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Article: Spherical subset simulation (S3) for solving non-linear dynamical reliability problems

TitleSpherical subset simulation (S<sup>3</sup>) for solving non-linear dynamical reliability problems
Authors
KeywordsFailure probability
MCMC
Markov Chain Monte Carlo
Spherical subset simulation
Non-linear dynamic reliability
Issue Date2010
Citation
International Journal of Reliability and Safety, 2010, v. 4, n. 2-3, p. 122-138 How to Cite?
AbstractThis paper presents a methodology for general non-linear reliability problems. It is based on dividing the failure domain into a number of appropriately selected subregions and calculating the failure probability as a sum of the probabilities for each subregion. The probability of each subregion is calculated as a product of factors, which can be estimated accurately by a relatively small number of samples generated according to the conditional distribution corresponding to the particular subregion. These samples are generated through Markov Chain Monte Carlo simulations using a slice-sampling-based algorithm proposed by the authors. The proposed method is robust and is suitable for high-dimensional problems. This is in contrast to popular importance sampling methods that often break down for high-dimensional problems. The method is found to be significantly more efficient than Monte Carlo simulations. The efficiency of the method is demonstrated with two examples involving 4000 and 1501 random variables. Copyright © 2010 Inderscience Enterprises Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/296068
ISSN
2023 SCImago Journal Rankings: 0.170

 

DC FieldValueLanguage
dc.contributor.authorKatafygiotis, Lambros-
dc.contributor.authorCheung, Sai Hung-
dc.contributor.authorYuen, Ka Veng-
dc.date.accessioned2021-02-11T04:52:46Z-
dc.date.available2021-02-11T04:52:46Z-
dc.date.issued2010-
dc.identifier.citationInternational Journal of Reliability and Safety, 2010, v. 4, n. 2-3, p. 122-138-
dc.identifier.issn1479-389X-
dc.identifier.urihttp://hdl.handle.net/10722/296068-
dc.description.abstractThis paper presents a methodology for general non-linear reliability problems. It is based on dividing the failure domain into a number of appropriately selected subregions and calculating the failure probability as a sum of the probabilities for each subregion. The probability of each subregion is calculated as a product of factors, which can be estimated accurately by a relatively small number of samples generated according to the conditional distribution corresponding to the particular subregion. These samples are generated through Markov Chain Monte Carlo simulations using a slice-sampling-based algorithm proposed by the authors. The proposed method is robust and is suitable for high-dimensional problems. This is in contrast to popular importance sampling methods that often break down for high-dimensional problems. The method is found to be significantly more efficient than Monte Carlo simulations. The efficiency of the method is demonstrated with two examples involving 4000 and 1501 random variables. Copyright © 2010 Inderscience Enterprises Ltd.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Reliability and Safety-
dc.subjectFailure probability-
dc.subjectMCMC-
dc.subjectMarkov Chain Monte Carlo-
dc.subjectSpherical subset simulation-
dc.subjectNon-linear dynamic reliability-
dc.titleSpherical subset simulation (S<sup>3</sup>) for solving non-linear dynamical reliability problems-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1504/IJRS.2010.032442-
dc.identifier.scopuseid_2-s2.0-78651581644-
dc.identifier.volume4-
dc.identifier.issue2-3-
dc.identifier.spage122-
dc.identifier.epage138-
dc.identifier.eissn1479-3903-
dc.identifier.issnl1479-389X-

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