Publication: A novel maximum volume sampling model for reliability analysis
dc.contributor.author | Meng, Zeng | |
dc.contributor.author | Pang, Yongsheng | |
dc.contributor.author | Wu, Zhigen | |
dc.contributor.author | Ren, Shanhong | |
dc.contributor.author | Yıldız, Ali Rıza | |
dc.contributor.buuauthor | YILDIZ, ALİ RIZA | |
dc.contributor.department | Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/Otomotiv Mühendisliği Bölümü. | |
dc.contributor.researcherid | F-7426-2011 | |
dc.date.accessioned | 2024-06-11T12:04:48Z | |
dc.date.available | 2024-06-11T12:04:48Z | |
dc.date.issued | 2021-10-10 | |
dc.description.abstract | In this study, a maximum volume sampling model is proposed to improve the accuracy and efficiency of reliability computation. An ellipsoid is constructed with the maximum volume approach in a safe domain, and a new maximum volume optimization method is proposed. The sampling model only computes the samples outside the ellipsoid, which considerably enhances computational efficiency. Furthermore, the uniform sampling strategy and Givens transformation are adopted to efficiently solve the maximum volume optimization model. A series system example, a three-dimensional rock slope example, and an arch bridge example are tested to verify the validity of the proposed maximum volume sampling model. The results indicate that the maximum volume sampling model displays high accuracy and efficiency. | |
dc.description.sponsorship | National Natural Science Foundation of China (NSFC) - 11972143 | |
dc.description.sponsorship | Natural Science Foundation of Anhui Province - JZ2021AKZR0357 | |
dc.description.sponsorship | State Key Laboratory of Reliability and Intelligence of Electrical Equipment - EERI_KF2020002 | |
dc.identifier.doi | 10.1016/j.apm.2021.10.025 | |
dc.identifier.eissn | 1872-8480 | |
dc.identifier.endpage | 810 | |
dc.identifier.issn | 0307-904X | |
dc.identifier.startpage | 797 | |
dc.identifier.uri | https://doi.org/10.1016/j.apm.2021.10.025 | |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0307904X21004947 | |
dc.identifier.uri | https://hdl.handle.net/11452/42001 | |
dc.identifier.volume | 102 | |
dc.identifier.wos | 000720465200005 | |
dc.indexed.wos | WOS.SCI | |
dc.language.iso | en | |
dc.publisher | Elsevier Science | |
dc.relation.journal | Applied Mathematical Modelling | |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.subject | Subset simulation | |
dc.subject | Probability | |
dc.subject | Approximate | |
dc.subject | Efficiency | |
dc.subject | Stability | |
dc.subject | Accuracy | |
dc.subject | Moments | |
dc.subject | Reliability | |
dc.subject | Optimization | |
dc.subject | Sampling strategy | |
dc.subject | Givens transformation | |
dc.subject | Maximum volume sampling model | |
dc.subject | Engineering | |
dc.subject | Mathematics | |
dc.subject | Mechanics | |
dc.title | A novel maximum volume sampling model for reliability analysis | |
dc.type | Article | |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | 89fd2b17-cb52-4f92-938d-a741587a848d | |
relation.isAuthorOfPublication.latestForDiscovery | 89fd2b17-cb52-4f92-938d-a741587a848d |