Numerical and statistical investigation of the performance of closed-cell aluminium foam as a seismic isolation layer for tunnel linings

Inter-conversion of the generalized Kelvin and generalized Maxwell model parameters via a continuous spectrum method

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. 

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

Mechanistic-based pavement distress prediction models or transfer functions are often used to assess damage or predict the service life of the pavement structure. The reliability of pavement struct...

Wangxu Dong

Papers

Field evaluation and statistical analysis of the dynamic response of semi-rigid pavement under full-scale moving truck load