Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/9111
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dc.contributor.authorLee, Ikjin-
dc.contributor.authorLee, Ungki-
dc.contributor.authorRamu, Palaniappan-
dc.contributor.authorYadav, Deepanshu-
dc.contributor.authorBayrak, Gamze-
dc.contributor.authorAcar, Erdem-
dc.date.accessioned2022-11-30T19:33:15Z-
dc.date.available2022-11-30T19:33:15Z-
dc.date.issued2022-
dc.identifier.issn1615-147X-
dc.identifier.issn1615-1488-
dc.identifier.urihttps://doi.org/10.1007/s00158-022-03431-6-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/9111-
dc.description.abstractDesign of structural and multidisciplinary systems under uncertainties requires estimation of their reliability or equivalently the probability of failure under the given operating conditions. Various high technology systems including aircraft and nuclear power plants are designed for very small probabilities of failure, and estimation of these small probabilities is computationally challenging. Even though substantial number of approaches have been proposed to reduce the computational burden, there is no established guideline to decide which approach is the best choice for a given problem. This paper provides a review of the approaches developed for small probability estimation of structural or multidisciplinary systems and enlists the criterion/metrics to choose the preferred approach amongst the existing ones, for a given problem. First, the existing approaches are categorized into the sampling-based, the surrogate-based, and statistics of extremes based approaches. Next, the small probability estimation methods developed for time-independent systems and the ones tailored for time-dependent systems are discussed, respectively. Then, some real-life engineering applications in structural and multidisciplinary design studies are summarized. Finally, concluding remarks are provided, and areas for future research are suggested.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofStructural and Multidisciplinary Optimizationen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectExtreme value statisticsen_US
dc.subjectHigh reliabilityen_US
dc.subjectMachine learningen_US
dc.subjectRare eventen_US
dc.subjectSamplingen_US
dc.subjectSmall failure probabilityen_US
dc.subjectSurrogate modelen_US
dc.subjectRare-Event Probabilityen_US
dc.subjectImportance Sampling Methoden_US
dc.subjectSupport Vector Regressionen_US
dc.subjectAdaptive Directional Stratificationen_US
dc.subjectStructural Reliability Assessmenten_US
dc.subjectExtreme-Value Distributionen_US
dc.subjectArtificial Neural-Networken_US
dc.subjectSubset Simulation Methoden_US
dc.subjectMonte-Carloen_US
dc.subjectCross-Entropyen_US
dc.titleSmall failure probability: principles, progress and perspectivesen_US
dc.typeReviewen_US
dc.identifier.volume65en_US
dc.identifier.issue11en_US
dc.identifier.wosWOS:000878655500001en_US
dc.identifier.scopus2-s2.0-85141179789en_US
dc.institutionauthorAcar, Erdem-
dc.identifier.doi10.1007/s00158-022-03431-6-
dc.authorscopusid15073177700-
dc.authorscopusid57197782247-
dc.authorscopusid18042509000-
dc.authorscopusid57951305900-
dc.authorscopusid57190487667-
dc.authorscopusid55308448100-
dc.relation.publicationcategoryDiğeren_US
dc.identifier.scopusqualityQ1-
dc.ozel2022v3_Editen_US
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypeReview-
item.grantfulltextnone-
crisitem.author.dept02.7. Department of Mechanical Engineering-
Appears in Collections:Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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