{"id":"https://openalex.org/W4200241127","doi":"https://doi.org/10.1080/08839514.2021.2014188","title":"Application of Deep Convolution Neural Network in Crack Identification","display_name":"Application of Deep Convolution Neural Network in Crack Identification","publication_year":2021,"publication_date":"2021-12-08","ids":{"openalex":"https://openalex.org/W4200241127","doi":"https://doi.org/10.1080/08839514.2021.2014188"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2021.2014188","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2021.2014188","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.2014188?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.2014188?needAccess=true","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067398916","display_name":"Zhengyun Xu","orcid":"https://orcid.org/0000-0002-1190-991X"},"institutions":[{"id":"https://openalex.org/I178232147","display_name":"Guizhou University","ror":"https://ror.org/02wmsc916","country_code":"CN","type":"education","lineage":["https://openalex.org/I178232147"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengyun Xu","raw_affiliation_strings":["Guizhou University","College of Mechanical Engineering, Guizhou University, Guizhou, China"],"raw_orcid":"https://orcid.org/0000-0002-1190-991X","affiliations":[{"raw_affiliation_string":"Guizhou University","institution_ids":["https://openalex.org/I178232147"]},{"raw_affiliation_string":"College of Mechanical Engineering, Guizhou University, Guizhou, China","institution_ids":["https://openalex.org/I178232147"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043736836","display_name":"Songrong Qian","orcid":"https://orcid.org/0000-0001-5082-6028"},"institutions":[{"id":"https://openalex.org/I178232147","display_name":"Guizhou University","ror":"https://ror.org/02wmsc916","country_code":"CN","type":"education","lineage":["https://openalex.org/I178232147"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Songrong Qian","raw_affiliation_strings":["Guizhou University","College of Mechanical Engineering, Guizhou University, Guizhou, China","State Key Laboratory of Public Big Data, Guizhou University, Guizhou, China"],"raw_orcid":"https://orcid.org/0000-0001-5082-6028","affiliations":[{"raw_affiliation_string":"Guizhou University","institution_ids":["https://openalex.org/I178232147"]},{"raw_affiliation_string":"College of Mechanical Engineering, Guizhou University, Guizhou, China","institution_ids":["https://openalex.org/I178232147"]},{"raw_affiliation_string":"State Key Laboratory of Public Big Data, Guizhou University, Guizhou, China","institution_ids":["https://openalex.org/I178232147"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032159400","display_name":"Xiu Ran","orcid":null},"institutions":[{"id":"https://openalex.org/I178232147","display_name":"Guizhou University","ror":"https://ror.org/02wmsc916","country_code":"CN","type":"education","lineage":["https://openalex.org/I178232147"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiu Ran","raw_affiliation_strings":["Guizhou University","College of Mechanical Engineering, Guizhou University, Guizhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guizhou University","institution_ids":["https://openalex.org/I178232147"]},{"raw_affiliation_string":"College of Mechanical Engineering, Guizhou University, Guizhou, China","institution_ids":["https://openalex.org/I178232147"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031240825","display_name":"Ji Zhou","orcid":"https://orcid.org/0000-0003-2135-1814"},"institutions":[{"id":"https://openalex.org/I178232147","display_name":"Guizhou University","ror":"https://ror.org/02wmsc916","country_code":"CN","type":"education","lineage":["https://openalex.org/I178232147"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ji Zhou","raw_affiliation_strings":["Guizhou University","College of Mechanical Engineering, Guizhou University, Guizhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guizhou University","institution_ids":["https://openalex.org/I178232147"]},{"raw_affiliation_string":"College of Mechanical Engineering, Guizhou University, Guizhou, China","institution_ids":["https://openalex.org/I178232147"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5043736836"],"corresponding_institution_ids":["https://openalex.org/I178232147"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":{"value":2195,"currency":"USD","value_usd":2195},"fwci":0.3968,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.6041535,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"36","issue":"1","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":0.9998999834060669,"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":0.9998999834060669,"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":0.9914000034332275,"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/T11850","display_name":"Concrete Corrosion and Durability","score":0.9643999934196472,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.841195821762085},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6172797083854675},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6049266457557678},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5962260961532593},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5918455719947815},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5463661551475525},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5277931094169617},{"id":"https://openalex.org/keywords/universality","display_name":"Universality (dynamical