{"id":"https://openalex.org/W4293680188","doi":"https://doi.org/10.3390/s22166193","title":"Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information","display_name":"Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information","publication_year":2022,"publication_date":"2022-08-18","ids":{"openalex":"https://openalex.org/W4293680188","doi":"https://doi.org/10.3390/s22166193","pmid":"https://pubmed.ncbi.nlm.nih.gov/36015955"},"language":"en","primary_location":{"id":"doi:10.3390/s22166193","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22166193","pdf_url":"https://www.mdpi.com/1424-8220/22/16/6193/pdf?version=1660817755","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/1424-8220/22/16/6193/pdf?version=1660817755","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034492769","display_name":"Kyle Dunphy","orcid":"https://orcid.org/0009-0008-7073-0670"},"institutions":[{"id":"https://openalex.org/I125749732","display_name":"Western University","ror":"https://ror.org/02grkyz14","country_code":"CA","type":"education","lineage":["https://openalex.org/I125749732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Kyle Dunphy","raw_affiliation_strings":["Department of Civil and Environmental Engineering, Western University, London, ON N6A 3K7, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Western University, London, ON N6A 3K7, Canada","institution_ids":["https://openalex.org/I125749732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057548404","display_name":"Mohammad Navid Fekri","orcid":"https://orcid.org/0000-0001-8079-7117"},"institutions":[{"id":"https://openalex.org/I125749732","display_name":"Western University","ror":"https://ror.org/02grkyz14","country_code":"CA","type":"education","lineage":["https://openalex.org/I125749732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mohammad Navid Fekri","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada","institution_ids":["https://openalex.org/I125749732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012962681","display_name":"Katarina Grolinger","orcid":"https://orcid.org/0000-0003-0062-8212"},"institutions":[{"id":"https://openalex.org/I125749732","display_name":"Western University","ror":"https://ror.org/02grkyz14","country_code":"CA","type":"education","lineage":["https://openalex.org/I125749732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Katarina Grolinger","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada","institution_ids":["https://openalex.org/I125749732"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026979578","display_name":"Ayan Sadhu","orcid":"https://orcid.org/0000-0001-5685-7087"},"institutions":[{"id":"https://openalex.org/I125749732","display_name":"Western University","ror":"https://ror.org/02grkyz14","country_code":"CA","type":"education","lineage":["https://openalex.org/I125749732"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Ayan Sadhu","raw_affiliation_strings":["Department of Civil and Environmental Engineering, Western University, London, ON N6A 3K7, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, Western University, London, ON N6A 3K7, Canada","institution_ids":["https://openalex.org/I125749732"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5026979578"],"corresponding_institution_ids":["https://openalex.org/I125749732"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":3.8969,"has_fulltext":true,"cited_by_count":38,"citation_normalized_percentile":{"value":0.94228783,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"22","issue":"16","first_page":"6193","last_page":"6193"},"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/T11850","display_name":"Concrete Corrosion and Durability","score":0.9979000091552734,"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":0.996399998664856,"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/structural-health-monitoring","display_name":"Structural health monitoring","score":0.743492603302002},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6440639495849609},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5989682078361511},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5666124820709229},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5194544196128845},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5149350762367249},{"id":"https://openalex.org/keywords/scarcity","display_name":"Scarcity","score":0.4752851724624634},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4533880650997162},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44483843445777893},{"id":"https://openalex.org/keywords/risk-analysis","display_name":"Risk analysis (engineering)","score":0.4087505340576172},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.397394061088562},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.22101867198944092},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.10097995400428772}],"concepts":[{"id":"https://openalex.org/C2776247918","wikidata":"https://www.wikidata.org/wiki/Q1423713","display_name":"Structural health monitoring","level":2,"score":0.743492603302002},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6440639495849609},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5989682078361511},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5666124820709229},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5194544196128845},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5149350762367249},{"id":"https://openalex.org/C109747225","wikidata":"https://www.wikidata.org/wiki/Q815758","display_name":"Scarcity","level":2,"score":0.4752851724624634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4533880650997162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44483843445777893},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.4087505340576172},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.397394061088562},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.22101867198944092},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.10097995400428772},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","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/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000375","descriptor_name":"Aging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000375","descriptor_name":"Aging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000375","descriptor_name":"Aging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003625","descriptor_name":"Data Collection","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003625","descriptor_name":"Data Collection","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003625","descriptor_name":"Data