Sequential damage detection approaches for beams using time-modal features and artificial neural networks SCIE SCOPUS
DC Field | Value | Language |
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dc.contributor.author | Park, J.-H. | - |
dc.contributor.author | Kim, J.-T. | - |
dc.contributor.author | Hong, D.-S. | - |
dc.contributor.author | Ho, D.-D. | - |
dc.contributor.author | Yi, J.-H. | - |
dc.date.accessioned | 2020-04-20T09:40:31Z | - |
dc.date.available | 2020-04-20T09:40:31Z | - |
dc.date.created | 2020-01-28 | - |
dc.date.issued | 2009-06 | - |
dc.identifier.issn | 0022-460X | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/4283 | - |
dc.description.abstract | In this study, sequential approaches for damage detection in beams using time-modal features and artificial neural networks are proposed. The scheme of the sequential approaches mainly consists of two phases: time-domain damage monitoring and modal-domain damage estimation. In the first phase, an acceleration-based neural networks (ABNN) algorithm is designed to monitor the occurrence of damage in a structure by using cross-covariance functions of acceleration signals measured from two different sensors. By using the acceleration feature, the ABNN is trained for potential damage scenarios and loading patterns which are unknown. In the second phase, a modal feature-based neural networks (MBNN) algorithm is designed to estimate the location and severity of damage in the structure by using mode shapes and modal strain energies. By using the modal feature, the MBNN is trained for potential damage scenarios. The feasibility and the practicality of the proposed methodology are evaluated from numerical tests on simply supported beams and also from laboratory tests on free-free beams. © 2008 Elsevier Ltd. All rights reserved. | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | Academic Press | - |
dc.title | Sequential damage detection approaches for beams using time-modal features and artificial neural networks | - |
dc.type | Article | - |
dc.citation.endPage | 474 | - |
dc.citation.startPage | 451 | - |
dc.citation.title | Journal of Sound and Vibration | - |
dc.citation.volume | 323 | - |
dc.citation.number | 1-2 | - |
dc.contributor.alternativeName | 이진학 | - |
dc.identifier.bibliographicCitation | Journal of Sound and Vibration, v.323, no.1-2, pp.451 - 474 | - |
dc.identifier.doi | 10.1016/j.jsv.2008.12.023 | - |
dc.identifier.scopusid | 2-s2.0-64449086980 | - |
dc.identifier.wosid | 000266255300026 | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordPlus | Acceleration signals | - |
dc.subject.keywordPlus | Artificial neural networks | - |
dc.subject.keywordPlus | Cross-covariance functions | - |
dc.subject.keywordPlus | Damage estimations | - |
dc.subject.keywordPlus | Damage monitoring | - |
dc.subject.keywordPlus | Detection approaches | - |
dc.subject.keywordPlus | Feature-based | - |
dc.subject.keywordPlus | Free-free beams | - |
dc.subject.keywordPlus | Laboratory tests | - |
dc.subject.keywordPlus | Loading patterns | - |
dc.subject.keywordPlus | Modal strain energies | - |
dc.subject.keywordPlus | Mode shapes | - |
dc.subject.keywordPlus | Numerical tests | - |
dc.subject.keywordPlus | Second phase | - |
dc.subject.keywordPlus | Sequential approaches | - |
dc.subject.keywordPlus | Simply supported beams | - |
dc.subject.keywordPlus | Time domains | - |
dc.subject.keywordPlus | Backpropagation | - |
dc.subject.keywordPlus | Damage detection | - |
dc.subject.keywordPlus | Sensor networks | - |
dc.subject.keywordPlus | Strain energy | - |
dc.subject.keywordPlus | Structural analysis | - |
dc.subject.keywordPlus | Neural networks | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |