Neural network based technique for on-line bridge diagnostics

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dc.contributor.author Beskhyroun, Sherif en
dc.contributor.author Mikami, S en
dc.contributor.author Oshima, T en
dc.contributor.author Miyamori, Y en
dc.contributor.author Yamazaki, T en
dc.contributor.editor Furuta, H en
dc.coverage.spatial Osaka, Japan en
dc.date.accessioned 2011-10-30T21:11:17Z en
dc.date.issued 2009-09 en
dc.identifier.citation International Conference Structural Safety and Reliability (ICOSSAR2009), Osaka, Japan, 13 Sep 2009 - 17 Sep 2009. Editors: Furuta H. Proceedings of the International Conference Structural Safety and Reliability (ICOSSAR2009). Sep 2009 en
dc.identifier.uri http://hdl.handle.net/2292/8493 en
dc.description.abstract In this paper, a new damage identification algorithm based on artificial neural network (ANN) is presented. Several research papers have utilized ANN for damage detection and most of these papers used finite element models (FEM) for training the network. However, it is extremely difficult to create accurate FEM for complex structures and an inaccurate FEM can degrade or even lead to incorrect result in damage detection. The proposed algorithm is used to detect damage and locate its position using only the structure response data without the need for any modal identification or numerical models. The method is applied to the experimental data extracted from an out of service railway steel bridge after inducing some defects to its members. The damage was introduced to the bridge through the release of some bolts from a stiffener located on the web of the main girder of the bridge. The obtained results indicate that the current approach can identify the location of small damage effectively. en
dc.relation.ispartof International Conference Structural Safety and Reliability (ICOSSAR2009) en
dc.relation.ispartofseries Proceedings of the International Conference Structural Safety and Reliability (ICOSSAR2009) en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title Neural network based technique for on-line bridge diagnostics en
dc.type Conference Item en
dc.rights.holder Copyright: the author en
pubs.author-url http://www.furuta-lab.jp/icossar2009/ en
pubs.finish-date 2009-09-17 en
pubs.publication-status Published en
pubs.start-date 2009-09-13 en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Conference Paper en
pubs.elements-id 235015 en
pubs.record-created-at-source-date 2011-10-28 en


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