A rapid analysis framework for seismic response prediction and running safety assessment of train-bridge coupled systems

Abstract

With high-speed railway lines increasing gradually, the running safety assessment (RSA) of train-bridge coupled (TBC) systems has become an indispensable part of railway seismic design. However, the modeling of TBC systems is difficult and computationally complex. Therefore, it is difficult to conduct real-time assessment and response analysis rapidly. With this in mind, this paper integrates a surrogate framework based on deep learning methods and can help rapid-diagnose the seismic response and RSA. Meanwhile, real-time information feedback is performed to determine the safe speed of the train. To this end, a convolutional neural network-long short-term memory (CNN-LSTM) model with excellent time-series data processing capability is newly used for the seismic response analysis of the TBC system. Furthermore, response data obtained are processed and input to an interpretable adaptive network-based fuzzy inference system (ANFIS). The ANFIS derives a series of fuzzy rules while conducting the RSA, and these rules can help non-specialists understand the assessment process. Finally, parameter inversion is performed by the ANFIS to determine a safe threshold of train speed. According to the tests, the proposed rapid analysis framework can be applied to seismic response analysis and RSA with high efficiency and accuracy in multiple engineering scenarios. The framework may be helpful for railway design and make it possible to realize real-time data analysis for TBC systems.