Computational fluid dynamics -informed virtual safety assessment of steel-framed structure with fire-induced ductile failure

Zhiyi Shi, Yuan Feng, Temitope Egbelakin, Chengwei Yang and Wei Gao

Reliability Engineering and System Safety, 2025, vol. 258, issue C

Abstract: This paper proposes a Computational Fluid Dynamics-Informed (CI) Virtual Safety Assessment (VSA) framework for predicting the time-dependent ductile failure of steel-framed buildings during fire incidents. By incorporating a CI-based physical model, the spatiotemporally nonlinear temperature field in real fire scenarios can be reproduced and used as thermal boundary conditions for sequential thermal-elastoplastic analysis, enabling the assessment of fire-induced structural responses. Additionally, non-deterministic material properties caused by manufacturing imperfections are considered to analyze their impacts on uncertain high-temperature structural ductile deformation. To achieve rapid assessment, a Virtual Modeling (VM) technique is introduced to capture the nonlinear relationship between physical input parameters and corresponding structural responses. The proposed CI-VSA framework is applied to two real steel structures, a steel-framed factory and a transmission tower, to verify its efficiency and accuracy. The results demonstrate that, compared to traditional simulation-based prediction methods, the proposed CI-VSA framework reduces computational resource consumption by 99% and achieves highly accurate predictions for most sample points, with relative errors below 1%, under a training sample size of 1,000. In practice, the CI-VSA framework enables continuous prediction of spatiotemporal structural responses through the analysis of fire-thermal-structural interactions, achieves real-time updates of structural safety statuses, and ultimately provides early-stage safety warnings.

Keywords: Ductile failure; Fire-structural coupling analysis; Steel structural fire safety; Probabilistic structural fire engineering; Uncertainty quantification; Machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:258:y:2025:i:c:s0951832025001218

DOI: 10.1016/j.ress.2025.110918

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