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

Lingxu Wu, Wangbao Zhou, Tianxuan Zhong, Lizhong Jiang, Tianxing Wen, Lijun Xiong and Jiang Yi

Reliability Engineering and System Safety, 2025, vol. 260, issue C

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.

Keywords: High-speed railway simply-supported bridge system; Seismic response prediction neural network surrogate model; Bootstrap resampling; Interval prediction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:260:y:2025:i:c:s0951832025002078

DOI: 10.1016/j.ress.2025.111006

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