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Uncertainty quantification in predicting seismic response of high-speed railway simply-supported bridge system based on bootstrap

Author

Listed:
  • Wu, Lingxu
  • Zhou, Wangbao
  • Zhong, Tianxuan
  • Jiang, Lizhong
  • Wen, Tianxing
  • Xiong, Lijun
  • Yi, Jiang

Abstract

Reliable and rapid prediction of seismic-induced response is crucial for post-earthquake repair or rescue operations. In this paper, a method for quantifying uncertainty in rapid seismic response prediction for high-speed railway simply-supported bridge system (HRSBS) was developed based on a Bi-LSTM neural network surrogate model and Bootstrap resampling to address the challenge of acquiring timely seismic responses for HRSBS and the inability to determine confidence intervals from a single prediction result. Epistemic and aleatory uncertainties were quantified in rapid prediction of seismic-induced responses for HRSBS. The applicability of Bi-LSTM model based on a single seismic time series for predicting seismic-induced responses of HRSBS was identified. The results indicated that the prediction intervals with the 95% confidence level obtained by the proposed method encompass the actual values. The misjudgment rates of component damage states are effectively reduced. The Bi-LSTM model employing a single seismic time series input is suitable for predicting the time-history curves of seismic responses of components but not suitable for predicting seismic-induced residual displacement of rail.

Suggested Citation

  • Wu, Lingxu & Zhou, Wangbao & Zhong, Tianxuan & Jiang, Lizhong & Wen, Tianxing & Xiong, Lijun & Yi, Jiang, 2025. "Uncertainty quantification in predicting seismic response of high-speed railway simply-supported bridge system based on bootstrap," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:reensy:v:260:y:2025:i:c:s0951832025002078
    DOI: 10.1016/j.ress.2025.111006
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    References listed on IDEAS

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    1. Tang, Yumeng & Li, Shuang & Zhai, Changhai & Zhao, Jianjun, 2023. "Railway operation recovery method of regional high-speed railway based on optimal resilience after earthquakes," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    2. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    3. Wu, Yangyang & Chen, Suren, 2023. "Resilience modeling and pre-hazard mitigation planning of transportation network to support post-earthquake emergency medical response," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Basora, Luis & Viens, Arthur & Chao, Manuel Arias & Olive, Xavier, 2025. "A benchmark on uncertainty quantification for deep learning prognostics," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    5. Chongwen Jiang & Biao Wei & Dianbin Wang & Lizhong Jiang & Xuhui He, 2017. "Seismic Vulnerability Evaluation of a Three-Span Continuous Beam Railway Bridge," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-13, October.
    6. Zhu, Jingbao & Sun, Wentao & Li, Shanyou & Yao, Kunpeng & Song, Jindong, 2024. "Threshold-based earthquake early warning for high-speed railways using deep learning," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    7. Allahvirdizadeh, R. & Andersson, A. & Karoumi, R., 2023. "Improved dynamic design method of ballasted high-speed railway bridges using surrogate-assisted reliability-based design optimization of dependent variables," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    8. Xing, Lili & Gardoni, Paolo & Zhou, Ying & Zhang, Peng, 2025. "DNN-metamodeling and fragility estimate of high-rise buildings with outrigger systems subject to seismic loads," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    9. Zhou, Zhou & Yu, Xiaohui & Gardoni, Paolo & Ji, Kun & Lu, Dagang, 2025. "Seismic risk estimates for reinforced concrete structures with incorporation of corrosion and aftershock," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
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