TY  - EJOU
AU  - Sun, Linlin 
AU  - Wang, Zihui 
AU  - Cui, Shukun 
AU  - Yan, Ziquan 
AU  - Hu, Weiping 
AU  - Meng, Qingchun 

TI  - A Novel Model for Describing Rail Weld Irregularities and Predicting Wheel-Rail Forces Using a Machine Learning Approach
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 142
IS  - 1
SN  - 1526-1506

AB  - Rail weld irregularities are one of the primary excitation sources for vehicle-track interaction dynamics in modern high-speed railways. They can cause significant wheel-rail dynamic interactions, leading to wheel-rail noise, component damage, and deterioration. Few researchers have employed the vehicle-track interaction dynamic model to study the dynamic interactions between wheel and rail induced by rail weld geometry irregularities. However, the cosine wave model used to simulate rail weld irregularities mainly focuses on the maximum value and neglects the geometric shape. In this study, novel theoretical models were developed for three categories of rail weld irregularities, based on measurements of the high-speed railway from Beijing to Shanghai. The vertical dynamic forces in the time and frequency domains were compared under different running speeds. These forces generated by the rail weld irregularities that were measured and modeled, respectively, were compared to validate the accuracy of the proposed model. Finally, based on the numerical study, the impact force due to rail weld irrregularity is modeled using an Artificial Neural Network (ANN), and the optimum combination of parameters for this model is found. The results showed that the proposed model provided a more accurate wheel/rail dynamic evaluation caused by rail weld irregularities than that established in the literature. The ANN model used in this paper can effectively predict the impact force due to rail weld irrregularity while reducing the computation time.
KW  - Rail weld irregularity; high-speed railway; vehicle-track coupled dynamics; wheel/rail dynamic vertical force; artificial neural networks

DO  - 10.32604/cmes.2024.056023