Probabilistic safety analysis of coupled train-bridge system using deep learning based surrogate model. Issue 8 (3rd August 2023)

• Record Type: Journal Article
• Authors: Li, Huile; Wang, Tianyu; Wu, Gang
• Is Part Of: Structure and infrastructure engineering. Volume 19:Issue 8(2023)
• Page Start: 1138
• Page End: 1157
• Publication Date: 2023-08-03
• Subjects: Reliability analysis -- train-bridge dynamic interaction -- deep learning -- automatic differentiation -- convolutional neural network -- Monte Carlo simulation; Structural analysis (Engineering) -- Periodicals; Structural engineering -- Periodicals; Buildings -- Performance -- Periodicals; 620.005
• DOI: 10.1080/15732479.2021.2010104
• Languages: English
• ISSNs: 1573-2479

Abstract

Abstract: Due to unavoidable uncertainties, it is critical to perform probabilistic analysis when examining the safety of coupled train-bridge system based on the dynamic interaction analysis. This paper proposes a novel approach for probabilistic safety analysis of coupled train-bridge system using deep learning based surrogate model. Deep neural network embedded with convolutional neural network is designed and developed to construct the surrogate model substituting the 3D train-bridge interaction system model for reducing the computational efforts and efficiently predicting a series of dynamic indices of the coupled system. Due to the lack of explicit expression of the performance function, automatic differentiation is exploited to derive the surrogate model and further to realise the reliability and sensitivity analysis with the first-order method. The obtained reliability and sensitivity indices are compared to that resulting from the Monte Carlo simulation method with importance sampling and tail modelling. The proposed approach is applied on a high-speed train and bridge system, treating multiple main system parameters as random variables. Effects of the railway operational speed on the system reliability are investigated. The results show that the proposed approach can provide an efficient solution to the probabilistic safety analysis of coupled train-bridge system especially with small failure probability.