Development of a portable NDE system with advanced signal processing and machine learning for health condition diagnosis of in-service timber utility poles

Yu, Y; Li, J; Dackermann, U; Subhani, M

Development of a portable NDE system with advanced signal processing and machine learning for health condition diagnosis of in-service timber utility poles

Yu, Y

Li, J

Dackermann, U Subhani, M

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Publication Type:: Conference Proceeding
Citation:: Mechanics of Structures and Materials: Advancements and Challenges - Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM24 2016, 2017, pp. 1547 - 1552
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048	en_US
dc.contributor.author	Dackermann, U	en_US
dc.contributor.author	Subhani, M	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Mechanics of Structures and Materials: Advancements and Challenges - Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM24 2016, 2017, pp. 1547 - 1552	en_US
dc.identifier.isbn	9781138029934	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127682
dc.description.abstract	© 2017 Taylor & Francis Group, London. Aiming at current shortcomings of Non-Destructive Evaluation (NDE) in health condition estimation of timber utility poles, this paper put forward a novel testing method via combination of a portable NDE system, advanced signal processing and machine learning techniques. Primarily, the multi-sensing strategy is employed and incorporated in current NDE technique to capture reflected stress wave signals, avoiding difficult interpretation of complicated wave propagation by only one sensor. Secondly, advanced signal processing methods, such as Ensemble Empirical Mode Decomposition (EEMD) and Principal Component Analysis (PCA), are introduced to extract effective wave patterns that are sensitive to structural damage. Moreover, based on captured signal features, the state-of-the-art machine learning techniques are applied to implement the condition assessment. Finally, field testing results of 26 decommissioned timber poles at Mason Park in Sydney are used to validate the effectiveness of the proposed method.	en_US
dc.relation.ispartof	Mechanics of Structures and Materials: Advancements and Challenges - Proceedings of the 24th Australasian Conference on the Mechanics of Structures and Materials, ACMSM24 2016	en_US
dc.title	Development of a portable NDE system with advanced signal processing and machine learning for health condition diagnosis of in-service timber utility poles	en_US
dc.type	Conference Proceeding
utslib.for	0905 Civil 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.publication-status	Published	en_US

Abstract:

© 2017 Taylor & Francis Group, London. Aiming at current shortcomings of Non-Destructive Evaluation (NDE) in health condition estimation of timber utility poles, this paper put forward a novel testing method via combination of a portable NDE system, advanced signal processing and machine learning techniques. Primarily, the multi-sensing strategy is employed and incorporated in current NDE technique to capture reflected stress wave signals, avoiding difficult interpretation of complicated wave propagation by only one sensor. Secondly, advanced signal processing methods, such as Ensemble Empirical Mode Decomposition (EEMD) and Principal Component Analysis (PCA), are introduced to extract effective wave patterns that are sensitive to structural damage. Moreover, based on captured signal features, the state-of-the-art machine learning techniques are applied to implement the condition assessment. Finally, field testing results of 26 decommissioned timber poles at Mason Park in Sydney are used to validate the effectiveness of the proposed method.

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http://hdl.handle.net/10453/127682