{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/80862"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/80862","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Model-Data Fusion for Probabilistic Analysis of Civil Infrastructures","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Parida, Siddharth Shiladitya"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Sett, Kallol","Civil, Structural and Environmental Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-10-29T16:47:39Z","date_published":"2019-10-29T16:47:39Z","updated_at":"2026-07-27T19:05:25Z","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/10477/80862","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sett, Kallol","Civil, Structural and Environmental Engineering"]},{"key":"dc:creator","label":"Author","values":["Parida, Siddharth Shiladitya"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-10-29T16:47:39Z","2019","2019-07-24 12:13:10"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"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/10477/80862"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","In recent years, with the advancement of mathematical machineries and availability of faster computers, model-data fusion approaches have gained traction in many fields of engineering for parameter estimation and prediction of the behavior of large scale systems. The model-data fusion approaches, which are constrained by both the physics of the system and measured system response, yield better results than purely model-driven approaches and purely data-driven approaches.This dissertation discusses assembly and validation of a stochastic model-data fusion methodology for probabilistic analyses of civil infrastructures. The methodology relies on a minimum variance framework for the model-data fusion and utilizes scalable algorithms to allow for computationally efficient analyses of large- scale, nonlinear problems. The methodology is illustrated and validated on two typical applications in civil engineering - one in the geotechnical engineering domain and the other in the structural engineering domain.The eotechnical engineering application deals with stochastic full waveform inversion of geophysical measurements in characterizing any geotechnical sites. A 60m × 60m geotechnical site in Garner Valley, CA is used as the validation testbed. The methodology analyzes sparse geophysical measurements, available for the site, and yields high-resolution, probabilistic, three-dimensional images of P- and S-wave velocities of the soil at the site upto a depth of 40m, considering uncertainty due to limited measurements as well as measurement noise.The structural engineering application deals with health assessment and seismic performance prediction of an instrumented civil infrastructure. The Meloland Road overpass - a heavily instrumented highway bridge in El Centro, CA which experienced three moderate earthquakes in the past - is utilized as the validation testbed. The methodology, first, estimates the true state of the bridge - in terms of parameters of a numerical model of the bridge - using its measured behavior during past earthquakes. It, then, utilizes the updated numerical model of the bridge to evaluate its performance during future earthquakes.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Model-Data Fusion for Probabilistic Analysis of Civil Infrastructures"]}]}],"canonical_facts":{"dc:contributor":["Sett, Kallol","Civil, Structural and Environmental Engineering"],"dc:creator":["Parida, Siddharth Shiladitya"],"dc:date":["2019-10-29T16:47:39Z","2019","2019-07-24 12:13:10"],"dc:description":["Ph.D.","In recent years, with the advancement of mathematical machineries and availability of faster computers, model-data fusion approaches have gained traction in many fields of engineering for parameter estimation and prediction of the behavior of large scale systems. The model-data fusion approaches, which are constrained by both the physics of the system and measured system response, yield better results than purely model-driven approaches and purely data-driven approaches.This dissertation discusses assembly and validation of a stochastic model-data fusion methodology for probabilistic analyses of civil infrastructures. The methodology relies on a minimum variance framework for the model-data fusion and utilizes scalable algorithms to allow for computationally efficient analyses of large- scale, nonlinear problems. The methodology is illustrated and validated on two typical applications in civil engineering - one in the geotechnical engineering domain and the other in the structural engineering domain.The eotechnical engineering application deals with stochastic full waveform inversion of geophysical measurements in characterizing any geotechnical sites. A 60m × 60m geotechnical site in Garner Valley, CA is used as the validation testbed. The methodology analyzes sparse geophysical measurements, available for the site, and yields high-resolution, probabilistic, three-dimensional images of P- and S-wave velocities of the soil at the site upto a depth of 40m, considering uncertainty due to limited measurements as well as measurement noise.The structural engineering application deals with health assessment and seismic performance prediction of an instrumented civil infrastructure. The Meloland Road overpass - a heavily instrumented highway bridge in El Centro, CA which experienced three moderate earthquakes in the past - is utilized as the validation testbed. The methodology, first, estimates the true state of the bridge - in terms of parameters of a numerical model of the bridge - using its measured behavior during past earthquakes. It, then, utilizes the updated numerical model of the bridge to evaluate its performance during future earthquakes.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/80862"],"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":["Model-Data Fusion for Probabilistic Analysis of Civil Infrastructures"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:25Z"}