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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.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.

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.titleJournal of Systems Engineering and Electronics

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

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