{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/44294"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/44294","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Monitoring, modeling, and hybrid simulation—an integrated Bayesian-based approach to high-fidelity fragility analysis","abstract":"Fragility functions are one of the key technical ingredients in seismic risk assessment. The derivation of fragility functions has been extensively studied in the past; however, large uncertainties still exist, mainly due to limited collaboration between the interdependent components involved in the course of fragility estimation. This research aims to develop a systematic Bayesian-based framework to estimate high-fidelity fragility functions by integrating monitoring, modeling, and hybrid simulation, with the final goal of improving the accuracy of seismic risk assessment to support both pre- and post-disaster decision-making. In particular, this research addresses the following five aspects of the problem: (1) monitoring with wireless smart sensor networks to facilitate efficient and accurate pre- and post-disaster data collection, (2) new modeling techniques including innovative system identification strategies and model updating to enable accurate structural modeling, (3) hybrid simulation as an advanced numerical-experimental simulation tool to generate highly realistic and accurate response data for structures subject to earthquakes, (4) Bayesian-updating as a systematic way of incorporating hybrid simulation data to generate composite fragility functions with higher fidelity, and 5) the implementation of an integrated fragility analysis approach as a part of a seismic risk assessment framework. This research not only delivers an extensible and scalable framework for high-fidelity fragility analysis and reliable seismic risk assessment, but also provides advances in wireless smart sensor networks, system identification, and pseudo-dynamic testing in civil engineering applications.","abstract_html":"Fragility functions are one of the key technical ingredients in seismic risk assessment. The derivation of fragility functions has been extensively studied in the past; however, large uncertainties still exist, mainly due to limited collaboration between the interdependent components involved in the course of fragility estimation. This research aims to develop a systematic Bayesian-based framework to estimate high-fidelity fragility functions by integrating monitoring, modeling, and hybrid simulation, with the final goal of improving the accuracy of seismic risk assessment to support both pre- and post-disaster decision-making. In particular, this research addresses the following five aspects of the problem: (1) monitoring with wireless smart sensor networks to facilitate efficient and accurate pre- and post-disaster data collection, (2) new modeling techniques including innovative system identification strategies and model updating to enable accurate structural modeling, (3) hybrid simulation as an advanced numerical-experimental simulation tool to generate highly realistic and accurate response data for structures subject to earthquakes, (4) Bayesian-updating as a systematic way of incorporating hybrid simulation data to generate composite fragility functions with higher fidelity, and 5) the implementation of an integrated fragility analysis approach as a part of a seismic risk assessment framework. This research not only delivers an extensible and scalable framework for high-fidelity fragility analysis and reliable seismic risk assessment, but also provides advances in wireless smart sensor networks, system identification, and pseudo-dynamic testing in civil engineering applications.","abstract_has_math":false,"creators":["Li, Jian"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Spencer, Billie F., Jr.","Elnashai, Amr S.","Agha, Gul A.","Song, Junho"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-24T22:06:50Z","date_published":"2013-05-24T22:06:50Z","updated_at":"2026-07-22T22:25:34Z","subjects":["Fragility analysis","Structural Health Monitoring","Wireless Smart Sensor","Time Synchronization","Hybrid simulation","Multiple-Support Excitation","System identification","Output decoupling","Seismic risk assessment"],"languages":["en"],"rights":["Copyright 2013 Jian Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/44294","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Spencer, Billie F., Jr.","Elnashai, Amr S.","Agha, Gul A.","Song, Junho"]},{"key":"dc:creator","label":"Author","values":["Li, Jian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2013-05-24T22:06:50Z","2013-05"]},{"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":["Fragility analysis","Structural Health Monitoring","Wireless Smart Sensor","Time Synchronization","Hybrid simulation","Multiple-Support Excitation","System identification","Output decoupling","Seismic risk assessment"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Jian Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/44294"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Fragility functions are one of the key technical ingredients in seismic risk assessment. The derivation of fragility functions has been extensively studied in the past; however, large uncertainties still exist, mainly due to limited collaboration between the interdependent components involved in the course of fragility estimation. This research aims to develop a systematic Bayesian-based framework to estimate high-fidelity fragility functions by integrating monitoring, modeling, and hybrid simulation, with the final goal of improving the accuracy of seismic risk assessment to support both pre- and post-disaster decision-making. In particular, this research addresses the following five aspects of the problem: (1) monitoring with wireless smart sensor networks to facilitate efficient and accurate pre- and post-disaster data collection, (2) new modeling techniques including innovative system identification strategies and model updating to enable accurate structural modeling, (3) hybrid simulation as an advanced numerical-experimental simulation tool to generate highly realistic and accurate response data for structures subject to earthquakes, (4) Bayesian-updating as a systematic way of incorporating hybrid simulation data to generate composite fragility functions with higher fidelity, and 5) the implementation of an integrated fragility analysis approach as a part of a seismic risk assessment framework. This research not only delivers an extensible and scalable framework for high-fidelity fragility analysis and reliable seismic risk assessment, but also provides advances in wireless smart sensor networks, system identification, and pseudo-dynamic testing in civil engineering applications.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-04-08T21:40:33Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Li_Jian.docx: 15960005 bytes, checksum: 7a01bc8bf77796d728f64b9af3b35576 (MD5) Li_Jian.pdf: 18002545 bytes, checksum: 69ab493b377609053ade18f88a9923eb (MD5)","Made available in DSpace on 2013-05-24T22:06:50Z (GMT). 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This research aims to develop a systematic Bayesian-based framework to estimate high-fidelity fragility functions by integrating monitoring, modeling, and hybrid simulation, with the final goal of improving the accuracy of seismic risk assessment to support both pre- and post-disaster decision-making. In particular, this research addresses the following five aspects of the problem: (1) monitoring with wireless smart sensor networks to facilitate efficient and accurate pre- and post-disaster data collection, (2) new modeling techniques including innovative system identification strategies and model updating to enable accurate structural modeling, (3) hybrid simulation as an advanced numerical-experimental simulation tool to generate highly realistic and accurate response data for structures subject to earthquakes, (4) Bayesian-updating as a systematic way of incorporating hybrid simulation data to generate composite fragility functions with higher fidelity, and 5) the implementation of an integrated fragility analysis approach as a part of a seismic risk assessment framework. 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