Development of An Artificial Neural Network Model for Asphalt Pavement Deterioration Using LTPP Data

Abstract

Deterioration models are important and essential part of any Pavement Management System (PMS). These models are used to predict future pavement situation based on existence condition, parameters causing deterioration and implications of various maintenance and rehabilitation policies on pavement. The majority of these models are based on roughness which is one of the most important indices in pavement evaluation. High correlation between International Roughness Index (IRI) and user comfort led to modeling pavement deterioration based on IRI during PMS history. On the other hand, in recent years Artificial Neural Network (ANN) which is a valuable tool of soft computing is used in pavement modeling, widely. This study assessed the development of an ANN pavement deterioration model based on IRI using Back-Propagation Neural Networks (BPNN) technique. The Long-Term Pavement Performance (LTPP) data was extracted from two General Pavement Study (GPS) sections including GPS-1 and GPS-2. After training and testing the developed model, results were compared with a polynomial regression model. Results showed that predicted IRI values with developed ANN model have a good correlation with measured values rather than the polynomial regression model for both GPS-1 and GPS-2 sections.