基于群智算法优化的ME车辙预测模型OA

Rutting,a common disease of asphalt pavement,not only compromises the road quality and safety,but also plays a critical role in the structural design of asphalt pavement in many countries.To achieve more ac-curate prediction and evaluation of rutting evolution trends,it is particularly important to improve and optimize the existing rutting depth prediction model.Therefore,based on the long-term observation data of the RIOHTrack full-scale pavement acceleration loading test loop,the mechanical-empirical rutting depth prediction model in the Highway Asphalt Pavement Design Specifications(JTG D50-2017)was comprehensively adjusted and optimized.Three calibration parameters were introduced to calibrate the constant coefficients,tempera-tures,and cumulative load times,respectively,to improve the prediction accuracy and generalization ability of the model.Subsequently,a multi-strategy adaptive particle swarm optimization(MAPSO)algorithm incorpora-ting a neighborhood mutation strategy and fusing exponential adaptive inertia weights with sinusoidal adaptive learning factors,was proposed.Then this algorithm was used to estimate the values of three calibration param-eters to further improve the accuracy of the model.Finally,with the rutting data of 19 types of asphalt pave-ments in the RIOHTrack as an example,the MAPSO-RME model proposed in this article was applied for rutting depth prediction.The experimental results demonstrate that,compared with the mechanical-empirical rutting depth prediction model in the Highway Asphalt Pavement Design Specifications(JTG D50-2017),the MAPSO-RME model achieves remarkable improvement in fitting performance with a significant reduction in mean squared error(MSE)of prediction.