Identification of member connectivity and mass changes on a two-storey framed structure using frequency response functions and artificial neural networks

Dackermann, U; Li, J; Samali, B

Identification of member connectivity and mass changes on a two-storey framed structure using frequency response functions and artificial neural networks

Dackermann, U

Li, J

Samali, B

Permalink

Publication Type:: Journal Article
Citation:: Journal of Sound and Vibration, 2013, 332 (16), pp. 3636 - 3653
Issue Date:: 2013-08-05

Closed Access

	Filename	Description	Size
	2012006191OK.pdf		2.26 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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	2013-08-05	en_US
dc.identifier.citation	Journal of Sound and Vibration, 2013, 332 (16), pp. 3636 - 3653	en_US
dc.identifier.issn	0022-460X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26661
dc.description.abstract	This paper presents a structural health monitoring (SHM) technique that utilises pattern changes in frequency response functions (FRFs) as input parameters for a system of artificial neural networks (ANNs) to assess the structural condition of a structure. To verify the proposed method, it is applied to numerical and experimental models of a two-storey framed structure, on which structural damage is induced by member connectivity and mass changes, respectively. For the numerical structure, simulated time-history data are polluted with various levels of white Gaussian noise in order to realistically represent field-testing conditions. As a damage indicator, residual FRFs are used, which are derived by calculating the differences in FRF data between the undamaged/baseline structure and the structure with changed joint conditions or added mass. To obtain suitable patterns for neural network training, principal component analysis (PCA) techniques are adopted to reduce the size of the residual FRF data and to filter noise. A hierarchical system of individual ANNs, termed network ensemble, is then trained to map changes in PCA-reduced residual FRFs to damage conditions. The results obtained for both damage investigations, namely joint damage and mass changes, demonstrate that the proposed SHM technique is accurate and reliable in assessing the condition of the test structure numerically and experimentally based on direct FRF measurements and network ensemble analysis. From the outcomes of the individual networks, it is found that the proposed hierarchical network ensemble approach is highly efficient in filtering poor results of underperforming networks obtained from measurement locations with low damage sensitivity. © 2013 Elsevier Ltd.	en_US
dc.relation.ispartof	Journal of Sound and Vibration	en_US
dc.relation.isbasedon	10.1016/j.jsv.2013.02.018	en_US
dc.subject.classification	Acoustics	en_US
dc.title	Identification of member connectivity and mass changes on a two-storey framed structure using frequency response functions and artificial neural networks	en_US
dc.type	Journal Article
utslib.citation.volume	16	en_US
utslib.citation.volume	332	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	02 Physical Sciences	en_US
utslib.for	09 Engineering	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	closed_access
pubs.issue	16	en_US
pubs.publication-status	Published	en_US
pubs.volume	332	en_US

Abstract:

This paper presents a structural health monitoring (SHM) technique that utilises pattern changes in frequency response functions (FRFs) as input parameters for a system of artificial neural networks (ANNs) to assess the structural condition of a structure. To verify the proposed method, it is applied to numerical and experimental models of a two-storey framed structure, on which structural damage is induced by member connectivity and mass changes, respectively. For the numerical structure, simulated time-history data are polluted with various levels of white Gaussian noise in order to realistically represent field-testing conditions. As a damage indicator, residual FRFs are used, which are derived by calculating the differences in FRF data between the undamaged/baseline structure and the structure with changed joint conditions or added mass. To obtain suitable patterns for neural network training, principal component analysis (PCA) techniques are adopted to reduce the size of the residual FRF data and to filter noise. A hierarchical system of individual ANNs, termed network ensemble, is then trained to map changes in PCA-reduced residual FRFs to damage conditions. The results obtained for both damage investigations, namely joint damage and mass changes, demonstrate that the proposed SHM technique is accurate and reliable in assessing the condition of the test structure numerically and experimentally based on direct FRF measurements and network ensemble analysis. From the outcomes of the individual networks, it is found that the proposed hierarchical network ensemble approach is highly efficient in filtering poor results of underperforming networks obtained from measurement locations with low damage sensitivity. © 2013 Elsevier Ltd.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26661