{"id":"https://openalex.org/W3100090196","doi":"https://doi.org/10.1109/tase.2020.3034401","title":"Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning","display_name":"Data-Driven Structural Health Monitoring Using Feature Fusion and Hybrid Deep Learning","publication_year":2020,"publication_date":"2020-11-13","ids":{"openalex":"https://openalex.org/W3100090196","doi":"https://doi.org/10.1109/tase.2020.3034401","mag":"3100090196"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2020.3034401","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":"https://eprints.mdx.ac.uk/31283/1/FINAL_VERSION_IEEEE_Automation.PDF","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007116395","display_name":"Viet-Hung Dang","orcid":"https://orcid.org/0000-0003-3384-6427"},"institutions":[{"id":"https://openalex.org/I60488453","display_name":"Middlesex University","ror":"https://ror.org/01rv4p989","country_code":"GB","type":"education","lineage":["https://openalex.org/I60488453"]},{"id":"https://openalex.org/I257903683","display_name":"National University of Civil Engineering","ror":"https://ror.org/01351mb48","country_code":"VN","type":"education","lineage":["https://openalex.org/I257903683"]}],"countries":["GB","VN"],"is_corresponding":false,"raw_author_name":"Hung V. Dang","raw_affiliation_strings":["Faculty of Building and Industrial Construction, National University of Civil Engineering, Hanoi, Vietnam","London Digital Twin Research Centre, Faculty of Science and Technology, Middlesex University, London, U.K."],"affiliations":[{"raw_affiliation_string":"London Digital Twin Research Centre, Faculty of Science and Technology, Middlesex University, London, U.K.","institution_ids":["https://openalex.org/I60488453"]},{"raw_affiliation_string":"Faculty of Building and Industrial Construction, National University of Civil Engineering, Hanoi, Vietnam","institution_ids":["https://openalex.org/I257903683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001649826","display_name":"H. Tran-Ngoc","orcid":"https://orcid.org/0000-0003-2161-8064"},"institutions":[{"id":"https://openalex.org/I4210093659","display_name":"University of Transport and Communications","ror":"https://ror.org/006qk6d61","country_code":"VN","type":"education","lineage":["https://openalex.org/I4210093659"]},{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE","VN"],"is_corresponding":false,"raw_author_name":"Hoa Tran-Ngoc","raw_affiliation_strings":["Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam","Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium"],"affiliations":[{"raw_affiliation_string":"Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam","institution_ids":["https://openalex.org/I4210093659"]},{"raw_affiliation_string":"Faculty of Engineering and Architecture, Ghent University, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054786913","display_name":"Nguyen Van Tung","orcid":"https://orcid.org/0000-0003-4624-5567"},"institutions":[{"id":"https://openalex.org/I4210092184","display_name":"Schlumberger (France)","ror":"https://ror.org/01b5d7p48","country_code":"FR","type":"company","lineage":["https://openalex.org/I4210092184"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Tung V. Nguyen","raw_affiliation_strings":["Modeling Simulation Team, Schlumberger, Clamart, France"],"affiliations":[{"raw_affiliation_string":"Modeling Simulation Team, Schlumberger, Clamart, France","institution_ids":["https://openalex.org/I4210092184"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015920621","display_name":"Thanh Bui-Tien","orcid":"https://orcid.org/0000-0002-4001-9246"},"institutions":[{"id":"https://openalex.org/I4210093659","display_name":"University of Transport and Communications","ror":"https://ror.org/006qk6d61","country_code":"VN","type":"education","lineage":["https://openalex.org/I4210093659"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"T. Bui-Tien","raw_affiliation_strings":["Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam"],"affiliations":[{"raw_affiliation_string":"Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam","institution_ids":["https://openalex.org/I4210093659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084660092","display_name":"Guido De Roeck","orcid":"https://orcid.org/0000-0003-0293-1514"},"institutions":[{"id":"https://openalex.org/I99464096","display_name":"KU Leuven","ror":"https://ror.org/05f950310","country_code":"BE","type":"education","lineage":["https://openalex.org/I99464096"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Guido De Roeck","raw_affiliation_strings":["KU Leuven, Leuven, Belgium"],"affiliations":[{"raw_affiliation_string":"KU Leuven, Leuven, Belgium","institution_ids":["https://openalex.org/I99464096"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061828397","display_name":"Huan