{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/49631"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/49631","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Optimizing the sustainability of single-family housing units","abstract":"The sustainability of housing units can be improved by optimizing their social, environmental, and economic performances. The integration of green building equipment and systems such as geothermal heat pumps and water-efficient faucets often improves the social and environmental performances of housing units; however they can increase their initial cost and life cycle cost. Therefore, decision-makers need to carefully analyze and optimize the potential tradeoffs between the social, environmental, and economic performances of housing units. The main goal of this study is to develop novel multi-objective models for optimizing the sustainability of single-family housing units that represent 66% of the residential housing inventory in the US. To accomplish this goal, the research objectives of this study are to develop (1) an innovative housing social impact model that is capable of generating and analyzing optimal tradeoffs between the social quality of life for housing residents and the life cycle cost of housing; (2) a novel housing environmental performance model for maximizing the environmental performance of housing units while minimizing their initial cost; (3) a multi-objective optimization model that provides the capability of generating optimal tradeoffs among the three housing sustainability objectives of social quality-of-life, environmental performance, and life cycle cost; and (4) a scalable and expandable parallel computing framework that provides the capability of reducing the computational time of optimizing housing sustainability decisions and transforming this optimization problem from an intractable problem to a feasible and practical one. The performances of these developed models and framework were analyzed and refined using case studies of single-family housing units. The results of these performance evaluations illustrated that the developed optimization models were capable of generating a wide range of optimal solutions, where each identifies an optimal configuration of design and construction decisions that provides an optimal tradeoff among the three housing sustainability objectives. These novel research models and framework are expected to enhance the current practice of housing design and construction and contribute to maximizing the sustainability of single-family housing units.","abstract_html":"The sustainability of housing units can be improved by optimizing their social, environmental, and economic performances. The integration of green building equipment and systems such as geothermal heat pumps and water-efficient faucets often improves the social and environmental performances of housing units; however they can increase their initial cost and life cycle cost. Therefore, decision-makers need to carefully analyze and optimize the potential tradeoffs between the social, environmental, and economic performances of housing units. The main goal of this study is to develop novel multi-objective models for optimizing the sustainability of single-family housing units that represent 66% of the residential housing inventory in the US. To accomplish this goal, the research objectives of this study are to develop (1) an innovative housing social impact model that is capable of generating and analyzing optimal tradeoffs between the social quality of life for housing residents and the life cycle cost of housing; (2) a novel housing environmental performance model for maximizing the environmental performance of housing units while minimizing their initial cost; (3) a multi-objective optimization model that provides the capability of generating optimal tradeoffs among the three housing sustainability objectives of social quality-of-life, environmental performance, and life cycle cost; and (4) a scalable and expandable parallel computing framework that provides the capability of reducing the computational time of optimizing housing sustainability decisions and transforming this optimization problem from an intractable problem to a feasible and practical one. The performances of these developed models and framework were analyzed and refined using case studies of single-family housing units. The results of these performance evaluations illustrated that the developed optimization models were capable of generating a wide range of optimal solutions, where each identifies an optimal configuration of design and construction decisions that provides an optimal tradeoff among the three housing sustainability objectives. These novel research models and framework are expected to enhance the current practice of housing design and construction and contribute to maximizing the sustainability of single-family housing units.","abstract_has_math":false,"creators":["Karatas, Aslihan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["El-Rayes, Khaled A.","Liu, Liang Y.","Tutumluer, Erol","Golparvar-Fard, Mani"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-05-30T16:53:16Z","date_published":"2014-05-30T16:53:16Z","updated_at":"2026-07-22T22:25:38Z","subjects":["sustainable buildings","housing units","social quality-of-life","occupants comfort","thermal comfort","indoor lighting quality","Indoor Air Quality","neighborhood quality","environmental performance","carbon footprint","water consumption","life-cycle cost","parallel computing","optimization","genetic algorithms","residential buildings","sustainable construction","distributed processing","parallel genetic algorithms"],"languages":["en"],"rights":["Copyright 2014 Aslihan Karatas"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/49631","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["El-Rayes, Khaled A.","Liu, Liang Y.","Tutumluer, Erol","Golparvar-Fard, Mani"]},{"key":"dc:creator","label":"Author","values":["Karatas, Aslihan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-05-30T16:53:16Z","2014-05","10000-01-01"]},{"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":["sustainable buildings","housing units","social quality-of-life","occupants comfort","thermal comfort","indoor lighting quality","Indoor Air Quality","neighborhood quality","environmental performance","carbon footprint","water consumption","life-cycle cost","parallel computing","optimization","genetic algorithms","residential buildings","sustainable construction","distributed processing","parallel genetic algorithms"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2014 Aslihan Karatas"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/49631"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The sustainability of housing units can be improved by optimizing their social, environmental, and economic performances. 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To accomplish this goal, the research objectives of this study are to develop (1) an innovative housing social impact model that is capable of generating and analyzing optimal tradeoffs between the social quality of life for housing residents and the life cycle cost of housing; (2) a novel housing environmental performance model for maximizing the environmental performance of housing units while minimizing their initial cost; (3) a multi-objective optimization model that provides the capability of generating optimal tradeoffs among the three housing sustainability objectives of social quality-of-life, environmental performance, and life cycle cost; and (4) a scalable and expandable parallel computing framework that provides the capability of reducing the computational time of optimizing housing sustainability decisions and transforming this optimization problem from an intractable problem to a feasible and practical one. The performances of these developed models and framework were analyzed and refined using case studies of single-family housing units. The results of these performance evaluations illustrated that the developed optimization models were capable of generating a wide range of optimal solutions, where each identifies an optimal configuration of design and construction decisions that provides an optimal tradeoff among the three housing sustainability objectives. 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The main goal of this study is to develop novel multi-objective models for optimizing the sustainability of single-family housing units that represent 66% of the residential housing inventory in the US. To accomplish this goal, the research objectives of this study are to develop (1) an innovative housing social impact model that is capable of generating and analyzing optimal tradeoffs between the social quality of life for housing residents and the life cycle cost of housing; (2) a novel housing environmental performance model for maximizing the environmental performance of housing units while minimizing their initial cost; (3) a multi-objective optimization model that provides the capability of generating optimal tradeoffs among the three housing sustainability objectives of social quality-of-life, environmental performance, and life cycle cost; and (4) a scalable and expandable parallel computing framework that provides the capability of reducing the computational time of optimizing housing sustainability decisions and transforming this optimization problem from an intractable problem to a feasible and practical one. The performances of these developed models and framework were analyzed and refined using case studies of single-family housing units. The results of these performance evaluations illustrated that the developed optimization models were capable of generating a wide range of optimal solutions, where each identifies an optimal configuration of design and construction decisions that provides an optimal tradeoff among the three housing sustainability objectives. 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