{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/79993"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/79993","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Structural Health Monitoring Using Unsupervised Learning Methods with a View on Post-Earthquake Damage Detection","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Eltouny, Kareem; 0000-0003-3918-2297"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Liang, Xiao","Civil, Structural and Environmental Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-07-30T15:11:41Z","date_published":"2019-07-30T15:11:41Z","updated_at":"2026-07-27T19:05:21Z","subjects":["civil engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/79993","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Liang, Xiao","Civil, Structural and Environmental Engineering"]},{"key":"dc:creator","label":"Author","values":["Eltouny, Kareem; 0000-0003-3918-2297"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-07-30T15:11:41Z","2019","2019-05-16 18:59:09"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["civil engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/79993"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","In a world of aging infrastructure, structural health monitoring (SHM) emerged as a major step towards resilient and sustainable societies. As we live in the information age, the advancements in machine learning and sensor technology have made SHM a more attractive damage detection method than the traditional non-destructive testing methods. Data-driven SHM requires a statistical learning model which could be supervised or unsupervised. Unsupervised learning only requires data from the undamaged structure during the training process. However, there is a lack of robust nonparametric density estimation to be used for the training model. In this thesis, a new unsupervised learning approach, multivariate Kernel Density Maximum Entropy method, is introduced in addition to examining two other approaches based on outlier analysis and kernel density estimation. Concepts and algorithms will be provided for each approach. Additionally, four types of damage-sensitive features are selected to be examined and compared in terms of provided advantages and disadvantages for each feature type. Two case studies, a numerical three-story three-dimensional reinforced concrete frame and a shake-table test of a three-story reinforced concrete frame with masonry infill, are investigated by applying the previously described unsupervised learning methods. In each case study, comparisons are conducted between results obtained by using different damage-sensitive features or different unsupervised learning approach. Conclusions and recommendations for future studies are provided at the end of this thesis."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Structural Health Monitoring Using Unsupervised Learning Methods with a View on Post-Earthquake Damage Detection"]}]}],"canonical_facts":{"dc:contributor":["Liang, Xiao","Civil, Structural and Environmental Engineering"],"dc:creator":["Eltouny, Kareem; 0000-0003-3918-2297"],"dc:date":["2019-07-30T15:11:41Z","2019","2019-05-16 18:59:09"],"dc:description":["M.S.","In a world of aging infrastructure, structural health monitoring (SHM) emerged as a major step towards resilient and sustainable societies. As we live in the information age, the advancements in machine learning and sensor technology have made SHM a more attractive damage detection method than the traditional non-destructive testing methods. Data-driven SHM requires a statistical learning model which could be supervised or unsupervised. Unsupervised learning only requires data from the undamaged structure during the training process. However, there is a lack of robust nonparametric density estimation to be used for the training model. In this thesis, a new unsupervised learning approach, multivariate Kernel Density Maximum Entropy method, is introduced in addition to examining two other approaches based on outlier analysis and kernel density estimation. Concepts and algorithms will be provided for each approach. Additionally, four types of damage-sensitive features are selected to be examined and compared in terms of provided advantages and disadvantages for each feature type. Two case studies, a numerical three-story three-dimensional reinforced concrete frame and a shake-table test of a three-story reinforced concrete frame with masonry infill, are investigated by applying the previously described unsupervised learning methods. In each case study, comparisons are conducted between results obtained by using different damage-sensitive features or different unsupervised learning approach. Conclusions and recommendations for future studies are provided at the end of this thesis."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/79993"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["civil engineering"],"dc:title":["Structural Health Monitoring Using Unsupervised Learning Methods with a View on Post-Earthquake Damage Detection"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:21Z"}