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dc.contributor.authorFan, X.
dc.contributor.authorLi, Jun
dc.contributor.authorHao, Hong
dc.date.accessioned2018-08-08T04:41:29Z
dc.date.available2018-08-08T04:41:29Z
dc.date.created2018-08-08T03:50:44Z
dc.date.issued2017
dc.identifier.citationFan, X. and Li, J. and Hao, H. 2017. Using sparse regularization and impedance sensitivity for structural damage detection, pp. 1537-1546.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/69554
dc.description.abstract

© 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved. Electromechanical impedance (EMI) based structural damage detection methods have been widely developed in civil and mechanical engineering community. In EMI-based structural health monitoring techniques, the piezoceramic (PZT) transducer is commonly bonded on the surface of the host structure and is excited by a sweeping voltage. The impedance of the PZT transducer can be measured and used to identify the changes occurred in the host structure by using statistical damage indices, such as RMSD and CC. However, those damage indices are not able to indicate more information of structural damage, such as the location and severity. This paper proposes a new structural damage identification approach based on model updating with impedance sensitivity and using sparse regularization technique to identify the damage location and severity. The sensitivities of impedance responses with respect to the stiffness parameters of the host structure are calculated and used to identify the damage with few number of resonance frequency shifts. The effectiveness and performance of the proposed approach are demonstrated with numerical simulations on an aluminum beam.

dc.titleUsing sparse regularization and impedance sensitivity for structural damage detection
dc.typeConference Paper
dcterms.source.startPage1537
dcterms.source.endPage1546
dcterms.source.titleSHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings
dcterms.source.seriesSHMII 2017 - 8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings
dcterms.source.isbn9781925553055
curtin.departmentSchool of Civil and Mechanical Engineering (CME)
curtin.accessStatusFulltext not available


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