Structural Reliability Methods Applied in Analysis of Steel Elements Subjected to Fire
Publication: Journal of Engineering Mechanics
Volume 147, Issue 12
Abstract
The accuracy and efficiency of an approach using surrogate models are investigated, in comparison with traditional structural reliability methods, in the analysis of two steel structures subjected to natural fire. For this purpose, an algorithm is used, coupled to a finite-element computational package, which includes the first-order reliability method (FORM), the approximate method most used in this type of problem, and Monte Carlo simulation (MCS), which is usually taken as a reference. On the other hand, the literature indicates surrogate models as a promising alternative to maintain, at acceptable levels, the computational cost in similar analyses. Therefore, two approaches using surrogate models are also applied to the algorithm, more specifically adaptive and nonadaptive artificial neural networks. The analyses led to differences of up to 21.83% between the failure probabilities obtained via FORM and via MCS. Furthermore, while the nonadaptive approach fails to achieve sufficient accuracy, the adaptive approach is confirmed to be a viable alternative, with differences, on average, close to 1.00% and computational times less than 1.60% of the time required by MCS.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors acknowledge the support of the Federal Universities of Santa Catarina and Alagoas, and the sponsorship of this research project by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. This work was also supported by the National Council for Technological and Scientific Development (CNPq) via Grant 302489/2017-7.
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Published In
Journal of Engineering Mechanics
Volume 147 • Issue 12 • December 2021
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© 2021 American Society of Civil Engineers.
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Received: Feb 9, 2021
Accepted: Aug 19, 2021
Published online: Sep 27, 2021
Published in print: Dec 1, 2021
Discussion open until: Feb 27, 2022
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