Development of Prediction Model for Rutting Depth Using Artificial Neural Network

One of the most common pavement distresses in flexible is rutting, which mainly caused by heavy wheel load and various other factors. The prediction rutting depth important for safe travel long-term performance pavements. Factors that are considered this paper rut Temperature, Equivalent Single Axle Load, Resilient modulus, Thickness hot mixed asphalt. input data all factors collected from Long-Term Pavement Performance Information Management System state Texas. Regression analysis performed dependent independent variables to obtain empirical relationship. In fields civil engineering, artificial neural networks have recently been utilized model qualities behavior materials determine complicated relationship between properties. An Artificial Neural Network used develop a predictive predict depth. A total number 70 observations were model. mathematical relation developed verified variable data.