{"id":"https://openalex.org/W4226242968","doi":"https://doi.org/10.1145/3529395","title":"Smart City Construction and Management by Digital Twins and BIM Big Data in COVID-19 Scenario","display_name":"Smart City Construction and Management by Digital Twins and BIM Big Data in COVID-19 Scenario","publication_year":2022,"publication_date":"2022-04-04","ids":{"openalex":"https://openalex.org/W4226242968","doi":"https://doi.org/10.1145/3529395"},"language":"en","primary_location":{"id":"doi:10.1145/3529395","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3529395","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3529395","source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3529395","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108044017","display_name":"Zhihan Lv","orcid":"https://orcid.org/0000-0003-2525-3074"},"institutions":[{"id":"https://openalex.org/I123387679","display_name":"Uppsala University","ror":"https://ror.org/048a87296","country_code":"SE","type":"education","lineage":["https://openalex.org/I123387679"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Zhihan Lv","raw_affiliation_strings":["Department of Game Design, Faculty of Arts, Uppsala University, Uppsala, Sweden"],"raw_orcid":"https://orcid.org/0000-0003-2525-3074","affiliations":[{"raw_affiliation_string":"Department of Game Design, Faculty of Arts, Uppsala University, Uppsala, Sweden","institution_ids":["https://openalex.org/I123387679"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100687770","display_name":"Dongliang Chen","orcid":"https://orcid.org/0000-0001-5619-1270"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dongliang Chen","raw_affiliation_strings":["College of Computer Science and Technology, Qingdao University, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Qingdao University, Qingdao, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023204809","display_name":"Haibin Lv","orcid":"https://orcid.org/0000-0003-1059-4765"},"institutions":[{"id":"https://openalex.org/I1331374319","display_name":"National Bureau of Statistics of China","ror":"https://ror.org/008zm7t63","country_code":"CN","type":"other","lineage":["https://openalex.org/I1331374319","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"government","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haibin Lv","raw_affiliation_strings":["North China Sea Offshore Engineering Survey Institute, Ministry of Natural Resources North Sea Bureau, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North China Sea Offshore Engineering Survey Institute, Ministry of Natural Resources North Sea Bureau, Qingdao, China","institution_ids":["https://openalex.org/I1331374319","https://openalex.org/I211433327"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":12.356,"has_fulltext":true,"cited_by_count":114,"citation_normalized_percentile":{"value":0.99500995,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"18","issue":"2s","first_page":"1","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9771000146865845,"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":0.9771000146865845,"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":0.958299994468689,"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/T10809","display_name":"Occupational Health and Safety Research","score":0.9243000149726868,"subfield":{"id":"https://openalex.org/subfields/3614","display_name":"Radiological and Ultrasound Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8557913899421692},{"id":"https://openalex.org/keywords/smart-city","display_name":"Smart city","score":0.5535470247268677},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5230820775032043},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5097762942314148},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.49411657452583313},{"id":"https://openalex.org/keywords/graphics-processing-unit","display_name":"Graphics processing unit","score":0.4825127124786377},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4463551938533783},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4436091482639313},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34087035059928894},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3379751443862915},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.13451999425888062}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8557913899421692},{"id":"https://openalex.org/C2777103469","wikidata":"https://www.wikidata.org/wiki/Q1231558","display_name":"Smart city","level":3,"score":0.5535470247268677},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5230820775032043},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5097762942314148},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.49411657452583313},{"id":"https://openalex.org/C2779851693","wikidata":"https://www.wikidata.org/wiki/Q183484","display_name":"Graphics processing unit","level":2,"score":0.4825127124786377},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4463551938533783},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4436091482639313},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34087035059928894},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3379751443862915},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.13451999425888062},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3529395","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3529395","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3529395","source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3529395","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3529395","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3529395","source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6899999976158142,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G5801954405","display_name":null,"funder_award_id":"61902203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4226242968.pdf","grobid_xml":"https://content.openalex.org/works/W4226242968.