Rutting prediction model for semi-rigid base asphalt pavement based on a data-mechanistic dual driven method. Issue 1 (6th December 2023)

• Record Type: Journal Article
• Authors: Han, Chengjia; Tong, Jusheng; Ma, Tao; Tong, Zheng; Wang, Siqi
• Is Part Of: International journal of pavement engineering. Volume 24:Issue 1(2023)
• Publication Date: 2023-12-06
• Subjects: Rutting prediction -- RIOHTrack full-scale track -- semi-rigid base asphalt pavement -- genetic algorithm -- mechanistic-empirical method -- artificial neural network; Pavements -- Design and construction -- Periodicals; Highway engineering -- Periodicals; 625.805
• DOI: 10.1080/10298436.2023.2173753
• Languages: English
• ISSNs: 1029-8436

Abstract

ABSTRACT: 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.