{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1050580605009778816.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"HDL","@value":"https://hdl.handle.net/2324/7164839"}}],"resourceType":"学術雑誌論文(journal article)","dc:title":[{"@language":"en","@value":"Bayesian RC-Frame Finite Element Model Updating and Damage Estimation Using Nested Sampling with Nonlinear Time History"}],"dc:language":"en","description":[{"type":"Abstract","notation":[{"@language":"en","@value":"This paper proposes a Bayesian RC-frame finite element model updating (FEMU) and damage state estimation approach using the nonlinear acceleration time history based on nested sampling. Numerical RC-frame finite element model (FEM) parameters are selected through nested sampling, and their probability density is estimated using nonlinear time history. In the first step, we estimate the error standard deviation and select the FEM parameters that are required to be updated by FEMU. In the second step, we estimate the probability density of the selected parameters and realize the FEMU through the resampling method and kernel density estimation (KDE). Additionally, we propose a damage state estimate approach, which is a derivative method of the FEMU sample. The numerical results demonstrate that the proposed approach is reliable for the Bayesian FEMU and damage state estimation using nonlinear time history."}]},{"type":"Other","notation":[{"@language":"en","@value":"This article belongs to the Special Issue Structural Identification and Damage Evaluation by Integrating Physics-Based Models with Data"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1070580605009778817","@type":"Researcher","personIdentifier":[{"@type":"ORCID","@value":"0000-0001-5738-6758"}],"foaf:name":[{"@language":"en","@value":"Wang, Kunyang"},{"@language":"ja","@value":"ワン, クンヤン"}],"jpcoar:affiliationName":[{"@language":"en","@value":"Division of Structural and Earthquake Engineering, Department of Civil Engineering, Graduate School of Engineering, Kyushu University"},{"@language":"ja","@value":"九州大学大学院工学府土木工学専攻"}]},{"@id":"https://cir.nii.ac.jp/crid/1070580605009778818","@type":"Researcher","personIdentifier":[{"@type":"ORCID","@value":"0000-0002-6523-1980"}],"foaf:name":[{"@language":"en","@value":"Kajita, Yukihide"},{"@language":"ja","@value":"梶田, 幸秀"},{"@language":"ja-Kana","@value":"カジタ, ユキヒデ"}],"jpcoar:affiliationName":[{"@language":"en","@value":"Department of Civil and Structural Engineering, Faculty of Engineering, Kyushu University"},{"@language":"ja","@value":"九州大学大学院工学研究院社会基盤部門"}]},{"@id":"https://cir.nii.ac.jp/crid/1070580605009778816","@type":"Researcher","foaf:name":[{"@language":"en","@value":"Yang, Yaoxin"}],"jpcoar:affiliationName":[{"@language":"en","@value":"YJK Building Software"}]}],"publication":{"publicationIdentifier":[{"@type":"EISSN","@value":"20755309"},{"@type":"PISSN","@value":"20755309"}],"prism:publicationName":[{"@language":"en","@value":"Buildings"}],"dc:publisher":[{"@value":"MDPI"}],"prism:publicationDate":"2023-05-14","prism:volume":"13","prism:number":"5","prism:startingPage":"1281"},"dcterms:accessRights":"http://purl.org/coar/access_right/c_abf2","dc:rights":["Creative Commons Attribution 4.0 International","© 2023 by the authors."],"foaf:topic":[{"@id":"https://cir.nii.ac.jp/all?q=Bayesian%20model%20updating","dc:title":"Bayesian model updating"},{"@id":"https://cir.nii.ac.jp/all?q=structural%20health%20monitoring","dc:title":"structural health monitoring"},{"@id":"https://cir.nii.ac.jp/all?q=nested%20sampling","dc:title":"nested sampling"},{"@id":"https://cir.nii.ac.jp/all?q=Bayesian%20model%20selection","dc:title":"Bayesian model selection"},{"@id":"https://cir.nii.ac.jp/all?q=finite%20element%20model","dc:title":"finite element model"},{"@id":"https://cir.nii.ac.jp/all?q=nonlinear%20model","dc:title":"nonlinear model"},{"@id":"https://cir.nii.ac.jp/all?q=damage%20degree%20estimation","dc:title":"damage degree estimation"}],"dcterms:subject":[{"subjectScheme":"Other","notation":[{"@language":"en","@value":"Bayesian model updating"}]},{"subjectScheme":"Other","notation":[{"@language":"en","@value":"structural health monitoring"}]},{"subjectScheme":"Other","notation":[{"@language":"en","@value":"nested sampling"}]},{"subjectScheme":"Other","notation":[{"@language":"en","@value":"Bayesian model selection"}]},{"subjectScheme":"Other","notation":[{"@language":"en","@value":"finite element model"}]},{"subjectScheme":"Other","notation":[{"@language":"en","@value":"nonlinear model"}]},{"subjectScheme":"Other","notation":[{"@language":"en","@value":"damage degree estimation"}]}],"relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1060580605009778816","@type":"Product","relationType":["references"]}],"dataSourceIdentifier":[{"@type":"IRDB","@value":"oai:irdb.nii.ac.jp:01211:0006025392"},{"@type":"OPENAIRE","@value":"jairo_______::c415d0b700447d5172b1d25c41c52516"}]}