{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/30889"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/30889","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Towards Multiclass Damage Detection and Localization using Limited Vibration Measurements","abstract":"Traditional vibration-based damage detection methods provide structural health information based on their measured data (i.e., acceleration and displacement response). Over the last few decades, various model-based and time-frequency methods have shown great promises for damage identification and localization. However, the existing methods are unable to perform satisfactorily in many situations, including the presence of limited sensor measurements and training data, detection of minor and progressive damage, and identification of multiclass damage, creating constraints to make them free of user-intervention and implemented using the modern sensors. The main objective of this thesis is to develop algorithms capable of damage identification and localization using limited measurements that can address the limitation of the traditional methods while providing a minimal to no user-intervention damage identification process. The proposed research in this thesis involves casting damage detection problems as non-parametric and autonomous with the least user intervention. Progressive damage identification is presented using novel time-frequency methods, such as synchrosqueezing transform and multivariate empirical mode decomposition, showing improved sensitivity of identifying minor damage over traditional methods. A basis-free method, such as multivariate empirical mode decomposition, is employed for damage localization using limited sensors. The acquired vibration measurement is decomposed into its mono components, and a damage localization index based on modal energy is proposed to overcome the need for a large number of sensors. The limited measurement aspect of damage localization is explored by selecting fewer sensors, and it is shown that with limited measurements, the proposed method is as effective as a total number of measurements equals the number of degrees of freedom of the model. To create an autonomous damage identification framework, Artificial Intelligence-based methods are explored the first time for multiclass damage classification and localization. Due to the lack of availability of a large amount of data, the acquired vibration data is augmented using windowing of the data per damage class. A novel window-based one-dimensional convolutional neural network is explored to classify sequential time-series of vibration measurements with only one hidden layer. The robustness of the proposed method is further evaluated by a suite of parametric and sensitivity analysis. Improvement of this method is further accomplished by implementing a windowed Long Short-term Memory network capable of learning long-term dependencies of the sequential data. Finally, the proposed methods are validated using a suite of experimental and full-scale studies, including a high-rate dynamics experimental testbed, a stadia prototype experimental setup, the MIT green building, and the Z24 bridge.","abstract_html":"Traditional vibration-based damage detection methods provide structural health information based on their measured data (i.e., acceleration and displacement response). Over the last few decades, various model-based and time-frequency methods have shown great promises for damage identification and localization. However, the existing methods are unable to perform satisfactorily in many situations, including the presence of limited sensor measurements and training data, detection of minor and progressive damage, and identification of multiclass damage, creating constraints to make them free of user-intervention and implemented using the modern sensors. The main objective of this thesis is to develop algorithms capable of damage identification and localization using limited measurements that can address the limitation of the traditional methods while providing a minimal to no user-intervention damage identification process. The proposed research in this thesis involves casting damage detection problems as non-parametric and autonomous with the least user intervention. Progressive damage identification is presented using novel time-frequency methods, such as synchrosqueezing transform and multivariate empirical mode decomposition, showing improved sensitivity of identifying minor damage over traditional methods. A basis-free method, such as multivariate empirical mode decomposition, is employed for damage localization using limited sensors. The acquired vibration measurement is decomposed into its mono components, and a damage localization index based on modal energy is proposed to overcome the need for a large number of sensors. The limited measurement aspect of damage localization is explored by selecting fewer sensors, and it is shown that with limited measurements, the proposed method is as effective as a total number of measurements equals the number of degrees of freedom of the model. To create an autonomous damage identification framework, Artificial Intelligence-based methods are explored the first time for multiclass damage classification and localization. Due to the lack of availability of a large amount of data, the acquired vibration data is augmented using windowing of the data per damage class. A novel window-based one-dimensional convolutional neural network is explored to classify sequential time-series of vibration measurements with only one hidden layer. The robustness of the proposed method is further evaluated by a suite of parametric and sensitivity analysis. Improvement of this method is further accomplished by implementing a windowed Long Short-term Memory network capable of learning long-term dependencies of the sequential data. Finally, the proposed methods are validated using a suite of experimental and full-scale studies, including a high-rate dynamics experimental testbed, a stadia prototype experimental setup, the MIT green building, and the Z24 bridge.","abstract_has_math":false,"creators":["Sony, Sandeep"],"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, A"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-02-19","date_published":"2021-02-19","updated_at":"2026-07-27T21:56:07Z","subjects":["Deep Learning","Damage Detection","Vibration","SHM","Limited sensors"],"languages":["en_ca"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/30889","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Sadhu, A"]},{"key":"dc:creator","label":"Author","values":["Sony, Sandeep"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-10T18:46:56Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-10T18:46:56Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-02-19"]},{"key":"dc:publisher","label":"Institution","values":["The University of Western Ontario"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil and Environmental Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph D"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep Learning","Damage Detection","Vibration","SHM","Limited sensors"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_ca"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14721/30889"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Collaborative Specialization: Scientific Computing","The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."]