropagation method until the error fell within the acceptable boundary. The model was validated by analyzing the correlations between the ITSs predicted from the data of the training and test sets. Finally, the model was complemented by the corresponding minimum and maximum values of the ITS measured at the target section.