A novel damage evaluation method for timber utility poles based on wavelet packet transform and support vector machine

Yu, Y; Dackermann, U; Li, J

A novel damage evaluation method for timber utility poles based on wavelet packet transform and support vector machine

Yu, Y

Dackermann, U Li, J

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Publication Type:: Conference Proceeding
Citation:: SHMII 2015 - 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, 2015
Issue Date:: 2015-01-01

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	SHMII7-A Novel Damage Evaluation Method for Timber Utility Poles Based on Wavelet Packet Transform and Support Vector Machine.doc	Accepted Manuscript version	1.49 MB	Microsoft Word	View/Open

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Field	Value	Language
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191	en_US
dc.contributor.author	Dackermann, U	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	SHMII 2015 - 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, 2015	en_US
dc.identifier.uri	http://hdl.handle.net/10453/36621
dc.description.abstract	© 2015, International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII. All rights reserved. This paper presents a novel damage evaluation approach using wavelet packet transform (WPT) and support vector machine (SVM) for the damage assessment of timber utility poles. The method is based on guided wave (GW) testing using a multi-sensor system. In the new method, first, WPT is utilized to extract entropy features of GW signals. Then, to eliminate the multicollinearity between extracted features, principle component analysis (PCA) is adopted and entropy features are replaced by a few principle components. Finally, a classifier model based on SVM is constructed to assess the pole condition. To improve the estimation accuracy of the model, genetic algorithm (GA) is used to optimize the parameters in the SVM. The new method is validated on laboratory timber specimens (undamaged and damaged) that are experimentally tested using an impact hammer for wave excitation and a multi-sensor array for wave response recording. WPT-based entropy feature extraction and PCA is subsequently applied to the recorded wave signals and the damage condition of the timber specimen is identified using the pre-trained classifier. The experimental results verify that the proposed method is effective achieving a high identification accuracy of up to 95%.	en_US
dc.relation	http://purl.org/au-research/grants/arc/LP110200162
dc.relation.ispartof	SHMII 2015 - 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure	en_US
dc.title	A novel damage evaluation method for timber utility poles based on wavelet packet transform and support vector machine	en_US
dc.type	Conference Proceeding
dc.location.activity	Torino, Italy
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:

© 2015, International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII. All rights reserved. This paper presents a novel damage evaluation approach using wavelet packet transform (WPT) and support vector machine (SVM) for the damage assessment of timber utility poles. The method is based on guided wave (GW) testing using a multi-sensor system. In the new method, first, WPT is utilized to extract entropy features of GW signals. Then, to eliminate the multicollinearity between extracted features, principle component analysis (PCA) is adopted and entropy features are replaced by a few principle components. Finally, a classifier model based on SVM is constructed to assess the pole condition. To improve the estimation accuracy of the model, genetic algorithm (GA) is used to optimize the parameters in the SVM. The new method is validated on laboratory timber specimens (undamaged and damaged) that are experimentally tested using an impact hammer for wave excitation and a multi-sensor array for wave response recording. WPT-based entropy feature extraction and PCA is subsequently applied to the recorded wave signals and the damage condition of the timber specimen is identified using the pre-trained classifier. The experimental results verify that the proposed method is effective achieving a high identification accuracy of up to 95%.

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http://hdl.handle.net/10453/36621