On the use of the cepstrum and artificial neural networks to identify structural mass changes from response-only measurements

Dackermann, U; Smith, WA; Li, J; Randall, RB

On the use of the cepstrum and artificial neural networks to identify structural mass changes from response-only measurements

Dackermann, U

Smith, WA Li, J

Randall, RB

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Publication Type:: Conference Proceeding
Citation:: Proceedings of ISMA 2014 - International Conference on Noise and Vibration Engineering and USD 2014 - International Conference on Uncertainty in Structural Dynamics, 2014, pp. 3739 - 3750
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Dackermann, U https://orcid.org/0000-0003-4406-4738	en_US
dc.contributor.author	Smith, WA	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048	en_US
dc.contributor.author	Randall, RB	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings of ISMA 2014 - International Conference on Noise and Vibration Engineering and USD 2014 - International Conference on Uncertainty in Structural Dynamics, 2014, pp. 3739 - 3750	en_US
dc.identifier.isbn	9789073802919	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33156
dc.description.abstract	This paper presents a damage identification technique based on response-only data utilising cepstrum analysis and artificial neural networks (ANNs) for the identification of added mass in a two-storey framed structure. The proposed technique applies cepstrum-based operational modal analysis (OMA) for the regeneration of frequency response functions (FRFs), and added mass is detected through the combined use of principal component analysis (PCA) for data compression and ANNs for feature extraction and pattern recognition. In particular, different treatments of the zeros in the curve-fitting of the transfer function cepstrum are investigated to improve the automation potential of the method for application in continuous online structural health monitoring (SHM). The proposed technique is validated on a laboratory structure tested on a large-scale shake table with ambient base loading. The results of the investigation show that the method is effective in identifying added mass based on response-only measurements.	en_US
dc.relation.ispartof	Proceedings of ISMA 2014 - International Conference on Noise and Vibration Engineering and USD 2014 - International Conference on Uncertainty in Structural Dynamics	en_US
dc.title	On the use of the cepstrum and artificial neural networks to identify structural mass changes from response-only measurements	en_US
dc.type	Conference Proceeding
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.publication-status	Published	en_US

Abstract:

This paper presents a damage identification technique based on response-only data utilising cepstrum analysis and artificial neural networks (ANNs) for the identification of added mass in a two-storey framed structure. The proposed technique applies cepstrum-based operational modal analysis (OMA) for the regeneration of frequency response functions (FRFs), and added mass is detected through the combined use of principal component analysis (PCA) for data compression and ANNs for feature extraction and pattern recognition. In particular, different treatments of the zeros in the curve-fitting of the transfer function cepstrum are investigated to improve the automation potential of the method for application in continuous online structural health monitoring (SHM). The proposed technique is validated on a laboratory structure tested on a large-scale shake table with ambient base loading. The results of the investigation show that the method is effective in identifying added mass based on response-only measurements.

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http://hdl.handle.net/10453/33156