DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, JJ | ko |
dc.contributor.author | Yun, Chung Bang | ko |
dc.date.accessioned | 2008-09-03T08:38:06Z | - |
dc.date.available | 2008-09-03T08:38:06Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2006-05 | - |
dc.identifier.citation | STRUCTURAL ENGINEERING AND MECHANICS, v.23, no.1, pp.75 - 95 | - |
dc.identifier.issn | 1225-4568 | - |
dc.identifier.uri | http://hdl.handle.net/10203/7257 | - |
dc.description.abstract | Two-step identification approaches for effective bridge health monitoring are proposed to alleviate the issues associated with many unknown parameters faced in real structures and to improve the accuracy in the estimate results. It is Suitable for on-line monitoring scheme, since the damage assessment is not always needed to be carried Out whereas the alarming for damages is to be Continuously monitored. In the first step for screening potentially damaged members, a damage indicator method based on modal strain energy, probabilistic neural networks and the conventional neural networks using grouping technique are utilized and then the conventional neural networks technique is utilized for damage assessment on the screened members in the second step. The effectiveness of the proposed methods is investigated through a Field test on the northern-most span of the old Hannam Grand Bridge over the Han River in Seoul, Korea. | - |
dc.description.sponsorship | This study is supported by Smart Infra-Structure Technology Center (SISTeC) at KAIST, which is sponsored by Ministry of Science and Technology (MOST) and the Korea Science and Engineering Foundation (KOSEF). Their financial supports are greatly acknowledged. | en |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | TECHNO-PRESS | - |
dc.subject | PROBABILISTIC NEURAL NETWORKS | - |
dc.subject | PLATE-LIKE STRUCTURES | - |
dc.subject | CABLE-STAYED BRIDGE | - |
dc.subject | DAMAGE-DETECTION | - |
dc.subject | IDENTIFICATION | - |
dc.subject | DIAGNOSIS | - |
dc.subject | DENSITY | - |
dc.title | Two-step approaches for effective bridge health monitoring | - |
dc.type | Article | - |
dc.identifier.wosid | 000237155400006 | - |
dc.identifier.scopusid | 2-s2.0-33646737100 | - |
dc.type.rims | ART | - |
dc.citation.volume | 23 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 75 | - |
dc.citation.endingpage | 95 | - |
dc.citation.publicationname | STRUCTURAL ENGINEERING AND MECHANICS | - |
dc.contributor.localauthor | Yun, Chung Bang | - |
dc.contributor.nonIdAuthor | Lee, JJ | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | bridge health monitoring | - |
dc.subject.keywordAuthor | two-step approach | - |
dc.subject.keywordAuthor | modal strain energy | - |
dc.subject.keywordAuthor | probabilistic neural networks | - |
dc.subject.keywordAuthor | neural networks | - |
dc.subject.keywordAuthor | field tests | - |
dc.subject.keywordPlus | PROBABILISTIC NEURAL NETWORKS | - |
dc.subject.keywordPlus | PLATE-LIKE STRUCTURES | - |
dc.subject.keywordPlus | CABLE-STAYED BRIDGE | - |
dc.subject.keywordPlus | DAMAGE-DETECTION | - |
dc.subject.keywordPlus | IDENTIFICATION | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordPlus | DENSITY | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.