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dc.contributor.authorPathirage, C.
dc.contributor.authorLi, Jun
dc.contributor.authorLi, L.
dc.contributor.authorHao, Hong
dc.contributor.authorLiu, Wan-Quan
dc.date.accessioned2018-12-13T09:10:13Z
dc.date.available2018-12-13T09:10:13Z
dc.date.created2018-12-12T02:46:39Z
dc.date.issued2018
dc.identifier.citationPathirage, C. and Li, J. and Li, L. and Hao, H. and Liu, W. 2018. Application of deep autoencoder model for structural condition monitoring. Journal of Systems Engineering and Electronics. 29 (4): pp. 873-880.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/71450
dc.identifier.doi10.21629/JSEE.2018.04.22
dc.description.abstract

Damage detection in structures is performed via vibration based structural identification. Modal information, such as frequencies and mode shapes, are widely used for structural damage detection to indicate the health conditions of civil structures. The deep learning algorithm that works on a multiple layer neural network model termed as deep autoencoder is proposed to learn the relationship between the modal information and structural stiffness parameters. This is achieved via dimension reduction of the modal information feature and a non-linear regression against the structural stiffness parameters. Numerical tests on a symmetrical steel frame model are conducted to generate the data for the training and validation, and to demonstrate the efficiency of the proposed approach for vibration based structural damage detection.

dc.titleApplication of deep autoencoder model for structural condition monitoring
dc.typeJournal Article
dcterms.source.volume29
dcterms.source.number4
dcterms.source.startPage873
dcterms.source.endPage880
dcterms.source.issn1671-1793
dcterms.source.titleJournal of Systems Engineering and Electronics
curtin.note

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curtin.departmentSchool of Civil and Mechanical Engineering (CME)
curtin.accessStatusOpen access


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