{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/83518"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/83518","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Study of Condition Monitoring of Bridges Using Genetic Algorithms","abstract":"A new methodology is developed to monitor the structural condition through non-destructive evaluation techniques using genetic algorithms. System identification techniques are used to establish the mathematical model of the real structural system through parametric methods. The problem of structural condition monitoring is formulated as an optimization problem. The remotely sensed static measurements, such as displacements and strains, under normal service loads are used to estimate the state of the structure. Genetic algorithm (GA), one of the computational intelligence methods, employs a highly parallel, massive, and adaptive computational environment to accomplish the mathematically difficult tasks in the structural condition monitoring. The feasibility of the proposed methodology is verified by the application to the detection and identification of structural damage. The location and magnitude of the damage in the element level defined as the stiffness change in structural members; and the location and size of the damage in the detailed level defined as the fracture damage in beam structures are successfully determined.","abstract_html":"A new methodology is developed to monitor the structural condition through non-destructive evaluation techniques using genetic algorithms. System identification techniques are used to establish the mathematical model of the real structural system through parametric methods. The problem of structural condition monitoring is formulated as an optimization problem. The remotely sensed static measurements, such as displacements and strains, under normal service loads are used to estimate the state of the structure. Genetic algorithm (GA), one of the computational intelligence methods, employs a highly parallel, massive, and adaptive computational environment to accomplish the mathematically difficult tasks in the structural condition monitoring. The feasibility of the proposed methodology is verified by the application to the detection and identification of structural damage. The location and magnitude of the damage in the element level defined as the stiffness change in structural members; and the location and size of the damage in the detailed level defined as the fracture damage in beam structures are successfully determined.","abstract_has_math":false,"creators":["Chou, Jung-Huai"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Ghaboussi, Jamshid"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T21:05:23Z","date_published":"2015-09-25T21:05:23Z","updated_at":"2026-07-22T22:26:21Z","subjects":["Engineering, Civil"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI9989963"],"render_values":[{"text":"(MiAaPQ)AAI9989963","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/83518","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ghaboussi, Jamshid"]},{"key":"dc:creator","label":"Author","values":["Chou, Jung-Huai"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T21:05:23Z","10000-01-01","2000"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering, Civil"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/83518","(MiAaPQ)AAI9989963"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A new methodology is developed to monitor the structural condition through non-destructive evaluation techniques using genetic algorithms. 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System identification techniques are used to establish the mathematical model of the real structural system through parametric methods. The problem of structural condition monitoring is formulated as an optimization problem. The remotely sensed static measurements, such as displacements and strains, under normal service loads are used to estimate the state of the structure. Genetic algorithm (GA), one of the computational intelligence methods, employs a highly parallel, massive, and adaptive computational environment to accomplish the mathematically difficult tasks in the structural condition monitoring. The feasibility of the proposed methodology is verified by the application to the detection and identification of structural damage. 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