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

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.

KEYWORDS:

• Rutting prediction
• RIOHTrack full-scale track
• semi-rigid base asphalt pavement
• genetic algorithm
• mechanistic-empirical method
• artificial neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 51878164; 51922030]; Southeast University ‘Zhongying Young Scholars’ Project; China Road and Bridge Engineering Co., Ltd. [grant number CRBC/KHM/2021/053]; National Key Research and Development Program of China [grant number 2020YFA0714302]; Postgraduate Research & Practice Innovation Program of Jiangsu Province [grant number KYCX21_0136]; Fundamental Research Funds for the Central Universities [grant number 2242021R10042].