{"id":"https://openalex.org/W2949240291","doi":"https://doi.org/10.1109/wsc40007.2019.9004934","title":"Enhanced Input Modeling for Construction Simulation Using Bayesian Deep Neural Networks","display_name":"Enhanced Input Modeling for Construction Simulation Using Bayesian Deep Neural Networks","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W2949240291","doi":"https://doi.org/10.1109/wsc40007.2019.9004934","mag":"2949240291"},"language":"en","primary_location":{"id":"doi:10.1109/wsc40007.2019.9004934","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc40007.2019.9004934","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1906.06421","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yitong Li","orcid":null},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yitong Li","raw_affiliation_strings":["Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":null,"display_name":"Wenying Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenying Ji","raw_affiliation_strings":["Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162714631"],"apc_list":null,"apc_paid":null,"fwci":1.4828,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.76889698,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11006","display_name":"BIM and Construction Integration","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6028000116348267},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5239999890327454},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4781000018119812},{"id":"https://openalex.org/keywords/input/output","display_name":"Input/output","score":0.4429999887943268},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.40450000762939453},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4043000042438507}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7146000266075134},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6161999702453613},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6028000116348267},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5239999890327454},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5097000002861023},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4781000018119812},{"id":"https://openalex.org/C196697905","wikidata":"https://www.wikidata.org/wiki/Q2042052","display_name":"Input/output","level":2,"score":0.4429999887943268},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43939998745918274},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.40450000762939453},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4043000042438507},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C108215451","wikidata":"https://www.wikidata.org/wiki/Q7263963","display_name":"Simulation modeling","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3237999975681305},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.2705000042915344}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/wsc40007.2019.9004934","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wsc40007.2019.9004934","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 Winter Simulation Conference (WSC)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1906.06421","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1906.06421","pdf_url":"https://arxiv.org/pdf/1906.06421","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1906.06421","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1906.06421","pdf_url":"https://arxiv.org/pdf/1906.06421","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1991555897","https://openalex.org/W2111959010","https://openalex.org/W2490033382","https://openalex.org/W2794150784","https://openalex.org/W2808239110","https://openalex.org/W2919115771","https://openalex.org/W4235840172","https://openalex.org/W4241166238","https://openalex.org/W6617145748","https://openalex.org/W6697383064","https://openalex.org/W6735443497","https://openalex.org/W6768565661","https://openalex.org/W6869538123","https://openalex.org/W6869563654"],"related_works":[],"abstract_inverted_index":{"This":[0,94],"paper":[1],"aims":[2],"to":[3,45,65],"propose":[4],"a":[5],"novel":[6],"deep":[7,42],"learning-integrated":[8],"framework":[9,21,39],"for":[10,35],"deriving":[11,82],"reliable":[12],"simulation":[13,102],"input":[14,36,54,79,84],"models":[15],"through":[16,103],"incorporating":[17,50,104],"multi-source":[18],"information.":[19],"The":[20,38],"sources":[22],"and":[23,69],"extracts":[24],"multisource":[25],"data":[26],"generated":[27],"from":[28],"construction":[29,92],"operations,":[30],"which":[31],"provides":[32],"rich":[33],"information":[34,52],"modeling.":[37,55],"implements":[40],"Bayesian":[41],"neural":[43],"networks":[44],"facilitate":[46],"the":[47,67,72,88],"purpose":[48],"of":[49,71],"richer":[51],"in":[53,91],"A":[56],"case":[57],"study":[58],"on":[59,99],"road":[60],"paving":[61],"operation":[62],"is":[63],"performed":[64],"test":[66],"feasibility":[68],"applicability":[70],"proposed":[73],"framework.":[74],"Overall,":[75],"this":[76],"research":[77,95],"enhances":[78],"modeling":[80],"by":[81],"detailed":[83],"models,":[85],"thereby,":[86],"augmenting":[87],"decision-making":[89],"processes":[90],"operations.":[93],"also":[96],"sheds":[97],"lights":[98],"prompting":[100],"data-driven":[101],"machine":[105],"learning":[106],"techniques.":[107]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2019-06-27T00:00:00"}