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1558296484","https://openalex.org/W2015432217","https://openalex.org/W2612651689","https://openalex.org/W2626415748","https://openalex.org/W2656280928","https://openalex.org/W2767448197","https://openalex.org/W2793287761","https://openalex.org/W2799126372","https://openalex.org/W2890098390","https://openalex.org/W2906168345","https://openalex.org/W2907797058","https://openalex.org/W2912850190","https://openalex.org/W2929635159","https://openalex.org/W2934647834","https://openalex.org/W2942629283","https://openalex.org/W2947324800","https://openalex.org/W2951847836","https://openalex.org/W2972623937","https://openalex.org/W2981832329","https://openalex.org/W2994200381","https://openalex.org/W2997072997","https://openalex.org/W2997604709","https://openalex.org/W3003285143","https://openalex.org/W3004455307","https://openalex.org/W3005118046","https://openalex.org/W3014834477","https://openalex.org/W3015781627","https://openalex.org/W3017174852","https://openalex.org/W3042091195","https://openalex.org/W3047414789","https://openalex.org/W3082412179","https://openalex.org/W3085431519","https://openalex.org/W3125280368","https://openalex.org/W3131122791","https://openalex.org/W3133358200","https://openalex.org/W3135882017","https://openalex.org/W3148871766","https://openalex.org/W3150590266","https://openalex.org/W3156264695","https://openalex.org/W3164267812","https://openalex.org/W6776644817"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4394895745","https://openalex.org/W4247566972","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W2910064364","https://openalex.org/W4200136508","https://openalex.org/W2915637974"],"abstract_inverted_index":{"With":[0,210],"the":[1,8,16,30,44,52,72,79,88,93,101,112,115,141,148,157,170,178,186,189,192,199,205,211,217,222,230,240,248,258],"rapid":[2],"development":[3],"of":[4,10,62,66,74,81,92,111,195,201,214,250],"information":[5,54,219],"technology":[6,27],"and":[7,18,33,77,104,109,126,143,188],"spread":[9],"Corona":[11],"Virus":[12],"Disease":[13],"2019":[14],"(COVID-19),":[15],"government":[17],"urban":[19],"managers":[20],"are":[21],"looking":[22],"for":[23],"ways":[24],"to":[25,28,50,139,176],"use":[26,272],"make":[29],"city":[31],"smarter":[32],"safer.":[34],"Intelligent":[35],"transportation":[36],"can":[37,238],"play":[38],"a":[39,122,165],"very":[40],"important":[41],"role":[42],"in":[43,119,275],"joint":[45],"prevention.":[46],"This":[47],"work":[48],"expects":[49],"explore":[51],"building":[53],"modeling":[55],"(BIM)":[56],"big":[57],"data":[58,82,117,124,242,274],"(BD)":[59],"processing":[60,261],"method":[61],"digital":[63,90],"twins":[64],"(DTs)":[65],"Smart":[67,75],"City,":[68],"thus":[69,228],"speeding":[70],"up":[71],"construction":[73],"City":[76],"improve":[78],"accuracy":[80,249],"processing.":[83],"During":[84],"construction,":[85],"DTs":[86],"build":[87],"same":[89],"copy":[91],"smart":[94,120,276],"city.":[95],"On":[96,185,245],"this":[97],"basis,":[98],"BIM":[99,259],"designs":[100],"building's":[102],"keel":[103],"structure,":[105],"optimizing":[106],"various":[107],"resources":[108],"configurations":[110],"building.":[113],"Regarding":[114],"fast":[116],"growth":[118],"cities,":[121],"complex":[123,144,273],"fusion":[125],"efficient":[127],"learning":[128,172],"algorithm,":[129],"namely":[130],"Multi-":[131],"Graphics":[132],"Processing":[133],"Unit":[134],"(GPU)":[135],",":[136],"is":[137,162,174,252],"proposed":[138],"process":[140],"multi-dimensional":[142],"BD":[145,260],"based":[146,263],"on":[147,264],"compositive":[149],"rough":[150],"set":[151],"model.":[152],"The":[153],"Bayesian":[154,166,180,265],"network":[155,167],"solves":[156],"multi-label":[158],"classification.":[159],"Each":[160],"label":[161,179],"regarded":[163],"as":[164,198],"node.":[168],"Then,":[169],"structural":[171],"approach":[173],"adopted":[175],"learn":[177],"network's":[181],"structure":[182],"from":[183],"data.":[184],"P53-old":[187],"P53-new":[190],"datasets,":[191],"running":[193],"time":[194],"Multi-GPU":[196],"decreases":[197],"number":[200],"GPUs":[202],"increases,":[203],"approaching":[204],"ideal":[206],"linear":[207],"speedup":[208],"ratio.":[209],"continuous":[212],"increase":[213],"K":[215,234],"value,":[216],"deterministic":[218],"input":[220],"into":[221],"tag":[223],"BN":[224],"will":[225],"be":[226],"reduced,":[227],"reducing":[229],"classification":[231],"accuracy.":[232],"When":[233],"=":[235],"3,":[236],"MLBN":[237,251],"provide":[239],"best":[241],"analysis":[243],"performance.":[244],"genbase":[246],"dataset,":[247],"0.982":[253],"\u00b1":[254],"0.013.":[255],"Through":[256],"experiments,":[257],"algorithm":[262],"Network":[266],"Structural":[267],"Learning":[268],"(BNSL)":[269],"helps":[270],"decision-makers":[271],"cities":[277],"efficiently.":[278]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":24},{"year":2023,"cited_by_count":36},{"year":2022,"cited_by_count":33}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
