{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/39557"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/39557","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Vision-based robust structural dynamic monitoring and modal identification using stationary and drone-based imaging systems.","abstract":"Structural health monitoring (SHM) plays a critical role in assessing and managing the condition of the civil infrastructure and prevents sudden closures or catastrophic failure of the structures by timely detection of ongoing degradations. Vision-based SHM has emerged as a promising non-contact alternative to conventional SHM approaches for estimating structural response and modal parameters. However, vision-based system identification comes with several practical challenges due to low sub-pixel structural motion, hardware constraints, environmental noise, and platform-induced disturbances in mobile sensing platform systems. To address these challenges, this thesis proposes innovative non-contact, vision-based SHM methodologies for the extraction of dynamic characteristics and validates experimentally on laboratory and field conditions using both stationary and drone-mounted cameras. This thesis investigates phase-based motion magnification (PMM) and proposes a hybrid framework to extract dynamic parameters and to study the influence of low-frame-rate imaging on PMM in motion-magnified video. Subsequently, a decentralized vision-based modal identification technique is proposed by combining multi-camera fields of view to extract high-density spatiotemporal information, utilizing fiducial markers as inexpensive virtual sensors to recover three-dimensional structural vibrations and to estimate modal parameters for full-field vision analysis without deploying stereo camera systems. To address accessibility challenges associated with large civil infrastructure, a drone-based vision-driven framework is developed to extract absolute displacement responses and dynamic parameters via ego-motion compensation and homography-based mapping. The feasibility of drone-based modal identification is evaluated through laboratory and field experiments, highlighting practical limitations in the estimation of the dynamic measurements under low signal-to-noise ratio conditions. Finally, for dense displacement extraction and modal parameter estimation, a deep learning-based tracking framework is proposed for long-duration video sequences using a sliding-window and long-context weights strategy under limited GPU memory constraints. The proposed methodologies are validated through laboratory-scale studies and field deployments, demonstrating strong agreement between vision-based estimates and conventional contact sensors. Overall, this thesis establishes scalable, robust vision-based pipelines for non-contact structural vibration monitoring, advancing the practical implementation of camera-based SHM systems for real-world civil infrastructure.","abstract_html":"Structural health monitoring (SHM) plays a critical role in assessing and managing the condition of the civil infrastructure and prevents sudden closures or catastrophic failure of the structures by timely detection of ongoing degradations. Vision-based SHM has emerged as a promising non-contact alternative to conventional SHM approaches for estimating structural response and modal parameters. However, vision-based system identification comes with several practical challenges due to low sub-pixel structural motion, hardware constraints, environmental noise, and platform-induced disturbances in mobile sensing platform systems. To address these challenges, this thesis proposes innovative non-contact, vision-based SHM methodologies for the extraction of dynamic characteristics and validates experimentally on laboratory and field conditions using both stationary and drone-mounted cameras. This thesis investigates phase-based motion magnification (PMM) and proposes a hybrid framework to extract dynamic parameters and to study the influence of low-frame-rate imaging on PMM in motion-magnified video. Subsequently, a decentralized vision-based modal identification technique is proposed by combining multi-camera fields of view to extract high-density spatiotemporal information, utilizing fiducial markers as inexpensive virtual sensors to recover three-dimensional structural vibrations and to estimate modal parameters for full-field vision analysis without deploying stereo camera systems. To address accessibility challenges associated with large civil infrastructure, a drone-based vision-driven framework is developed to extract absolute displacement responses and dynamic parameters via ego-motion compensation and homography-based mapping. The feasibility of drone-based modal identification is evaluated through laboratory and field experiments, highlighting practical limitations in the estimation of the dynamic measurements under low signal-to-noise ratio conditions. Finally, for dense displacement extraction and modal parameter estimation, a deep learning-based tracking framework is proposed for long-duration video sequences using a sliding-window and long-context weights strategy under limited GPU memory constraints. The proposed methodologies are validated through laboratory-scale studies and field deployments, demonstrating strong agreement between vision-based estimates and conventional contact sensors. Overall, this thesis establishes scalable, robust vision-based pipelines for non-contact structural vibration monitoring, advancing the practical implementation of camera-based SHM systems for real-world civil infrastructure.","abstract_has_math":false,"creators":["Mittal, Shivank"],"institution":"The University of Western Ontario","degree_name":"Ph D","degree_level":null,"degree_discipline":"Civil and Environmental Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Sadhu, Ayan"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-04-08","date_published":"2026-04-08","updated_at":"2026-07-27T21:56:11Z","subjects":["Structural health monitoring","computer vision","fiducial marker","unmanned aerial vehicle","vision-based dynamic measurement","non-contact sensing","modal identification","deep learning","phase-based motion magnification","blind source separation","correlation tracker","Cotracker3"],"languages":["en"],"rights":["Attribution-NonCommercial-NoDerivatives 4.0 International"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/39557","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Sadhu, Ayan"]},{"key":"dc:creator","label":"Author","values":["Mittal, Shivank"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-04-27T18:08:25Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-04-08"]},{"key":"dc:publisher","label":"Institution","values":["The