{"id":"https://openalex.org/W4207005730","doi":"https://doi.org/10.3390/s22030858","title":"Data Loss Reconstruction Method for a Bridge Weigh-in-Motion System Using Generative Adversarial Networks","display_name":"Data Loss Reconstruction Method for a Bridge Weigh-in-Motion System Using Generative Adversarial Networks","publication_year":2022,"publication_date":"2022-01-23","ids":{"openalex":"https://openalex.org/W4207005730","doi":"https://doi.org/10.3390/s22030858","pmid":"https://pubmed.ncbi.nlm.nih.gov/35161604"},"language":"en","primary_location":{"id":"doi:10.3390/s22030858","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22030858","pdf_url":"https://www.mdpi.com/1424-8220/22/3/858/pdf?version=1643014661","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/3/858/pdf?version=1643014661","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109358945","display_name":"Yizhou Zhuang","orcid":null},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yizhou Zhuang","raw_affiliation_strings":["College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310014, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310014, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039806456","display_name":"Jiacheng Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiacheng Qin","raw_affiliation_strings":["College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310014, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310014, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101569485","display_name":"Bin Chen","orcid":"https://orcid.org/0000-0002-2791-0069"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Chen","raw_affiliation_strings":["Department of Civil Engineering, Zhejiang University City College, Hangzhou 310015, China","Yangtze Delta Institute of Urban Infrastructure, Hangzhou 310005, China"],"raw_orcid":"https://orcid.org/0000-0002-2791-0069","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Zhejiang University City College, Hangzhou 310015, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Yangtze Delta Institute of Urban Infrastructure, Hangzhou 310005, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001482421","display_name":"Chuan\u2010Zhi Dong","orcid":"https://orcid.org/0000-0001-6010-2859"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chuanzhi Dong","raw_affiliation_strings":["Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA"],"raw_orcid":"https://orcid.org/0000-0001-6010-2859","affiliations":[{"raw_affiliation_string":"Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009628640","display_name":"Chenbo Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I55712492","display_name":"Zhejiang University of Technology","ror":"https://ror.org/02djqfd08","country_code":"CN","type":"education","lineage":["https://openalex.org/I55712492"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenbo Xue","raw_affiliation_strings":["College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310014, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310014, China","institution_ids":["https://openalex.org/I55712492"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031686159","display_name":"Said M. Easa","orcid":"https://orcid.org/0000-0003-0754-138X"},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Said M. Easa","raw_affiliation_strings":["Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada"],"raw_orcid":"https://orcid.org/0000-0003-0754-138X","affiliations":[{"raw_affiliation_string":"Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada","institution_ids":["https://openalex.org/I530967"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5001482421"],"corresponding_institution_ids":["https://openalex.org/I106165777"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":1.8444,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.82988486,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"22","issue":"3","first_page":"858","last_page":"858"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9997000098228455,"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":0.9997000098228455,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9980999827384949,"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/T14304","display_name":"Transport Systems and Technology","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"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/discriminator","display_name":"Discriminator","score":0.7447655200958252},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.669219970703125},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6229430437088013},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.6173126101493835},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5407275557518005},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4689979553222656},{"id":"https://openalex.org/keywords/motion-capture","display_name":"Motion capture","score":0.45239678025245667},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.44411739706993103},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4421972632408142},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.42990192770957947},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4298044443130493},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.3845292925834656},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.37444883584976196},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.31949493288993835},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.08290520310401917},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08267799019813538}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7447655200958252},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.669219970703125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6229430437088013},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.6173126101493835},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5407275557518005},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4689979553222656},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.45239678025245667},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.44411739706993103},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4421972632408142},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.42990192770957947},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4298044443130493},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.3845292925834656},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37444883584976196},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31949493288993835},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.08290520310401917},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08267799019813538},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D009038","descriptor_name":"Motion","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009038","descriptor_name":"Motion","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009038","descriptor_name":"Motion","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":5,"locations":[{"id":"doi:10.3390/s22030858","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22030858","pdf_url":"https://www.mdpi.com/1424-8220/22/3/858/pdf?version=1643014661","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:35161604","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35161604","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:9ba57a42931a4b0cbccbe7473468ab71","is_oa":true,"landing_page_url":"https://doaj.org/article/9ba57a42931a4b0cbccbe7473468ab71","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 