{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/109201"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/109201","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Surrogate learning for scoured bridges for capacity prediction with climate-scenario deep uncertainties","abstract":"Waterway bridges are among the most vulnerable components of transportation infrastructure due to scour—the erosion of soil around foundations caused by flowing water—a risk expected to grow under climate-driven increases in flood intensity. Traditional fragility models often assume a fixed structural capacity, overlooking how scour alters soil–foundation–structure interaction (SFSI) and degrades performance.This study develops a computationally efficient surrogate-learning framework, trained on nonlinear pushover analyses, to predict yield-level transverse capacities—base shear, deck displacement, base moment, and column rotation—collectively expressed as a capacity tuple. To incorporate deep uncertainty arising from future IPCC climate-scenario trajectories, we employ a credal-set approach that yields upper and lower bounds on capacity predictions. The resulting method enables rapid, risk-informed evaluation of scour-critical bridges, supporting practical decision-making under uncertain future hydraulic hazards.","abstract_html":"Waterway bridges are among the most vulnerable components of transportation infrastructure due to scour—the erosion of soil around foundations caused by flowing water—a risk expected to grow under climate-driven increases in flood intensity. Traditional fragility models often assume a fixed structural capacity, overlooking how scour alters soil–foundation–structure interaction (SFSI) and degrades performance.This study develops a computationally efficient surrogate-learning framework, trained on nonlinear pushover analyses, to predict yield-level transverse capacities—base shear, deck displacement, base moment, and column rotation—collectively expressed as a capacity tuple. To incorporate deep uncertainty arising from future IPCC climate-scenario trajectories, we employ a credal-set approach that yields upper and lower bounds on capacity predictions. The resulting method enables rapid, risk-informed evaluation of scour-critical bridges, supporting practical decision-making under uncertain future hydraulic hazards.","abstract_has_math":false,"creators":["Qadir, Ilham"],"institution":"University of Missouri--Kansas City","degree_name":"M.S. (Master of Science)","degree_level":"Masters","degree_discipline":"Civil Engineering (UMKC)","degree_department":null,"school":null,"contributors":[],"advisors":["Chen, ZhiQiang"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-24T05:19:15Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/109201","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Chen, ZhiQiang"]},{"key":"dc:creator","label":"Author","values":["Qadir, Ilham"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-28T04:12:29Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-28T04:12:29Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering (UMKC)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S. 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Traditional fragility models often assume a fixed structural capacity, overlooking how scour alters soil–foundation–structure interaction (SFSI) and degrades performance.This study develops a computationally efficient surrogate-learning framework, trained on nonlinear pushover analyses, to predict yield-level transverse capacities—base shear, deck displacement, base moment, and column rotation—collectively expressed as a capacity tuple. To incorporate deep uncertainty arising from future IPCC climate-scenario trajectories, we employ a credal-set approach that yields upper and lower bounds on capacity predictions. The resulting method enables rapid, risk-informed evaluation of scour-critical bridges, supporting practical decision-making under uncertain future hydraulic hazards."]},{"key":"dc:title","label":"Title","values":["Surrogate learning for scoured bridges for capacity prediction with climate-scenario deep uncertainties"]}]}],"canonical_facts":{"dc:contributor.advisor":["Chen, ZhiQiang"],"dc:creator":["Qadir, Ilham"],"dc:date.accessioned":["2025-07-28T04:12:29Z"],"dc:date.available":["2025-07-28T04:12:29Z"],"dc:date.issued":["2025"],"dc:description":["Title from PDF of title page viewed July 28, 2025","Thesis advisor: ZhiQiang Chen","Vita","Includes bibliographical references (pages 54-62)","Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2025"],"dc:description.abstract":["Waterway bridges are among the most vulnerable components of transportation infrastructure due to scour—the erosion of soil around foundations caused by flowing water—a risk expected to grow under climate-driven increases in flood intensity. Traditional fragility models often assume a fixed structural capacity, overlooking how scour alters soil–foundation–structure interaction (SFSI) and degrades performance.This study develops a computationally efficient surrogate-learning framework, trained on nonlinear pushover analyses, to predict yield-level transverse capacities—base shear, deck displacement, base moment, and column rotation—collectively expressed as a capacity tuple. To incorporate deep uncertainty arising from future IPCC climate-scenario trajectories, we employ a credal-set approach that yields upper and lower bounds on capacity predictions. The resulting method enables rapid, risk-informed evaluation of scour-critical bridges, supporting practical decision-making under uncertain future hydraulic hazards."],"dc:identifier.uri":["https://hdl.handle.net/10355/109201"],"dc:title":["Surrogate learning for scoured bridges for capacity prediction with climate-scenario deep uncertainties"],"thesis:degree_discipline":["Civil Engineering (UMKC)"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.S. (Master of Science)"],"thesis:institution_name":["University of Missouri--Kansas City"]},"updated_at":"2026-07-24T05:19:15Z"}