{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/102507"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/102507","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Computational creativity applications in engineering","abstract":"We investigated and implemented two computational creativity applications in generating engineering processes and building material designs respectively. To synthesize engineering processes, we developed a full system that generates engaging Rube Goldberg Machine designs. We first describe the use of case-based reasoning (CBR) and an existing knowledge base to yield a combinatorial design space for experiments. We then apply automated planning techniques to generate experiment procedures. We further use functional modeling to represent the experiment devices and demonstrate how that representation enables the planner to generate a valid Rube Goldberg Machine. Finally, a semantic similarity metric is proposed to evaluate the quality of a generated chain of experiments. To discover concrete formulas as building materials with desired properties, we use a conditional variational autoencoder (CVAE), a type of semisupervised generative model. Our model is trained using open data from the UCI Machine Learning Repository joined with environmental impact data computed using a web-based tool. We demonstrate that the CVAE can design concrete formulas with lower emissions and natural resource usage while meeting design requirements. To ensure fair comparison between extant and generated formulas, we also train regression models to predict the environmental impacts and strength of discovered formulas. With these results, a construction engineer may create a formula that meets structural needs and best addresses local environmental concerns.","abstract_html":"We investigated and implemented two computational creativity applications in generating engineering processes and building material designs respectively. To synthesize engineering processes, we developed a full system that generates engaging Rube Goldberg Machine designs. We first describe the use of case-based reasoning (CBR) and an existing knowledge base to yield a combinatorial design space for experiments. We then apply automated planning techniques to generate experiment procedures. We further use functional modeling to represent the experiment devices and demonstrate how that representation enables the planner to generate a valid Rube Goldberg Machine. Finally, a semantic similarity metric is proposed to evaluate the quality of a generated chain of experiments. To discover concrete formulas as building materials with desired properties, we use a conditional variational autoencoder (CVAE), a type of semisupervised generative model. Our model is trained using open data from the UCI Machine Learning Repository joined with environmental impact data computed using a web-based tool. We demonstrate that the CVAE can design concrete formulas with lower emissions and natural resource usage while meeting design requirements. To ensure fair comparison between extant and generated formulas, we also train regression models to predict the environmental impacts and strength of discovered formulas. With these results, a construction engineer may create a formula that meets structural needs and best addresses local environmental concerns.","abstract_has_math":false,"creators":["Ge, Xiou"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Varshney, Lav R."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-02-06T19:36:42Z","date_published":"2019-02-06T19:36:42Z","updated_at":"2026-07-22T22:24:42Z","subjects":["Computational Creativity, Engineering"],"languages":["en"],"rights":["Copyright 2018 Xiou Ge"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/102507","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Varshney, Lav R."]},{"key":"dc:creator","label":"Author","values":["Ge, Xiou"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-02-06T19:36:42Z","2018-12-10","2018-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Computational Creativity, Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Xiou Ge"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/102507"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We investigated and implemented two computational creativity applications in generating engineering processes and building material designs respectively. To synthesize engineering processes, we developed a full system that generates engaging Rube Goldberg Machine designs. We first describe the use of case-based reasoning (CBR) and an existing knowledge base to yield a combinatorial design space for experiments. We then apply automated planning techniques to generate experiment procedures. We further use functional modeling to represent the experiment devices and demonstrate how that representation enables the planner to generate a valid Rube Goldberg Machine. Finally, a semantic similarity metric is proposed to evaluate the quality of a generated chain of experiments. To discover concrete formulas as building materials with desired properties, we use a conditional variational autoencoder (CVAE), a type of semisupervised generative model. Our model is trained using open data from the UCI Machine Learning Repository joined with environmental impact data computed using a web-based tool. We demonstrate that the CVAE can design concrete formulas with lower emissions and natural resource usage while meeting design requirements. To ensure fair comparison between extant and generated formulas, we also train regression models to predict the environmental impacts and strength of discovered formulas. With these results, a construction engineer may create a formula that meets structural needs and best addresses local environmental concerns.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Xiou Ge, accepted the attached license on 2018-12-10 at 14:20.","The student, Xiou Ge, submitted this Thesis for approval on 2018-12-10 at 14:30.","This Thesis was approved for publication on 2018-12-10 at 14:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13271 on 2019-02-05 at 11:16:00","Made available in DSpace on 2019-02-06T19:36:42Z (GMT). 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We then apply automated planning techniques to generate experiment procedures. We further use functional modeling to represent the experiment devices and demonstrate how that representation enables the planner to generate a valid Rube Goldberg Machine. Finally, a semantic similarity metric is proposed to evaluate the quality of a generated chain of experiments. To discover concrete formulas as building materials with desired properties, we use a conditional variational autoencoder (CVAE), a type of semisupervised generative model. Our model is trained using open data from the UCI Machine Learning Repository joined with environmental impact data computed using a web-based tool. We demonstrate that the CVAE can design concrete formulas with lower emissions and natural resource usage while meeting design requirements. To ensure fair comparison between extant and generated formulas, we also train regression models to predict the environmental impacts and strength of discovered formulas. With these results, a construction engineer may create a formula that meets structural needs and best addresses local environmental concerns.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Xiou Ge, accepted the attached license on 2018-12-10 at 14:20.","The student, Xiou Ge, submitted this Thesis for approval on 2018-12-10 at 14:30.","This Thesis was approved for publication on 2018-12-10 at 14:43.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13271 on 2019-02-05 at 11:16:00","Made available in DSpace on 2019-02-06T19:36:42Z (GMT). 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