},{"key":"dc:description.abstract","label":"Abstract","values":["Traditional vibration-based damage detection methods provide structural health information based on their measured data (i.e., acceleration and displacement response). Over the last few decades, various model-based and time-frequency methods have shown great promises for damage identification and localization. However, the existing methods are unable to perform satisfactorily in many situations, including the presence of limited sensor measurements and training data, detection of minor and progressive damage, and identification of multiclass damage, creating constraints to make them free of user-intervention and implemented using the modern sensors. The main objective of this thesis is to develop algorithms capable of damage identification and localization using limited measurements that can address the limitation of the traditional methods while providing a minimal to no user-intervention damage identification process. The proposed research in this thesis involves casting damage detection problems as non-parametric and autonomous with the least user intervention. Progressive damage identification is presented using novel time-frequency methods, such as synchrosqueezing transform and multivariate empirical mode decomposition, showing improved sensitivity of identifying minor damage over traditional methods. A basis-free method, such as multivariate empirical mode decomposition, is employed for damage localization using limited sensors. The acquired vibration measurement is decomposed into its mono components, and a damage localization index based on modal energy is proposed to overcome the need for a large number of sensors. The limited measurement aspect of damage localization is explored by selecting fewer sensors, and it is shown that with limited measurements, the proposed method is as effective as a total number of measurements equals the number of degrees of freedom of the model. To create an autonomous damage identification framework, Artificial Intelligence-based methods are explored the first time for multiclass damage classification and localization. Due to the lack of availability of a large amount of data, the acquired vibration data is augmented using windowing of the data per damage class. A novel window-based one-dimensional convolutional neural network is explored to classify sequential time-series of vibration measurements with only one hidden layer. The robustness of the proposed method is further evaluated by a suite of parametric and sensitivity analysis. Improvement of this method is further accomplished by implementing a windowed Long Short-term Memory network capable of learning long-term dependencies of the sequential data. Finally, the proposed methods are validated using a suite of experimental and full-scale studies, including a high-rate dynamics experimental testbed, a stadia prototype experimental setup, the MIT green building, and the Z24 bridge."]},{"key":"dc:title","label":"Title","values":["Towards Multiclass Damage Detection and Localization using Limited Vibration Measurements"]}]}],"canonical_facts":{"dc:contributor.advisor":["Sadhu, A"],"dc:creator":["Sony, Sandeep"],"dc:date.accessioned":["2025-07-10T18:46:56Z"],"dc:date.available":["2025-07-10T18:46:56Z"],"dc:date.issued":["2021-02-19"],"dc:description":["Collaborative Specialization: Scientific Computing","The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."],"dc:description.abstract":["Traditional vibration-based damage detection methods provide structural health information based on their measured data (i.e., acceleration and displacement response). Over the last few decades, various model-based and time-frequency methods have shown great promises for damage identification and localization. However, the existing methods are unable to perform satisfactorily in many situations, including the presence of limited sensor measurements and training data, detection of minor and progressive damage, and identification of multiclass damage, creating constraints to make them free of user-intervention and implemented using the modern sensors. The main objective of this thesis is to develop algorithms capable of damage identification and localization using limited measurements that can address the limitation of the traditional methods while providing a minimal to no user-intervention damage identification process. The proposed research in this thesis involves casting damage detection problems as non-parametric and autonomous with the least user intervention. Progressive damage identification is presented using novel time-frequency methods, such as synchrosqueezing transform and multivariate empirical mode decomposition, showing improved sensitivity of identifying minor damage over traditional methods. A basis-free method, such as multivariate empirical mode decomposition, is employed for damage localization using limited sensors. The acquired vibration measurement is decomposed into its mono components, and a damage localization index based on modal energy is proposed to overcome the need for a large number of sensors. The limited measurement aspect of damage localization is explored by selecting fewer sensors, and it is shown that with limited measurements, the proposed method is as effective as a total number of measurements equals the number of degrees of freedom of the model. To create an autonomous damage identification framework, Artificial Intelligence-based methods are explored the first time for multiclass damage classification and localization. Due to the lack of availability of a large amount of data, the acquired vibration data is augmented using windowing of the data per damage class. A novel window-based one-dimensional convolutional neural network is explored to classify sequential time-series of vibration measurements with only one hidden layer. The robustness of the proposed method is further evaluated by a suite of parametric and sensitivity analysis. Improvement of this method is further accomplished by implementing a windowed Long Short-term Memory network capable of learning long-term dependencies of the sequential data. Finally, the proposed methods are validated using a suite of experimental and full-scale studies, including a high-rate dynamics experimental testbed, a stadia prototype experimental setup, the MIT green building, and the Z24 bridge."],"dc:identifier.uri":["https://hdl.handle.net/20.500.14721/30889"],"dc:language.iso":["en_ca"],"dc:publisher":["The University of Western Ontario"],"dc:subject":["Deep Learning","Damage Detection","Vibration","SHM","Limited sensors"],"dc:title":["Towards Multiclass Damage Detection and Localization using Limited Vibration Measurements"],"dc:type":["thesis"],"thesis:degree_discipline":["Civil and Environmental Engineering"],"thesis:degree_name":["Ph D"]},"updated_at":"2026-07-27T21:56:07Z"}