systems)","score":0.49648338556289673},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4524194598197937},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4347212016582489},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4266938865184784},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41585949063301086},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4010877013206482},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34762492775917053}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.841195821762085},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6172797083854675},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6049266457557678},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5962260961532593},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5918455719947815},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5463661551475525},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5277931094169617},{"id":"https://openalex.org/C183992945","wikidata":"https://www.wikidata.org/wiki/Q2495574","display_name":"Universality (dynamical systems)","level":2,"score":0.49648338556289673},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4524194598197937},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4347212016582489},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4266938865184784},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41585949063301086},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4010877013206482},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34762492775917053},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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":[{"id":"doi:10.1080/08839514.2021.2014188","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2021.2014188","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.2014188?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a7b69e97b621404489659bf0a71d6969","is_oa":false,"landing_page_url":"https://doaj.org/article/a7b69e97b621404489659bf0a71d6969","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 36, Iss 1 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1080/08839514.2021.2014188","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2021.2014188","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2021.2014188?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.4699999988079071,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4200241127.pdf"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1523493493","https://openalex.org/W2086926907","https://openalex.org/W2204513093","https://openalex.org/W2302345939","https://openalex.org/W2470621907","https://openalex.org/W2528305538","https://openalex.org/W2531409750","https://openalex.org/W2558580397","https://openalex.org/W2758219896","https://openalex.org/W2783755104","https://openalex.org/W2790443277","https://openalex.org/W2800346298","https://openalex.org/W2803199689","https://openalex.org/W2810188946","https://openalex.org/W2896568470","https://openalex.org/W2896613037","https://openalex.org/W2896969435","https://openalex.org/W2899803215","https://openalex.org/W2900066043","https://openalex.org/W2905127877","https://openalex.org/W2906939751","https://openalex.org/W2922073063","https://openalex.org/W2935931695","https://openalex.org/W2942685721","https://openalex.org/W2963908722","https://openalex.org/W2964147297","https://openalex.org/W2970362668","https://openalex.org/W2999795060","https://openalex.org/W3024770686","https://openalex.org/W3033697593","https://openalex.org/W3047042937","https://openalex.org/W3092784991","https://openalex.org/W3118378226","https://openalex.org/W3120073045","https://openalex.org/W3124451475","https://openalex.org/W3135518078","https://openalex.org/W3191764388"],"related_works":["https://openalex.org/W2065095781","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3088721469","https://openalex.org/W2964954556","https://openalex.org/W3029198973","https://openalex.org/W3019910406"],"abstract_inverted_index":{"The":[0,153],"surface":[1,35,42],"crack":[2,80],"of":[3,13,23,37,63,130,137,145,159],"structure":[4],"is":[5,28,126,166],"an":[6,135],"important":[7],"sign":[8],"to":[9,17,30,56,93],"evaluate":[10],"the":[11,19,24,34,38,60,95,101,109,113,120,131,141,146,157,160,163],"safety":[12,20],"structure.":[14,39],"In":[15],"order":[16],"ensure":[18],"and":[21,32,51,78,117,143,162,168],"reliability":[22],"building":[25],"structure,":[26,105],"it":[27,125],"necessary":[29],"detect":[31],"monitor":[33],"cracks":[36],"Traditional":[40],"artificial":[41],"inspections":[43],"are":[44,71,91],"time-consuming":[45],"because":[46],"inspectors":[47],"have":[48],"different":[49],"experience":[50],"knowledge,":[52],"which":[53],"can":[54,107,133],"lead":[55],"misjudgments.":[57],"Based":[58],"on":[59],"basic":[61],"framework":[62],"four":[64],"deep":[65],"convolution":[66],"neural":[67],"networks,":[68],"their":[69],"classifiers":[70],"reconstructed.":[72],"To":[73,139],"fully":[74],"train":[75],"these":[76],"networks":[77],"simulate":[79],"images":[81],"taken":[82],"in":[83,86,112],"various":[84],"situations":[85],"life,":[87],"image":[88,114],"enhancement":[89],"techniques":[90],"used":[92],"extend":[94],"dataset.":[96],"After":[97,122],"training,":[98],"compared":[99],"with":[100],"established":[102],"shallow":[103],"network":[104],"they":[106],"learn":[108],"feature":[110],"information":[111],"more":[115],"fully,":[116],"finally":[118],"improve":[119],"accuracy.":[121],"further":[123],"verification,":[124],"found":[127],"that":[128],"one":[129],"models":[132],"achieve":[134],"accuracy":[136],"96.5%.":[138],"verify":[140],"universality":[142],"validity":[144,158],"model,":[147,161],"two":[148],"cross-datasets":[149],"experiments":[150],"were":[151],"performed.":[152],"experimental":[154],"results":[155],"show":[156],"diagnostic":[164],"precision":[165],"98.23%":[167],"99.04%,":[169],"respectively.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-04T06:09:54.619538","created_date":"2025-10-10T00:00:00"}
