A hybrid method for vibration-based bridge damage detection

Damage detection algorithms employing the conventional acceleration measurements and the associated modal features may underperform due to the limited number of sensors used in the monitoring and the smoothing effect of spline functions used to increase the spatial resolution. The effectiveness of s...

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Detalles Bibliográficos
Autores: Gönen, Semih|||0000-0002-9588-4552, Erduran, Emrah
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/404211
Acceso en línea:https://hdl.handle.net/2117/404211
https://dx.doi.org/10.3390/rs14236054
Access Level:acceso abierto
Palabra clave:Structural health monitoring
Bridges -- Maintenance and repair
Dage detection
Vibration-based
Computer vision
Curvature
Strain energy
Modal flexibility
Monitorització de salut estructural
Ponts -- Manteniment i reparació
Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures
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spelling A hybrid method for vibration-based bridge damage detectionGönen, Semih|||0000-0002-9588-4552Erduran, EmrahStructural health monitoringBridges -- Maintenance and repairDage detectionVibration-basedStructural health monitoringComputer visionCurvatureStrain energyModal flexibilityMonitorització de salut estructuralPonts -- Manteniment i reparacióÀrees temàtiques de la UPC::Enginyeria civil::Materials i estructuresDamage detection algorithms employing the conventional acceleration measurements and the associated modal features may underperform due to the limited number of sensors used in the monitoring and the smoothing effect of spline functions used to increase the spatial resolution. The effectiveness of such algorithms could be increased if a more accurate estimate of mode shapes were provided. This study presents a hybrid structural health monitoring method for vibration-based damage detection of bridge-type structures. The proposed method is based on the fusion of data from conventional accelerometers and computer vision-based measurements. The most commonly used mode shape-based damage measures, namely, the mode shape curvature method, the modal strain energy method, and the modal flexibility method, are used for damage detection. The accuracy of these parameters used together with the conventional sparse sensor setups and the proposed hybrid approach is investigated in numerical case studies, with damage scenarios simulated on a simply-supported bridge. The simulations involve measuring the acceleration response of the bridge to ambient vibrations and train crossings and then processing the data to determine the modal frequencies and mode shapes. The efficiency and accuracy of the proposed hybrid health monitoring methodology are demonstrated in case studies involving scenarios in which conventional acceleration measurements fail to detect and locate damage. The robustness of the proposed method against various levels of noise is shown as well. In the studies considered, damage as small as 10% decrease in flexural stiffness of the bridge and localized in less than 1% of the span-length of the bridge is reliably detected even with very high levels of measurement noise. Finally, a modified modal flexibility damage parameter is derived and used to alleviate the shortcomings of the modal flexibility damage parameter.Peer ReviewedMultidisciplinary Digital Publishing Institute (MDPI)20222022-11-2920242024-03-12journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/404211https://dx.doi.org/10.3390/rs14236054reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4042112026-05-27T15:37:01Z
dc.title.none.fl_str_mv A hybrid method for vibration-based bridge damage detection
title A hybrid method for vibration-based bridge damage detection
spellingShingle A hybrid method for vibration-based bridge damage detection
Gönen, Semih|||0000-0002-9588-4552
Structural health monitoring
Bridges -- Maintenance and repair
Dage detection
Vibration-based
Structural health monitoring
Computer vision
Curvature
Strain energy
Modal flexibility
Monitorització de salut estructural
Ponts -- Manteniment i reparació
Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures
title_short A hybrid method for vibration-based bridge damage detection
title_full A hybrid method for vibration-based bridge damage detection
title_fullStr A hybrid method for vibration-based bridge damage detection
title_full_unstemmed A hybrid method for vibration-based bridge damage detection
title_sort A hybrid method for vibration-based bridge damage detection
dc.creator.none.fl_str_mv Gönen, Semih|||0000-0002-9588-4552
Erduran, Emrah
author Gönen, Semih|||0000-0002-9588-4552
author_facet Gönen, Semih|||0000-0002-9588-4552
Erduran, Emrah
author_role author
author2 Erduran, Emrah
author2_role author
dc.subject.none.fl_str_mv Structural health monitoring
Bridges -- Maintenance and repair
Dage detection
Vibration-based
Structural health monitoring
Computer vision
Curvature
Strain energy
Modal flexibility
Monitorització de salut estructural
Ponts -- Manteniment i reparació
Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures
topic Structural health monitoring
Bridges -- Maintenance and repair
Dage detection
Vibration-based
Structural health monitoring
Computer vision
Curvature
Strain energy
Modal flexibility
Monitorització de salut estructural
Ponts -- Manteniment i reparació
Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures
description Damage detection algorithms employing the conventional acceleration measurements and the associated modal features may underperform due to the limited number of sensors used in the monitoring and the smoothing effect of spline functions used to increase the spatial resolution. The effectiveness of such algorithms could be increased if a more accurate estimate of mode shapes were provided. This study presents a hybrid structural health monitoring method for vibration-based damage detection of bridge-type structures. The proposed method is based on the fusion of data from conventional accelerometers and computer vision-based measurements. The most commonly used mode shape-based damage measures, namely, the mode shape curvature method, the modal strain energy method, and the modal flexibility method, are used for damage detection. The accuracy of these parameters used together with the conventional sparse sensor setups and the proposed hybrid approach is investigated in numerical case studies, with damage scenarios simulated on a simply-supported bridge. The simulations involve measuring the acceleration response of the bridge to ambient vibrations and train crossings and then processing the data to determine the modal frequencies and mode shapes. The efficiency and accuracy of the proposed hybrid health monitoring methodology are demonstrated in case studies involving scenarios in which conventional acceleration measurements fail to detect and locate damage. The robustness of the proposed method against various levels of noise is shown as well. In the studies considered, damage as small as 10% decrease in flexural stiffness of the bridge and localized in less than 1% of the span-length of the bridge is reliably detected even with very high levels of measurement noise. Finally, a modified modal flexibility damage parameter is derived and used to alleviate the shortcomings of the modal flexibility damage parameter.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-11-29
2024
2024-03-12
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/404211
https://dx.doi.org/10.3390/rs14236054
url https://hdl.handle.net/2117/404211
https://dx.doi.org/10.3390/rs14236054
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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