DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jin, Seung-Seop | ko |
dc.contributor.author | Jung, Hyung-Jo | ko |
dc.date.accessioned | 2016-07-04T03:09:41Z | - |
dc.date.available | 2016-07-04T03:09:41Z | - |
dc.date.created | 2016-05-10 | - |
dc.date.created | 2016-05-10 | - |
dc.date.issued | 2016-05 | - |
dc.identifier.citation | COMPUTERS & STRUCTURES, v.168, pp.30 - 45 | - |
dc.identifier.issn | 0045-7949 | - |
dc.identifier.uri | http://hdl.handle.net/10203/209000 | - |
dc.description.abstract | Despite the numerous studies concerning finite element model updating (FEMU), a challenging computational cost issue persists. Therefore, surrogate modeling has recently gained considerable attention in FEMU. Conventionally, surrogate models are constructed by identical samples for all outputs. It is very inefficient and subjective, if various response-surfaces exhibit even for identical parameters. Accordingly, we propose a sequential surrogate modeling for FEMU. It uses infill criteria to guide sampling for updating surrogate models automatically. The proposed method is successful to construct the different response-surfaces and apply FEMU. It is promising for constructing surrogate models with minimal user intervention and tremendous computational efficiency. (C) 2016 Elsevier Ltd. All rights reserved | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | RESPONSE-SURFACE METHOD | - |
dc.subject | MONTE-CARLO-SIMULATION | - |
dc.subject | STOCHASTIC PREDICTIONS | - |
dc.subject | GLOBAL OPTIMIZATION | - |
dc.subject | STRUCTURAL IDENTIFICATION | - |
dc.subject | POLYMERIC NANOCOMPOSITES | - |
dc.subject | FRAMEWORK | - |
dc.subject | UNCERTAINTY | - |
dc.subject | RELIABILITY | - |
dc.subject | PARAMETERS | - |
dc.title | Sequential surrogate modeling for efficient finite element model updating | - |
dc.type | Article | - |
dc.identifier.wosid | 000374079100003 | - |
dc.identifier.scopusid | 2-s2.0-84959547315 | - |
dc.type.rims | ART | - |
dc.citation.volume | 168 | - |
dc.citation.beginningpage | 30 | - |
dc.citation.endingpage | 45 | - |
dc.citation.publicationname | COMPUTERS & STRUCTURES | - |
dc.identifier.doi | 10.1016/j.compstruc.2016.02.005 | - |
dc.contributor.localauthor | Jung, Hyung-Jo | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Finite element model updating | - |
dc.subject.keywordAuthor | Surrogate model | - |
dc.subject.keywordAuthor | Non-stationary response-surface | - |
dc.subject.keywordAuthor | Kriging model | - |
dc.subject.keywordAuthor | Sequential modeling | - |
dc.subject.keywordPlus | RESPONSE-SURFACE METHOD | - |
dc.subject.keywordPlus | MONTE-CARLO-SIMULATION | - |
dc.subject.keywordPlus | STOCHASTIC PREDICTIONS | - |
dc.subject.keywordPlus | GLOBAL OPTIMIZATION | - |
dc.subject.keywordPlus | STRUCTURAL IDENTIFICATION | - |
dc.subject.keywordPlus | POLYMERIC NANOCOMPOSITES | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordPlus | UNCERTAINTY | - |
dc.subject.keywordPlus | RELIABILITY | - |
dc.subject.keywordPlus | PARAMETERS | - |
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