Support vector machines in reliability calculations of engineering structures
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Date
2025-08-07
Authors
Sadílková Šomodíková, Martina
Lehký, David
0000-0001-7117-4946Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
CRC Press
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Abstract
In the paper, a metamodeling approach based on support vector regression is studied as a promising tool in the assessment of reliability level. The method consists of two steps: firstly, an approximation of the original limit state function is performed, and in the second step a failure probability or reliability index is calculated with a simpler, approximated function using traditional simulation techniques. Two problems with explicit limit state functions are used to study the effectivity of the method. In order to be as effective as possible with respect to computational effort, a stratified Latin Hypercube Sampling simulation method is utilized to properly select training set elements. The accuracy of the method is analyzed and compared with other surrogate modeling methods, namely the polynomial- and artificial neural network-based response surface method, achieving comparable results.
In the paper, a metamodeling approach based on support vector regression is studied as a promising tool in the assessment of reliability level. The method consists of two steps: firstly, an approximation of the original limit state function is performed, and in the second step a failure probability or reliability index is calculated with a simpler, approximated function using traditional simulation techniques. Two problems with explicit limit state functions are used to study the effectivity of the method. In order to be as effective as possible with respect to computational effort, a stratified Latin Hypercube Sampling simulation method is utilized to properly select training set elements. The accuracy of the method is analyzed and compared with other surrogate modeling methods, namely the polynomial- and artificial neural network-based response surface method, achieving comparable results.
In the paper, a metamodeling approach based on support vector regression is studied as a promising tool in the assessment of reliability level. The method consists of two steps: firstly, an approximation of the original limit state function is performed, and in the second step a failure probability or reliability index is calculated with a simpler, approximated function using traditional simulation techniques. Two problems with explicit limit state functions are used to study the effectivity of the method. In order to be as effective as possible with respect to computational effort, a stratified Latin Hypercube Sampling simulation method is utilized to properly select training set elements. The accuracy of the method is analyzed and compared with other surrogate modeling methods, namely the polynomial- and artificial neural network-based response surface method, achieving comparable results.
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Citation
Engineering Materials, Structures, Systems and Methods for a More Sustainable Future. 2025, p. 1113-1118.
https://www.taylorfrancis.com/chapters/edit/10.1201/9781003677895-187/support-vector-machines-reliability-calculations-engineering-structures-šomodíková-lehký
https://www.taylorfrancis.com/chapters/edit/10.1201/9781003677895-187/support-vector-machines-reliability-calculations-engineering-structures-šomodíková-lehký
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Peer-reviewed
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Accepted version
Date of access to the full text
2026-08-07
Language of document
en
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Creative Commons Attribution-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nd/4.0/
http://creativecommons.org/licenses/by-nd/4.0/