Damaged cable identification in cable-stayed bridge from bridge deck strain measurements using support vector machine

Ren, J; Zhang, B; Zhu, X; Li, S

Damaged cable identification in cable-stayed bridge from bridge deck strain measurements using support vector machine

Ren, J Zhang, B Zhu, X

Li, S

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Publisher:: SAGE PUBLICATIONS INC
Publication Type:: Journal Article
Citation:: Advances in Structural Engineering, 2022, 25, (4), pp. 754-771
Issue Date:: 2022-03-01

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Field	Value	Language
dc.contributor.author	Ren, J
dc.contributor.author	Zhang, B
dc.contributor.author	Zhu, X https://orcid.org/0000-0001-5083-9320
dc.contributor.author	Li, S
dc.date.accessioned	2022-04-28T12:38:51Z
dc.date.available	2022-04-28T12:38:51Z
dc.date.issued	2022-03-01
dc.identifier.citation	Advances in Structural Engineering, 2022, 25, (4), pp. 754-771
dc.identifier.issn	1369-4332
dc.identifier.issn	2048-4011
dc.identifier.uri	http://hdl.handle.net/10453/156759
dc.description.abstract	A new two-step approach is developed for damaged cable identification in a cable-stayed bridge from deck bending strain responses using Support Vector Machine. A Damaged Cable Identification Machine (DCIM) based on support vector classification is constructed to determine the damaged cable and a Damage Severity Identification Machine (DSIM) based on support vector regression is built to estimate the damage severity. A field cable-stayed bridge with a long-term monitoring system is used to verify the proposed method. The three-dimensional Finite Element Model (FEM) of the cable-stayed bridge is established using ANSYS, and the model is validated using the field testing results, such as the mode shape, natural frequencies and its bending strain responses of the bridge under a moving vehicle. Then the validated FEM is used to simulate the bending strain responses of the longitude deck near the cable anchors when the vehicle is passing over the bridge. Different damage scenarios are simulated for each cable with various severities. Based on damage indexes vector, the training datasets and testing datasets are acquired, including single damaged cable scenarios and double damaged cable scenarios. Eventually, DCIM is trained using Support Vector Classification Machine and DSIM is trained using Support Vector Regression Machine. The testing datasets are input in DCIM and DSIM to check their accuracy and generalization capability. Different noise levels including 5%, 10%, and 20% are considered to study their anti-noise capability. The results show that DCIM and DSIM both have good generalization capability and anti-noise capability.
dc.language	English
dc.publisher	SAGE PUBLICATIONS INC
dc.relation	http://purl.org/au-research/grants/arc/DP160103197
dc.relation.ispartof	Advances in Structural Engineering
dc.relation.isbasedon	10.1177/13694332211049996
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering, 1202 Building
dc.subject.classification	Civil Engineering
dc.title	Damaged cable identification in cable-stayed bridge from bridge deck strain measurements using support vector machine
dc.type	Journal Article
utslib.citation.volume	25
utslib.for	0905 Civil Engineering
utslib.for	1202 Building
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
utslib.copyright.status	open_access	*
dc.date.updated	2022-04-28T12:38:43Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	25
utslib.citation.issue	4

Abstract:

A new two-step approach is developed for damaged cable identification in a cable-stayed bridge from deck bending strain responses using Support Vector Machine. A Damaged Cable Identification Machine (DCIM) based on support vector classification is constructed to determine the damaged cable and a Damage Severity Identification Machine (DSIM) based on support vector regression is built to estimate the damage severity. A field cable-stayed bridge with a long-term monitoring system is used to verify the proposed method. The three-dimensional Finite Element Model (FEM) of the cable-stayed bridge is established using ANSYS, and the model is validated using the field testing results, such as the mode shape, natural frequencies and its bending strain responses of the bridge under a moving vehicle. Then the validated FEM is used to simulate the bending strain responses of the longitude deck near the cable anchors when the vehicle is passing over the bridge. Different damage scenarios are simulated for each cable with various severities. Based on damage indexes vector, the training datasets and testing datasets are acquired, including single damaged cable scenarios and double damaged cable scenarios. Eventually, DCIM is trained using Support Vector Classification Machine and DSIM is trained using Support Vector Regression Machine. The testing datasets are input in DCIM and DSIM to check their accuracy and generalization capability. Different noise levels including 5%, 10%, and 20% are considered to study their anti-noise capability. The results show that DCIM and DSIM both have good generalization capability and anti-noise capability.

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http://hdl.handle.net/10453/156759