Local calibration of creep compliance models of asphalt concrete

This paper presents the results of two alternative local calibration methods; nonlinear multiple regression and artificial neural network (ANN), and compares their performance to the MEPDG generalized model. In total, 861 data points from 41 specimens representing 14 types of AC mixes were prepared and tested in the laboratory. The creep compliance values were determined in the laboratory and compared to values generated by globally and locally calibrated models. The analysis shows that the MEPDG creep compliance model without local calibration predicts creep values poorly, thus calibration should be a required step in the implementation of MEPDG designs. As expected, both nonlinear regression and ANN model significantly improve the reliability of creep compliance predictions. The ANN model produced the lowest error in creep compliance predictions.