A clustering approach for structural health monitoring on bridges

Diez, A; Khoa, NLD; Makki Alamdari, M; Wang, Y; Chen, F; Runcie, P

A clustering approach for structural health monitoring on bridges

Diez, A Khoa, NLD Makki Alamdari, M Wang, Y Chen, F

Runcie, P

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Publication Type:: Journal Article
Citation:: Journal of Civil Structural Health Monitoring, 2016, 6 (3), pp. 429 - 445
Issue Date:: 2016-07-01

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Field	Value	Language
dc.contributor.author	Diez, A	en_US
dc.contributor.author	Khoa, NLD	en_US
dc.contributor.author	Makki Alamdari, M	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729	en_US
dc.contributor.author	Runcie, P	en_US
dc.date.issued	2016-07-01	en_US
dc.identifier.citation	Journal of Civil Structural Health Monitoring, 2016, 6 (3), pp. 429 - 445	en_US
dc.identifier.issn	2190-5452	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118538
dc.description.abstract	© 2016, Springer-Verlag Berlin Heidelberg. Structural health monitoring is a process for identifying damage in civil infrastructures using sensing system. It has been increasingly employed due to advances in sensing technologies and data analytic using machine learning. A common problem within this scenario is that limited data of real structural faults are available. Therefore, unsupervised and novelty detection machine learning methods must be employed. This work presents a clustering based approach to group substructures or joints with similar behaviour on bridge and then detect abnormal or damaged ones, as part of efforts in applying structural health monitoring to the Sydney Harbour Bridge, one of iconic structures in Australia. The approach is a combination of feature extraction, a nearest neighbor based outlier removal, followed by a clustering approach over both vibration events and joints representatives. Vibration signals caused by passing vehicles from different joints are then classified and damaged joints can be detected and located. The validity of the approach was demonstrated using real data collected from the Sydney Harbour Bridge. The clustering results showed correlations among similarly located joints in different bridge zones. Moreover, it also helped to detect a damaged joint and a joint with a faulty instrumented sensor, and thus demonstrated the feasibility of the proposed clustering based approach to complement existing damage detection strategies.	en_US
dc.relation.ispartof	Journal of Civil Structural Health Monitoring	en_US
dc.relation.isbasedon	10.1007/s13349-016-0160-0	en_US
dc.title	A clustering approach for structural health monitoring on bridges	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	6	en_US
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
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	6	en_US

Abstract:

© 2016, Springer-Verlag Berlin Heidelberg. Structural health monitoring is a process for identifying damage in civil infrastructures using sensing system. It has been increasingly employed due to advances in sensing technologies and data analytic using machine learning. A common problem within this scenario is that limited data of real structural faults are available. Therefore, unsupervised and novelty detection machine learning methods must be employed. This work presents a clustering based approach to group substructures or joints with similar behaviour on bridge and then detect abnormal or damaged ones, as part of efforts in applying structural health monitoring to the Sydney Harbour Bridge, one of iconic structures in Australia. The approach is a combination of feature extraction, a nearest neighbor based outlier removal, followed by a clustering approach over both vibration events and joints representatives. Vibration signals caused by passing vehicles from different joints are then classified and damaged joints can be detected and located. The validity of the approach was demonstrated using real data collected from the Sydney Harbour Bridge. The clustering results showed correlations among similarly located joints in different bridge zones. Moreover, it also helped to detect a damaged joint and a joint with a faulty instrumented sensor, and thus demonstrated the feasibility of the proposed clustering based approach to complement existing damage detection strategies.

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http://hdl.handle.net/10453/118538