University of Western Ontario"]},{"key":"dc:type","label":"Dc Type","values":["doctoral thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil and Environmental Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph D"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The University of Western Ontario"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Structural health monitoring","computer vision","fiducial marker","unmanned aerial vehicle","vision-based dynamic measurement","non-contact sensing","modal identification","deep learning","phase-based motion magnification","blind source separation","correlation tracker","Cotracker3"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Attribution-NonCommercial-NoDerivatives 4.0 International"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14721/39557"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Structural health monitoring (SHM) plays a critical role in assessing and managing the condition of the civil infrastructure and prevents sudden closures or catastrophic failure of the structures by timely detection of ongoing degradations. Vision-based SHM has emerged as a promising non-contact alternative to conventional SHM approaches for estimating structural response and modal parameters. However, vision-based system identification comes with several practical challenges due to low sub-pixel structural motion, hardware constraints, environmental noise, and platform-induced disturbances in mobile sensing platform systems. To address these challenges, this thesis proposes innovative non-contact, vision-based SHM methodologies for the extraction of dynamic characteristics and validates experimentally on laboratory and field conditions using both stationary and drone-mounted cameras. This thesis investigates phase-based motion magnification (PMM) and proposes a hybrid framework to extract dynamic parameters and to study the influence of low-frame-rate imaging on PMM in motion-magnified video. Subsequently, a decentralized vision-based modal identification technique is proposed by combining multi-camera fields of view to extract high-density spatiotemporal information, utilizing fiducial markers as inexpensive virtual sensors to recover three-dimensional structural vibrations and to estimate modal parameters for full-field vision analysis without deploying stereo camera systems. To address accessibility challenges associated with large civil infrastructure, a drone-based vision-driven framework is developed to extract absolute displacement responses and dynamic parameters via ego-motion compensation and homography-based mapping. The feasibility of drone-based modal identification is evaluated through laboratory and field experiments, highlighting practical limitations in the estimation of the dynamic measurements under low signal-to-noise ratio conditions. Finally, for dense displacement extraction and modal parameter estimation, a deep learning-based tracking framework is proposed for long-duration video sequences using a sliding-window and long-context weights strategy under limited GPU memory constraints. The proposed methodologies are validated through laboratory-scale studies and field deployments, demonstrating strong agreement between vision-based estimates and conventional contact sensors. Overall, this thesis establishes scalable, robust vision-based pipelines for non-contact structural vibration monitoring, advancing the practical implementation of camera-based SHM systems for real-world civil infrastructure."]},{"key":"dc:title","label":"Title","values":["Vision-based robust structural dynamic monitoring and modal identification using stationary and drone-based imaging systems."]}]}],"canonical_facts":{"dc:contributor.advisor":["Sadhu, Ayan"],"dc:creator":["Mittal, Shivank"],"dc:date.accessioned":["2026-04-27T18:08:25Z"],"dc:date.issued":["2026-04-08"],"dc:description.abstract":["Structural health monitoring (SHM) plays a critical role in assessing and managing the condition of the civil infrastructure and prevents sudden closures or catastrophic failure of the structures by timely detection of ongoing degradations. 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Subsequently, a decentralized vision-based modal identification technique is proposed by combining multi-camera fields of view to extract high-density spatiotemporal information, utilizing fiducial markers as inexpensive virtual sensors to recover three-dimensional structural vibrations and to estimate modal parameters for full-field vision analysis without deploying stereo camera systems. To address accessibility challenges associated with large civil infrastructure, a drone-based vision-driven framework is developed to extract absolute displacement responses and dynamic parameters via ego-motion compensation and homography-based mapping. The feasibility of drone-based modal identification is evaluated through laboratory and field experiments, highlighting practical limitations in the estimation of the dynamic measurements under low signal-to-noise ratio conditions. Finally, for dense displacement extraction and modal parameter estimation, a deep learning-based tracking framework is proposed for long-duration video sequences using a sliding-window and long-context weights strategy under limited GPU memory constraints. The proposed methodologies are validated through laboratory-scale studies and field deployments, demonstrating strong agreement between vision-based estimates and conventional contact sensors. Overall, this thesis establishes scalable, robust vision-based pipelines for non-contact structural vibration monitoring, advancing the practical implementation of camera-based SHM systems for real-world civil infrastructure."],"dc:identifier.uri":["https://hdl.handle.net/20.500.14721/39557"],"dc:language.iso":["en"],"dc:publisher":["The University of Western Ontario"],"dc:rights":["Attribution-NonCommercial-NoDerivatives 4.0 International"],"dc:subject":["Structural health monitoring","computer vision","fiducial marker","unmanned aerial vehicle","vision-based dynamic measurement","non-contact sensing","modal identification","deep learning","phase-based motion magnification","blind source separation","correlation tracker","Cotracker3"],"dc:title":["Vision-based robust structural dynamic monitoring and modal identification using stationary and drone-based imaging systems."],"dc:type":["doctoral thesis"],"thesis:degree_discipline":["Civil and Environmental Engineering"],"thesis:degree_name":["Ph D"],"thesis:institution_name":["The University of Western Ontario"]},"updated_at":"2026-07-27T21:56:11Z"}