{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97467"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97467","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Optimal resource allocation to maximize model accuracy","abstract":"In the modelling of civil engineering structures, the engineer should make several assumptions to obtain a reasonable prediction of the behavior of the structure. Experience in projects in the professional practice shows that model assumptions made mechanically, without getting into specific details, lead to inaccurate models and to expensive resources allocation that does not render the optimal results. An example within civil engineering is the modelling of a structure, where – typically - structural engineers model the structure above the foundation and geotechnical engineers model the foundation and the soil behavior. These two areas have different error tolerances, mainly because the mechanical uncertainty of manufactured materials as concrete and steel is smaller than the uncertainty of soil. If these differences are not addressed correctly, the optimization process to get more accurate results is done incorrectly. This kind of situation is also common to other interaction with seismic engineering or hydrological engineering, among others. To address the limitations, it is necessary to consider the problem as a system, rather than dealing with the local behavior of the different parts. This work proposes a procedure based on the importance of random and categorical variables to address the different sources of uncertainty. Considering individual component models as categorical variables, the procedure follows a multi-model uncertainty propagation approach. The developed procedure is applied to an example of a cantilever beam on clay foundation.","abstract_html":"In the modelling of civil engineering structures, the engineer should make several assumptions to obtain a reasonable prediction of the behavior of the structure. Experience in projects in the professional practice shows that model assumptions made mechanically, without getting into specific details, lead to inaccurate models and to expensive resources allocation that does not render the optimal results. An example within civil engineering is the modelling of a structure, where – typically - structural engineers model the structure above the foundation and geotechnical engineers model the foundation and the soil behavior. These two areas have different error tolerances, mainly because the mechanical uncertainty of manufactured materials as concrete and steel is smaller than the uncertainty of soil. If these differences are not addressed correctly, the optimization process to get more accurate results is done incorrectly. This kind of situation is also common to other interaction with seismic engineering or hydrological engineering, among others. To address the limitations, it is necessary to consider the problem as a system, rather than dealing with the local behavior of the different parts. This work proposes a procedure based on the importance of random and categorical variables to address the different sources of uncertainty. Considering individual component models as categorical variables, the procedure follows a multi-model uncertainty propagation approach. The developed procedure is applied to an example of a cantilever beam on clay foundation.","abstract_has_math":false,"creators":["Dankert, Guillermo Leo"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Gardoni, Paolo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:16:06Z","date_published":"2017-08-10T19:16:06Z","updated_at":"2026-07-22T22:24:34Z","subjects":["Model uncertainty","Structural engineering","Soil structure interaction","Multi-model uncertainty propagation"],"languages":["en"],"rights":["Copyright 2017 Guillermo Dankert"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97467","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gardoni, Paolo"]},{"key":"dc:creator","label":"Author","values":["Dankert, Guillermo Leo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:16:06Z","2017-04-25","2017-05"]},{"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":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Model uncertainty","Structural engineering","Soil structure interaction","Multi-model uncertainty propagation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Guillermo Dankert"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97467"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In the modelling of civil engineering structures, the engineer should make several assumptions to obtain a reasonable prediction of the behavior of the structure. Experience in projects in the professional practice shows that model assumptions made mechanically, without getting into specific details, lead to inaccurate models and to expensive resources allocation that does not render the optimal results. An example within civil engineering is the modelling of a structure, where – typically - structural engineers model the structure above the foundation and geotechnical engineers model the foundation and the soil behavior. These two areas have different error tolerances, mainly because the mechanical uncertainty of manufactured materials as concrete and steel is smaller than the uncertainty of soil. If these differences are not addressed correctly, the optimization process to get more accurate results is done incorrectly. This kind of situation is also common to other interaction with seismic engineering or hydrological engineering, among others. To address the limitations, it is necessary to consider the problem as a system, rather than dealing with the local behavior of the different parts. This work proposes a procedure based on the importance of random and categorical variables to address the different sources of uncertainty. Considering individual component models as categorical variables, the procedure follows a multi-model uncertainty propagation approach. The developed procedure is applied to an example of a cantilever beam on clay foundation.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Guillermo Dankert, accepted the attached license on 2017-04-25 at 10:48.","The student, Guillermo Dankert, submitted this Thesis for approval on 2017-04-25 at 10:56.","This Thesis was approved for publication on 2017-04-25 at 15:47.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11035 on 2017-08-10 at 13:46:12","Made available in DSpace on 2017-08-10T19:16:06Z (GMT). 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An example within civil engineering is the modelling of a structure, where – typically - structural engineers model the structure above the foundation and geotechnical engineers model the foundation and the soil behavior. These two areas have different error tolerances, mainly because the mechanical uncertainty of manufactured materials as concrete and steel is smaller than the uncertainty of soil. If these differences are not addressed correctly, the optimization process to get more accurate results is done incorrectly. This kind of situation is also common to other interaction with seismic engineering or hydrological engineering, among others. To address the limitations, it is necessary to consider the problem as a system, rather than dealing with the local behavior of the different parts. This work proposes a procedure based on the importance of random and categorical variables to address the different sources of uncertainty. Considering individual component models as categorical variables, the procedure follows a multi-model uncertainty propagation approach. The developed procedure is applied to an example of a cantilever beam on clay foundation.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Guillermo Dankert, accepted the attached license on 2017-04-25 at 10:48.","The student, Guillermo Dankert, submitted this Thesis for approval on 2017-04-25 at 10:56.","This Thesis was approved for publication on 2017-04-25 at 15:47.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11035 on 2017-08-10 at 13:46:12","Made available in DSpace on 2017-08-10T19:16:06Z (GMT). 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