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High-speed railway seismic response prediction using CNN-LSTM hybrid neural network

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Abstract

In addressing the challenges of analyzing seismic response data for high-speed railroads, this research introduces a hybrid prediction model combining convolutional neural networks (CNN) and long short-term memory networks (LSTM). The model's novelty lies in its ability to significantly improve the precision of fiber grating monitoring for high-speed railroads. Employing quasi-distributed fiber optic gratings, seven grating monitoring points were strategically placed on each fiber to capture responses of the track plate, rail, base plate, and beam during seismic activities. Using data from peripheral gratings, the model predicts the central point's response. A continuous feature map, formed via a time-sliding window from the rail's acquisition location, undergoes initial feature extraction with CNN. These features are then sequenced for the LSTM network, culminating in prediction. Empirical results validate the model's efficacy, with an RMSE of 0.3753, MAE of 0.2968, and a R2 of 0.9371, underscoring its potential in earthquake response analysis for rail infrastructures.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This research work was jointly supported by the National Natural Science Foundation of China (Grant No. 11972379), and Hunan Science Fund for Distinguished Young Scholars (2021JJ10061).

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Authors and Affiliations

  1. College of Civil Engineering, Xiangtan University, Xiangtan, 411105, Hunan, China

    Xuebing Zhang, Xiaonan Xie, Shenghua Tang, Li Wang & Han Wu

  2. School of Civil Engineering, Central South University, Changsha, 410075, Hunan, China

    Han Zhao & Ping Xiang

  3. School of Transportation, Southeast University, Nanjing, 211189, Jiangsu, China

    Xueji Shi

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  1. Xuebing Zhang
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  2. Xiaonan Xie
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Contributions

XZ: Supervision, Writing—review and editing, Funding acquisition. XX: Conceptualization, Methodology, Software, Validation, Writing-original draft. ST: Supervision. HZ: Validation, Investigation. XS: Conceptualization. LW: Conceptualization, Data curation. HW: Visualization. PX: Supervision, Writing—review·and editing, Funding acquisition.

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Correspondence to Ping Xiang.

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Zhang, X., Xie, X., Tang, S. et al. High-speed railway seismic response prediction using CNN-LSTM hybrid neural network. J Civil Struct Health Monit 14, 1125–1139 (2024). https://doi.org/10.1007/s13349-023-00758-6

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