###### Abstract  
Monitoring and predicting the dynamic responses of railway bridges under moving trains, including displacement and acceleration, are vital for evaluating the safety and serviceability of the train-bridge system. Traditionally, finite element analysis methods with high computational burden are used to predict the train-induced responses according to the given train loads and, hence, cannot easily be integrated as an available structural-health-monitoring strategy. Therefore, this study develops a novel framework, combining the train-bridge coupling mechanism and deep learning algorithms to efficiently predict the train-induced bridge responses while considering train load duration. Initially, the feasibility of using neural networks to calculate the train-bridge coupling vibration is demonstrated by leveraging the nonlinear relationship between train load and bridge responses. Subsequently, the instantaneous multiple moving axial loads of the moving train are regarded as the equivalent node loads that excite adjacent predefined nodes on the bridge. Afterwards, a deep long short-term memory (LSTM) network is established as a surrogate model to predict the train-induced bridge responses. Finally, the prediction accuracy is validated using a numerical case study of a simply supported railway bridge. The factors that may affect the prediction accuracy, such as network structure, training samples, the number of structural units, and noise level, are discussed. Results show that the developed framework can efficiently predict the train-induced bridge responses. The prediction accuracy of the bridge displacement is higher than that of the acceleration. In addition, the robustness of the displacement prediction is proven to be better than that of the acceleration with the variation of carriage number, riding speed, and measurement noise. a _Applied_  
_sciences_  
element simulation that considers the train-bridge coupling theory heavily relies on numerical integration, incurring high computational costs and making it difficult to meet the requirement of rapidly predicting bridge responses. In another aspect, the SHM system of railway bridges can collect massive structural response data that are available for in-service assessment; however, it is not realistic and affordable for one to be installed on all medium- and small-span railway bridges [3; 4]. Researchers have been focusing their efforts on data-driven structural response prediction methods. Among them, machine learning methods have emerged as a research hotspot [5], gradually becoming applied to the prediction of structural dynamic responses in various contexts such as residential buildings, bridges, and wind turbines [6; 7]. Machine learning methods enable the prediction of dynamic responses under different types of loads, including seismic loads, wind loads, and train loads. On the one hand, experts have extensively employed deep learning methods, e.g., fully connected neural networks [8] and feedforward neural networks [9], to predict the nonlinear dynamic time-history responses of bridges and buildings under seismic actions. The prediction errors are then utilized for post-earthquake structural damage assessment and localization [10]. On the other hand, the machine learning methods have also been employed to predict structural dynamic responses under wind excitations. For example, the wind pressure time histories on high-rise buildings were predicted using the backpropagation neural network [11], the aerodynamical responses of long-span bridges were predicted based on the multi-layer feedforward networks [12; 13]. Additionally, advancements in predicting the dynamic responses of bridges induced by train loads have been progressing steadily. For example, the GA-TCN-LSTM model proposed by [PERSON] et al. [14] shows high accuracy in predicting dynamic response at different train speeds. [PERSON] et al.