22, Iss 3, p 858 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/3/858/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22030858","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 22; Issue 3; Pages: 858","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:8839323","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8839323","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s22030858","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22030858","pdf_url":"https://www.mdpi.com/1424-8220/22/3/858/pdf?version=1643014661","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.550000011920929}],"awards":[{"id":"https://openalex.org/G229985962","display_name":null,"funder_award_id":"LY17E080022","funder_id":"https://openalex.org/F4320338464","funder_display_name":"Natural Science Foundation of Zhejiang Province"},{"id":"https://openalex.org/G8869908417","display_name":null,"funder_award_id":"51774177","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"},{"id":"https://openalex.org/F4320338464","display_name":"Natural Science Foundation of Zhejiang Province","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4207005730.pdf","grobid_xml":"https://content.openalex.org/works/W4207005730.grobid-xml"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W31913789","https://openalex.org/W66427752","https://openalex.org/W114517082","https://openalex.org/W615444344","https://openalex.org/W1517623310","https://openalex.org/W1836465849","https://openalex.org/W1966163611","https://openalex.org/W1977130709","https://openalex.org/W1983835418","https://openalex.org/W2017369146","https://openalex.org/W2032555075","https://openalex.org/W2063186106","https://openalex.org/W2063877550","https://openalex.org/W2064186732","https://openalex.org/W2123680943","https://openalex.org/W2142194919","https://openalex.org/W2146130798","https://openalex.org/W2170212699","https://openalex.org/W2473196973","https://openalex.org/W2552480641","https://openalex.org/W2800346298","https://openalex.org/W2881181195","https://openalex.org/W2886079509","https://openalex.org/W2940612809","https://openalex.org/W2942902180","https://openalex.org/W2946971698","https://openalex.org/W2963420272","https://openalex.org/W2968368194","https://openalex.org/W2996834926","https://openalex.org/W2997045238","https://openalex.org/W3001158989","https://openalex.org/W3003365835","https://openalex.org/W3004549124","https://openalex.org/W3008043442","https://openalex.org/W3031149341","https://openalex.org/W3033466713","https://openalex.org/W3040855861","https://openalex.org/W3044248863","https://openalex.org/W3092593798","https://openalex.org/W3114947343","https://openalex.org/W4210738954","https://openalex.org/W6638667902","https://openalex.org/W6646496024","https://openalex.org/W6682042988","https://openalex.org/W6685139801","https://openalex.org/W6697168296","https://openalex.org/W6713134421","https://openalex.org/W6778937593"],"related_works":["https://openalex.org/W4293320219","https://openalex.org/W2953246223","https://openalex.org/W4283584549","https://openalex.org/W2554314924","https://openalex.org/W4288256692","https://openalex.org/W2998859928","https://openalex.org/W4381885966","https://openalex.org/W2969399009","https://openalex.org/W4398186750","https://openalex.org/W3151498616"],"abstract_inverted_index":{"In":[0,151],"the":[1,9,32,36,40,43,65,69,81,86,90,96,103,109,113,116,120,127,130,138,142,152,166,174,180,185,189,194,200,203,210,219,232,240,249,266],"application":[2],"of":[3,35,42,129,173,188,202,224,239,269],"a":[4,262],"bridge":[5,50,70,270],"weigh-in-motion":[6],"(WIM)":[7],"system,":[8],"collected":[10,37,82,123],"data":[11,28,38,67,91,97,117,187,267],"may":[12],"be":[13,253],"temporarily":[14],"or":[15,22,56],"permanently":[16],"lost":[17,118],"due":[18],"to":[19,46,63,107,141,146,193],"sensor":[20],"failure":[21],"system":[23,45],"transmission":[24],"failure.":[25],"The":[26,73,134,206],"high":[27],"loss":[29,98,156,159],"rate":[30],"weakens":[31],"distribution":[33,171,237],"characteristics":[34,172,238],"and":[39,84,101,115,160,165,169,228,235],"ability":[41],"monitoring":[44,271],"conduct":[47],"assessments":[48],"on":[49],"condition.":[51],"A":[52],"deep":[53],"learning-based":[54],"model,":[55,196],"generative":[57],"adversarial":[58],"network":[59],"(GAN),":[60],"is":[61,105,214,246],"proposed":[62,74,204,250],"reconstruct":[64],"missing":[66,87],"in":[68,76,119,144,215,222,255],"WIM":[71],"systems.":[72,272],"GAN":[75,195],"this":[77,197],"study":[78],"can":[79,252,260],"model":[80,153],"dataset":[83,114,213,221,242],"predict":[85],"data.":[88],"Firstly,":[89],"from":[92,112],"stable":[93],"measurements":[94],"before":[95],"are":[99,122,163,176,243],"provided,":[100],"then":[102],"generator":[104,143],"trained":[106],"extract":[108],"retained":[110],"features":[111],"process":[121],"by":[124,179,183],"using":[125],"only":[126],"responses":[128],"remaining":[131],"functional":[132],"sensors.":[133],"discriminator":[135],"feeds":[136],"back":[137],"recognition":[139],"results":[140,207],"order":[145],"improve":[147],"its":[148],"reconstruction":[149,268],"accuracy.":[150],"training,":[154],"two":[155],"functions,":[157],"generation":[158],"confrontation":[161],"loss,":[162],"used,":[164],"general":[167],"outline":[168],"potential":[170,236],"signal":[175],"well":[177],"processed":[178],"model.":[181],"Finally,":[182],"applying":[184],"engineering":[186],"Hangzhou":[190],"Jiangdong":[191],"Bridge":[192],"paper":[198],"verifies":[199],"effectiveness":[201],"method.":[205],"show":[208],"that":[209,248],"final":[211],"reconstructed":[212],"good":[216],"agreement":[217],"with":[218],"actual":[220],"terms":[223],"total":[225],"vehicle":[226],"weight":[227],"axle":[229],"weight.":[230],"Furthermore,":[231],"approximate":[233],"contour":[234],"original":[241],"reproduced.":[244],"It":[245],"suggested":[247],"method":[251,264],"used":[254],"real-life":[256],"applications.":[257],"This":[258],"research":[259],"provide":[261],"promising":[263],"for":[265]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}