Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/723
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dc.contributor.authorBayrak, Gamze-
dc.contributor.author Acar, Erdem-
dc.date.accessioned2019-03-16T07:46:31Z
dc.date.available2019-03-16T07:46:31Z
dc.date.issued2018-03
dc.identifier.citationBayrak, G., & Acar, E. (2017). Reliability Estimation Using Markov Chain Monte Carlo–Based Tail Modeling. AIAA Journal, 56(3), 1211-1224.en_US
dc.identifier.issn00011452
dc.identifier.urihttps://arc.aiaa.org/doi/pdf/10.2514/1.J055947-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/723-
dc.description.abstractTail modeling is an efficient method used in reliability estimation of highly safe structures. Classical tail modeling is based on performing limit-state function evaluations through a sampling scheme, selecting a threshold value to specify the tail part of the cumulative distribution function, fitting a proper model to the tail part, and estimating the reliability. In this approach, limit-state function calculations that do not belong to the tail part are mostly discarded, and so majority of limit-state evaluations are wasted. In this paper, Markov chain Monte Carlo method with Metropolis–Hastings algorithm is used to draw samples from the tail part only so that a more accurate reliability index prediction is achieved. A commonly used proposal distribution formula is modified by using a scale parameter. The optimal value of this scale parameter is obtained for various numerical example problems with a varying number of random variables, and an approximate relationship is obtained between the optimal value of the scale parameter and the number of random variables. The approximate relationship is tested on the reliability prediction of a horizontal axis wind turbine and observed to work well. It is also found that the proposed approach is more accurate than the classical tail modeling when the number of variables is less than or equal to four. For a larger number of random variables, none of the two approaches are found to be superior to another.en_US
dc.language.isoenen_US
dc.publisherAmerican Institute of Aeronautics and Astronautics Inc.en_US
dc.relation.ispartofAIAA Journalen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectReliability analysisen_US
dc.subjectReliabilityen_US
dc.subjectTime-dependent reliabilityen_US
dc.titleReliability estimation using Markov chain Monte carlo-based tail modelingen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Mechanical Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Makine Mühendisliği Bölümütr_TR
dc.identifier.volume56
dc.identifier.issue3
dc.identifier.startpage1211
dc.identifier.endpage1224
dc.relation.tubitakinfo:eu-repo/grantAgreement/TÜBİTAK/MAG/214M205
dc.authorid0000-0002-3661-5563-
dc.identifier.wosWOS:000426601300025en_US
dc.identifier.scopus2-s2.0-85043253515en_US
dc.institutionauthorAcar, Erdem-
dc.identifier.doi10.2514/1.J055947-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
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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