应用数学和力学2026，Vol.47Issue(5)：639-654,16.DOI:10.21656/1000-0887.460045
基于群智算法优化的ME车辙预测模型
Optimization of the ME Rutting Depth Prediction Model Using Swarm Intelligence Algorithms
摘要
Abstract
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.关键词
沥青路面/车辙/参数校准/多策略自适应粒子群算法Key words
asphalt pavement/rutting/parameter calibration/multi-strategy adaptive particle swarm optimization分类
交通工程引用本文复制引用
刘佳佳,李卓轩,张伟光,曹进德..基于群智算法优化的ME车辙预测模型[J].应用数学和力学,2026,47(5):639-654,16.基金项目
国家重点研发计划(2020YFA0714300) （2020YFA0714300）
南京现代综合交通实验室开放课题(MTF2023004) （MTF2023004）
