Enhanced drive-by bridge modal identification via dual Kalman filter and singular spectrum analysis

Li, J; Zhu, X; Guo, J

Enhanced drive-by bridge modal identification via dual Kalman filter and singular spectrum analysis

Li, J Zhu, X

Guo, J

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Publisher:: JOHN WILEY & SONS LTD
Publication Type:: Journal Article
Citation:: Structural Control and Health Monitoring, 2022, 29, (5)
Issue Date:: 2022-05-01

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Field	Value	Language
dc.contributor.author	Li, J
dc.contributor.author	Zhu, X https://orcid.org/0000-0001-5083-9320
dc.contributor.author	Guo, J
dc.date.accessioned	2022-05-09T06:11:16Z
dc.date.available	2022-05-09T06:11:16Z
dc.date.issued	2022-05-01
dc.identifier.citation	Structural Control and Health Monitoring, 2022, 29, (5)
dc.identifier.issn	1545-2255
dc.identifier.issn	1545-2263
dc.identifier.uri	http://hdl.handle.net/10453/157161
dc.description.abstract	The drive-by bridge health monitoring is to assess the bridge condition using the acceleration responses measured on the body or axle of instrumented vehicles. The vehicle responses are greatly affected by the road surface roughness that makes the bridge dynamic information blurred. Instead of direct using vehicle responses for the bridge monitoring, the dynamic response of contact point (CP) between the vehicle and bridge is further explored to enhance the drive-by bridge modal identification. A novel three-step framework is proposed to extract the components related to the bridge vibration from vehicle responses. The first step is to identify the input forces of two successive vehicles by solving the combined state-input estimation problem using dual Kalman filter. The CP responses of two contact points are calculated using the input forces and vehicle parameters. In the second step, the subtraction technique is applied to the identified CP responses of the two instrumented vehicles and the effect of the road surface roughness can be significantly reduced. Finally, an automatic singular spectrum analysis technique (auto SSA) is incorporated to decompose the response residual. Then the mono-component modes related to the bridge response are extracted from the response residual for drive-by bridge modal identification and/or the nonstationary characteristic identification of vehicle–bridge interaction (VBI) system. Results of numerical and experimental study demonstrate that the method can significantly suppress the vehicle response component and reduce the effect of road surface roughness to enhance the bridge modal identification.
dc.language	English
dc.publisher	JOHN WILEY & SONS LTD
dc.relation.ispartof	Structural Control and Health Monitoring
dc.relation.isbasedon	10.1002/stc.2927
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0905 Civil Engineering
dc.subject.classification	Civil Engineering
dc.title	Enhanced drive-by bridge modal identification via dual Kalman filter and singular spectrum analysis
dc.type	Journal Article
utslib.citation.volume	29
utslib.for	0905 Civil Engineering
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	*
utslib.copyright.embargo	2023-01-26T00:00:00+1000Z
dc.date.updated	2022-05-09T06:11:13Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	29
utslib.citation.issue	5

Abstract:

The drive-by bridge health monitoring is to assess the bridge condition using the acceleration responses measured on the body or axle of instrumented vehicles. The vehicle responses are greatly affected by the road surface roughness that makes the bridge dynamic information blurred. Instead of direct using vehicle responses for the bridge monitoring, the dynamic response of contact point (CP) between the vehicle and bridge is further explored to enhance the drive-by bridge modal identification. A novel three-step framework is proposed to extract the components related to the bridge vibration from vehicle responses. The first step is to identify the input forces of two successive vehicles by solving the combined state-input estimation problem using dual Kalman filter. The CP responses of two contact points are calculated using the input forces and vehicle parameters. In the second step, the subtraction technique is applied to the identified CP responses of the two instrumented vehicles and the effect of the road surface roughness can be significantly reduced. Finally, an automatic singular spectrum analysis technique (auto SSA) is incorporated to decompose the response residual. Then the mono-component modes related to the bridge response are extracted from the response residual for drive-by bridge modal identification and/or the nonstationary characteristic identification of vehicle–bridge interaction (VBI) system. Results of numerical and experimental study demonstrate that the method can significantly suppress the vehicle response component and reduce the effect of road surface roughness to enhance the bridge modal identification.

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http://hdl.handle.net/10453/157161