Modeling of creep compliance behavior in asphalt mixes using multiple regression and artificial neural networks

This research aims to provide an appropriate approach to enhance asphalt mixtures creep compliance performance predictions and presents two predictive models, one with multiple regression analysis and the other with feed-forward artificial neural networks (ANN). The two models were evaluated in terms of loading time, testing temperature, asphalt modification, air voids level, and aging condition. The results showed that the two proposed models can be used to predict the HMA creep compliance behavior. However, The prediction accuracy of the feed-forward ANN model is much better as compared with the multiple regression model. The developed feed-forward ANN model has the capability to explain more than 99% of the measured data. Such feasible prediction model provides an attractive alternative for making a better primary decision about selecting asphalt mixtures variables in a quite short time with a very low error rate.