Identification of added mass on a two-storey framed structure utilising FRFs and ANNs

Dackermann, U; Li, J; Samali, B

Identification of added mass on a two-storey framed structure utilising FRFs and ANNs

Dackermann, U

Li, J

Samali, B

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Publication Type:: Conference Proceeding
Citation:: Incorporating Sustainable Practice in Mechanics of Structures and Materials - Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials, 2011, pp. 757 - 762
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Dackermann, U https://orcid.org/0000-0003-4406-4738	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048	en_US
dc.contributor.author	Samali, B https://orcid.org/0000-0002-3426-7745	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Incorporating Sustainable Practice in Mechanics of Structures and Materials - Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials, 2011, pp. 757 - 762	en_US
dc.identifier.isbn	9780415616577	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19321
dc.identifier.uri	http://hdl.handle.net/10453/33171
dc.description.abstract	This paper presents a vibration-based damage detection method that utilises frequency response functions (FRFs) to identify added mass on a two-storey framed structure. Added mass is used to simulate frequency changes due to structural damage. Artificial neural networks (ANNs) are employed to map changes in FRFs to locations of the added mass. In order to obtain suitable inputs for neural network training, principalcomponent analysis (PCA) techniques are adopted to reduce the size of the FRF data and to filter noise. A hierarchy of neural network ensembles is used to take advantage of individual measurement characteristics from different sensors. The method is tested on laboratory and numerical models of a two-storey framed structure. From the two kinds of structures, FRF data are determined and compressed utilising PCA techniques. The PCAreduced FRFs are then used as input patterns for training and testing of ANN ensembles predicting different locations of added mass. © 2011 Taylor & Francis Group, London.	en_US
dc.relation.ispartof	Incorporating Sustainable Practice in Mechanics of Structures and Materials - Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials	en_US
dc.title	Identification of added mass on a two-storey framed structure utilising FRFs and ANNs	en_US
dc.type	Conference Proceeding
utslib.for	090506 Structural Engineering	en_US
dc.location.activity	Melbourne, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents a vibration-based damage detection method that utilises frequency response functions (FRFs) to identify added mass on a two-storey framed structure. Added mass is used to simulate frequency changes due to structural damage. Artificial neural networks (ANNs) are employed to map changes in FRFs to locations of the added mass. In order to obtain suitable inputs for neural network training, principalcomponent analysis (PCA) techniques are adopted to reduce the size of the FRF data and to filter noise. A hierarchy of neural network ensembles is used to take advantage of individual measurement characteristics from different sensors. The method is tested on laboratory and numerical models of a two-storey framed structure. From the two kinds of structures, FRF data are determined and compressed utilising PCA techniques. The PCAreduced FRFs are then used as input patterns for training and testing of ANN ensembles predicting different locations of added mass. © 2011 Taylor & Francis Group, London.

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http://hdl.handle.net/10453/19321