# Braking Performance and Response Analysis of Trains on Bridges Under Seismic Excitation
## 1 Introduction
Their research offered detailed quantitative insights into longitudinal force induction, wheel-rail separation, and adhesion degradation under seismic excitation, validating the necessity of coupled modeling under uncertainty. Furthermore, [PERSON] et al. [11] incorporated the velocity pulse characteristics of near-fault ground motions into a probabilistic failure analysis framework, revealing the combined uncertainty contribution from bridge configuration and running speed. In a high-dimensional input context, [PERSON] et al. [22] developed a long short-term memory (LSTM) neural network to predict seismic responses of trains, ensuring both nonlinear mapping accuracy and computational efficiency. Complementarily, [PERSON] et al. [23] introduced a deep learning surrogate model integrated with structural uncertainty for seismic safety analysis, significantly improving the quantitative assessment of operational risks. Notably, [PERSON] et al. [24] applied integrated train-track-bridge modeling in the case study of the Sesia viaduct and verified sensitivity under various structural configurations to seismic impact. These studies not only enriched the theoretical framework of coupled seismic response but also offered actionable data and models for post-earthquake operational strategies [25; 26; 27]. With the rapid advancement of deep learning and machine learning technologies, increasing research has applied artificial intelligence to optimize seismic response prediction and braking decision-making [28; 29]. By training convolutional neural networks (CNN) or LSTM-based models, researchers can efficiently evaluate the safety implications of various ground motions and train operating conditions. For instance, [PERSON] et al. [22] developed a deep long short-term memory network to predict nonlinear seismic responses of structures, demonstrating high accuracy in capturing dynamic features. Similarly, [PERSON] et al. [23] proposed a deep learning-based surrogate model for probabilistic safety assessment of train-bridge systems, improving the reliability of response predictions under complex seismic scenarios. These approaches provide more accurate and efficient tools to enhance the safety and resilience of train operations in future seismic environments [30; 31; 32]. Coupled dynamic models for train-track-bridge interaction under seismic excitation have also been extensively studied in recent years, providing the numerical foundations on which the present work builds [33; 34; 35]. In this study, a relevant model of the subway-track-bridge system is developed to investigate the dynamic response of trains under emergency braking during earthquakes. The nonlinear Hertzian contact is used to simulate wheel-rail interaction, and both ground motion and track irregularities are incorporated to reflect extreme conditions that may occur in actual operations. Simulation results show that, under seismic conditions, implementing deceleration measures in a timely manner significantly reduces the dynamic responses of both the train and the bridge compared to maintaining a constant speed, thereby enhancing the operational stability and passenger safety. These findings offer practical guidance for design and emergency response strategies in subway systems, highlighting the necessity of establishing a more robust safety assessment framework in earthquake-prone regions. The results of this study can contribute to the revision of seismic design codes for urban rail transit systems, and provide a theoretical and practical foundation for the reconfiguration of braking systems under extreme environments, thereby ensuring operational stability and safety during natural disasters.