Efficient and accurate simulation of the wheel-rail (WR) contact state is vital for assessing the running safety of high-speed trains on bridges during earthquakes. This study proposes a hybrid contact searching method to enhance the accuracy and robustness of contact point detection under extreme conditions. Based on this method, a comprehensive data pool of WR contact states is established. A WR contact surrogate model is then developed using a backpropagation (BP) neural network, enabling rapid prediction of the creep coefficient. Finally, the surrogate model is embedded into a coupled dynamic system of the high-speed train-bridge, and seismic running safety assessments are conducted under multiple operating conditions. The results show that the proposed hybrid contact searching method is both accurate and reliable. The surrogate model achieves a prediction error within 8%, significantly improving computational efficiency. These findings highlight the promising engineering applicability of the surrogate-model-based running safety assessment approach for high-speed trains running on bridges during earthquakes.
Liu et al. (Thu,) studied this question.