Damage classification and estimation in experimental structures using time series analysis and pattern recognition

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dc.contributor.author De Lautour, OR en
dc.contributor.author Omenzetter, Piotr en
dc.date.accessioned 2011-12-02T01:08:40Z en
dc.date.issued 2010 en
dc.identifier.citation Mechanical Systems and Signal Processing 24(5):1556-1569 2010 en
dc.identifier.issn 0888-3270 en
dc.identifier.uri http://hdl.handle.net/2292/9742 en
dc.description.abstract Developed for studying long sequences of regularly sampled data, time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring (SHM). In this research, Autoregressive (AR) models were used to fit the acceleration time histories obtained from two experimental structures: a 3-storey bookshelf structure and the ASCE Phase II Experimental SHM Benchmark Structure, in undamaged and limited number of damaged states. The coefficients of the AR models were considered to be damage-sensitive features and used as input into an Artificial Neural Network (ANN). The ANN was trained to classify damage cases or estimate remaining structural stiffness. The results showed that the combination of AR models and ANNs are efficient tools for damage classification and estimation, and perform well using small number of damage-sensitive features and limited sensors. en
dc.publisher Elsevier Ltd. en
dc.relation.ispartofseries Mechanical Systems and Signal Processing 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. Details obtained from http://www.sherpa.ac.uk/romeo/issn/0888-3270/ en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title Damage classification and estimation in experimental structures using time series analysis and pattern recognition en
dc.type Journal Article en
dc.identifier.doi 10.1016/j.ymssp.2009.12.008 en
pubs.issue 5 en
pubs.begin-page 1556 en
pubs.volume 24 en
dc.rights.holder Copyright: Elsevier Ltd. en
pubs.end-page 1569 en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Article en
pubs.elements-id 99389 en
pubs.record-created-at-source-date 2010-09-01 en

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