X. Nguyen","orcid":"https://orcid.org/0000-0002-4105-2558"},"institutions":[{"id":"https://openalex.org/I60488453","display_name":"Middlesex University","ror":"https://ror.org/01rv4p989","country_code":"GB","type":"education","lineage":["https://openalex.org/I60488453"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Huan X. Nguyen","raw_affiliation_strings":["London Digital Twin Research Center, Faculty of Science and Technology, Middlesex University, London, U.K."],"affiliations":[{"raw_affiliation_string":"London Digital Twin Research Center, Faculty of Science and Technology, Middlesex University, London, U.K.","institution_ids":["https://openalex.org/I60488453"]}]}],"institution_assertions":[],"countries_distinct_count":4,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.644,"has_fulltext":false,"cited_by_count":100,"citation_normalized_percentile":{"value":0.999961,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"18","issue":"4","first_page":"2087","last_page":"2103"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9997,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9997,"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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9988,"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/T11609","display_name":"Geophysical Methods and Applications","score":0.9821,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/sensor-fusion","display_name":"Sensor Fusion","score":0.5854023},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5189952}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5953138},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5854023},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.55155814},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.54213685},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5189952},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45135486},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43006274},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41548353},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3489731},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.335461},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2020.3034401","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"https://eprints.mdx.ac.uk/31283/1/FINAL_VERSION_IEEEE_Automation.PDF","pdf_url":"https://eprints.mdx.ac.uk/31283/1/FINAL_VERSION_IEEEE_Automation.PDF","source":{"id":"https://openalex.org/S4306400025","display_name":"Middlesex University Research Repository (Middlesex University Of London)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I60488453","host_organization_name":"Middlesex University","host_organization_lineage":["https://openalex.org/I60488453"],"host_organization_lineage_names":["Middlesex University"],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://eprints.mdx.ac.uk/31283/1/FINAL_VERSION_IEEEE_Automation.PDF","pdf_url":"https://eprints.mdx.ac.uk/31283/1/FINAL_VERSION_IEEEE_Automation.PDF","source":{"id":"https://openalex.org/S4306400025","display_name":"Middlesex University Research Repository (Middlesex University Of London)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I60488453","host_organization_name":"Middlesex University","host_organization_lineage":["https://openalex.org/I60488453"],"host_organization_lineage_names":["Middlesex University"],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false},"sustainable_development_goals":[{"score":0.65,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"grants":[{"funder":"https://openalex.org/F4320312933","funder_display_name":"Department for Business, Energy and Industrial Strategy, UK Government","award_id":null}],"datasets":[],"versions":[],"referenced_works_count":33,"referenced_works":["https://openalex.org/W1589046910","https://openalex.org/W1686594169","https://openalex.org/W2007221293","https://openalex.org/W2011667925","https://openalex.org/W2053772077","https://openalex.org/W2064675550","https://openalex.org/W2074432747","https://openalex.org/W2090629586","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2312099968","https://openalex.org/W2325975990","https://openalex.org/W2337787762","https://openalex.org/W2520239233","https://openalex.org/W2523725189","https://openalex.org/W2528295566","https://openalex.org/W2559891893","https://openalex.org/W2902164950","https://openalex.org/W2909961666","https://openalex.org/W2942742309","https://openalex.org/W2944945116","https://openalex.org/W2954324962","https://openalex.org/W2954350473","https://openalex.org/W2964121744","https://openalex.org/W2970971581","https://openalex.org/W2982476176","https://openalex.org/W2993105312","https://openalex.org/W2998168445","https://openalex.org/W3003519348","https://openalex.org/W3006436762","https://openalex.org/W3011374968","https://openalex.org/W3100777112","https://openalex.org/W3103145119"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4304166257","https://openalex.org/W4294635752","https://openalex.org/W4230611425","https://openalex.org/W3214791684","https://openalex.org/W2731899572","https://openalex.org/W2611989081","https://openalex.org/W2152662039","https://openalex.org/W2132659060","https://openalex.org/W2031992971"],"abstract_inverted_index":{"Smart":[0],"structural":[1,381],"health":[2,382],"monitoring":[3,209,379],"(SHM)":[4],"for":[5,12,190,207,228,284,294,324,373,377],"large-scale":[6],"infrastructure":[7],"is":[8,34,51,128,161,271,292,313,328],"an":[9,76],"intriguing":[10],"subject":[11],"engineering":[13],"communities":[14],"thanks":[15],"to":[16,97,145,196,201,217,254],"its":[17,301],"significant":[18],"advantages":[19,369],"such":[20,111],"as":[21,38,112,172,174],"timely":[22],"damage":[23,170,362],"detection,":[24,171],"optimal":[25],"maintenance":[26],"strategy,":[27],"and":[28,75,90,119,139,156,184,220,238,342],"reduced":[29,365],"resource":[30,366],"requirement.":[31],"Yet,":[32],"it":[33,39,160,188],"a":[35,42,61,181,203,335,375],"challenging":[36],"topic":[37],"requires":[40],"handling":[41],"large":[43],"amount":[44],"of":[45,68,135,213,344,357,380],"collected":[46],"sensors":[47],"data":[48,74,158,249],"continuously,":[49],"which":[50,259,308],"inevitably":[52],"contaminated":[53],"by":[54,274,350],"random":[55],"noises.":[56],"Therefore,":[57],"this":[58,295],"study":[59],"developed":[60],"practical":[62,204],"end-to-end":[63],"framework":[64],"that":[65,163],"makes":[66],"use":[67],"physical":[69],"features":[70,100,234],"embedded":[71],"in":[72,224,235,314,330,383],"raw":[73],"elaborated":[77],"hybrid":[78,124,336],"deep":[79,125,281,337],"learning":[80,126,282,338],"model,":[81,115],"namely":[82,287],"1-DCNN-LSTM,":[83],"featuring":[84,250],"two":[85],"algorithms\u2014convolutional":[86],"neural":[87,320],"network":[88,321],"(CNN)":[89],"long-short":[91,288],"term":[92,289],"memory":[93,185,290],"(LSTM).":[94],"In":[95,215],"order":[96,216],"extract":[98],"relevant":[99,311],"from":[101,242],"sensory":[102],"data,":[103],"the":[104,113,132,140,164,175,210,256,269,278,309,315,345,384],"method":[105,206],"combines":[106],"various":[107],"signal":[108,244],"processing":[109],"techniques":[110],"autoregressive":[114],"discrete":[116],"wavelet":[117],"transform,":[118],"empirical":[120],"mode":[121],"decomposition.":[122],"The":[123,340],"1-DCNN-LSTM":[127],"designed":[129,323],"based":[130],"on":[131],"CNN's":[133],"capacity":[134],"capturing":[136,325],"local":[137,326],"information":[138,312,327],"LSTM":[141],"network's":[142],"prominent":[143],"ability":[144],"learn":[146],"long-term":[147],"dependencies.":[148],"Through":[149],"three":[150,351],"case":[151,352],"studies":[152,353],"involving":[153],"both":[154,236],"experimental":[155],"synthetic":[157],"sets,":[159],"demonstrated":[162],"proposed":[165,346],"approach":[166,347],"achieves":[167],"highly":[168,221,360],"accurate":[169,173,222,361],"powerful":[176],"2-D":[177],"CNN,":[178],"but":[179],"with":[180,264,332,354,364],"lower":[182],"time":[183,237],"complexity,":[186],"making":[187],"suitable":[189],"real-time":[191],"SHM.":[192],"Note":[195],"Practitioners":[197],"\u2014This":[198],"article":[199],"aims":[200],"develop":[202],"data-driven":[205],"automatically":[208],"operational":[211],"state":[212],"structures.":[214],"achieve":[218,255],"consistently":[219],"results":[223],"performing":[225],"different":[226,355],"tasks":[227],"diverse":[229],"structures,":[230,358],"we":[231],"combine":[232],"underlying":[233],"frequency":[239,316],"domains":[240],"extracted":[241],"measured":[243,273],"vibration":[245,270],"data.":[246],"Three":[247],"popular":[248],"methods":[251],"are":[252,348],"combined":[253],"diversity":[257],"gain":[258],"would":[260],"not":[261],"be":[262,371],"possible":[263],"each":[265,298],"individual":[266],"method.":[267],"As":[268],"usually":[272],"long":[275],"time-series":[276,285],"signals,":[277],"most":[279,310],"efficient":[280],"architecture":[283],"signal,":[286],"(LSTM),":[291],"considered":[293],"work.":[296],"Besides,":[297],"structure":[299],"has":[300],"own":[302],"dynamic":[303],"properties,":[304],"i.e.,":[305],"eigenfrequencies,":[306],"around":[307],"domain,":[317],"thus":[318],"convolutional":[319],"specifically":[322],"used":[329],"combination":[331],"LSTM,":[333],"forming":[334],"architecture.":[339],"applicability":[341],"effectiveness":[343],"supported":[349],"types":[356],"showing":[359],"detection":[363],"requirements.":[367],"These":[368],"can":[370],"valuable":[372],"developing":[374],"model":[376],"live":[378],"future":[385],"life-line":[386],"infrastructures.":[387]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3100090196","counts_by_year":[{"year":2024,"cited_by_count":34},{"year":2023,"cited_by_count":36},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":1}],"updated_date":"2025-01-04T07:39:25.081137","created_date":"2020-11-23"}