Rutting prediction model for semi-rigid base asphalt pavement based on a data-mechanistic dual driven method

• Published in: The international journal of pavement engineering Vol. 24; no. 1
• Main Authors: Han, Chengjia, Tong, Jusheng, Ma, Tao, Tong, Zheng, Wang, Siqi
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 06.12.2023; Taylor & Francis LLC
• Subjects: Accuracy; artificial neural network; Artificial neural networks; Asphalt mixes; Asphalt pavements; Boundary conditions; Field tests; genetic algorithm; Genetic algorithms; mechanistic-empirical method; Neural networks; Prediction models; Reliability; Rigid pavements; RIOHTrack full-scale track; Rutting prediction; semi-rigid base asphalt pavement
• ISSN: 1029-8436; 1477-268X
• DOI: 10.1080/10298436.2023.2173753

Rutting development in semi-rigid asphalt pavements can be predicted using models such as mechanistic-empirical (M-E) and data-driven methods. However, the prediction accuracies of the M-E methods in the laboratory may not be generalised due to the boundary condition differences, while inappropriate calibration in the data-driven methods may reduce the reliability owing to the lack of theoretical support. This study has proposed a combined framework of an M-E method and an artificial neural network (ANN) for rutting development prediction. This framework, namely the mechanistic-empirical and artificial neural network method, first calibrated the existing M-E model by adding a term of the time-varying hardening characteristics of asphalt mixture and optimised the parameters in the term using a genetic algorithm. The new M-E model was used to predict the possible range of rutting depth. An ANN then predicted the rutting depth that was normalised by the predicted range of the new M-E model. Thus, the proposed approach combined the reliability of the M-E method and the prediction accuracy of the ANN method. Field test results showed that the proposed framework with a 97.5% accuracy outperformed the ANN-based method.