Collection","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016208","descriptor_name":"Databases, Factual","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":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","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":false}],"locations_count":5,"locations":[{"id":"doi:10.3390/s22166193","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22166193","pdf_url":"https://www.mdpi.com/1424-8220/22/16/6193/pdf?version=1660817755","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:36015955","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36015955","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:d608230beb944abc8f4d60776e1d727e","is_oa":true,"landing_page_url":"https://doaj.org/article/d608230beb944abc8f4d60776e1d727e","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 22, Iss 16, p 6193 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/16/6193/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22166193","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 16; Pages: 6193","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:9412832","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9412832","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","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":"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/s22166193","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22166193","pdf_url":"https://www.mdpi.com/1424-8220/22/16/6193/pdf?version=1660817755","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":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.550000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4293680188.pdf","grobid_xml":"https://content.openalex.org/works/W4293680188.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W2739748921","https://openalex.org/W2768955070","https://openalex.org/W2806828180","https://openalex.org/W2896613037","https://openalex.org/W2899803215","https://openalex.org/W2911081702","https://openalex.org/W2919816425","https://openalex.org/W2962770929","https://openalex.org/W2962949934","https://openalex.org/W2963830453","https://openalex.org/W3011055781","https://openalex.org/W3013406096","https://openalex.org/W3033621544","https://openalex.org/W3033645921","https://openalex.org/W3034059856","https://openalex.org/W3034109443","https://openalex.org/W3044198040","https://openalex.org/W3044248863","https://openalex.org/W3045530378","https://openalex.org/W3047697279","https://openalex.org/W3092593798","https://openalex.org/W3093468414","https://openalex.org/W3096372554","https://openalex.org/W3120695505","https://openalex.org/W3125559191","https://openalex.org/W3136334612","https://openalex.org/W3138958118","https://openalex.org/W3156252492","https://openalex.org/W3170808441","https://openalex.org/W3175504132","https://openalex.org/W3177403466","https://openalex.org/W3180937864","https://openalex.org/W3192378566","https://openalex.org/W3197294349","https://openalex.org/W3212398396","https://openalex.org/W4205215834","https://openalex.org/W4206964074","https://openalex.org/W4224255587","https://openalex.org/W4226077772","https://openalex.org/W6735913928","https://openalex.org/W6780651711"],"related_works":["https://openalex.org/W1571141552","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W4399254932"],"abstract_inverted_index":{"The":[0,43,159,333],"deterioration":[1],"of":[2,85,120,141,157,161,224,241,258,271,343,355,372,385],"infrastructure's":[3],"health":[4],"has":[5],"become":[6],"more":[7],"predominant":[8],"on":[9,275,296,304],"a":[10,193,199,242,252,310,329],"global":[11],"scale":[12],"during":[13],"the":[14,30,50,83,89,92,134,139,155,189,207,217,222,239,267,272,291,318,339,353,370,373,380,383],"21st":[15],"century.":[16],"Aging":[17],"infrastructure":[18,53],"as":[19,21,192],"well":[20],"those":[22],"structures":[23],"damaged":[24,52],"by":[25,88,197,279,306],"natural":[26],"disasters":[27],"have":[28,129,185],"prompted":[29],"research":[31],"community":[32,191],"to":[33,61,81,106,116,137,151,168,205,290,359],"improve":[34,206],"state-of-the-art":[35],"methodologies":[36],"for":[37,45,254,283,328,347],"conducting":[38],"Structural":[39],"Health":[40],"Monitoring":[41],"(SHM).":[42],"necessity":[44],"efficient":[46],"SHM":[47,190,218],"arises":[48],"from":[49,122,188,233,251],"hazards":[51],"imposes,":[54],"often":[55,100,172],"resulting":[56],"in":[57,70,133,174,382],"structural":[58,146],"collapse,":[59],"leading":[60],"economic":[62],"loss":[63],"and":[64,91,104,144,165,176,281,285,317,325,338,341,352,376],"human":[65],"fatalities.":[66],"Furthermore,":[67],"day-to-day":[68],"operations":[69],"these":[71],"affected":[72],"areas":[73],"are":[74,99,212],"limited":[75,357],"until":[76],"an":[77],"inspection":[78,123],"is":[79,350,364],"performed":[80],"assess":[82],"level":[84],"damage":[86,178,208,227,256],"experienced":[87],"structure":[90],"required":[93],"rehabilitation":[94],"determined.":[95],"However,":[96,210],"human-based":[97],"inspections":[98],"labor-intensive,":[101],"inefficient,":[102],"subjective,":[103],"restricted":[105],"accessible":[107],"site":[108],"locations,":[109],"which":[110,198,220],"ultimately":[111],"negatively":[112],"impact":[113],"our":[114],"ability":[115],"collect":[117],"large":[118],"amounts":[119],"data":[121,148,194,231,345,348,358],"sites.":[124],"Though":[125],"Deep-Learning":[126],"(DL)":[127],"methods":[128,143],"been":[130],"heavily":[131],"explored":[132],"past":[135],"decade":[136],"rectify":[138],"limitations":[140],"traditional":[142],"automate":[145],"inspection,":[147],"scarcity":[149],"continues":[150],"remain":[152],"prevalent":[153],"within":[154,216],"field":[156,219],"SHM.":[158],"absence":[160],"sufficiently":[162],"large,":[163],"balanced,":[164],"generalized":[166],"databases":[167],"train":[169,360],"DL-based":[170,225],"models":[171],"results":[173],"inaccurate":[175],"biased":[177],"predictions.":[179],"Recently,":[180],"Generative":[181],"Adversarial":[182],"Networks":[183],"(GANs)":[184],"received":[186],"attention":[187],"augmentation":[195,349],"tool":[196],"training":[200,331],"dataset":[201],"can":[202],"be":[203],"expanded":[204],"classification.":[209],"there":[211],"no":[213],"existing":[214,361],"studies":[215],"investigate":[221],"performance":[223,240,270,302],"multiclass":[226,255],"identification":[228],"using":[229,247,356],"synthetic":[230,248,326,344,386],"generated":[232,250],"GANs.":[234],"Therefore,":[235],"this":[236,262],"paper":[237],"investigates":[238],"convolutional":[243],"neural":[244],"network":[245],"architecture":[246],"images":[249],"GAN":[253,362],"detection":[257],"concrete":[259],"surfaces.":[260],"Through":[261],"study,":[263],"it":[264],"was":[265,367],"determined":[266],"average":[268,305],"classification":[269,336],"proposed":[273],"CNN":[274],"hybrid":[276],"datasets":[277,287],"decreased":[278,303],"10.6%":[280],"7.4%":[282],"validation":[284],"testing":[286],"when":[288,308],"compared":[289],"same":[292,319],"model":[293,312,320],"trained":[294,313,321],"entirely":[295],"real":[297,315,324],"samples.":[298,387],"Moreover,":[299],"each":[300],"model's":[301],"1.6%":[307],"comparing":[309],"singular":[311],"with":[314,322,379],"samples":[316,327,374],"both":[323],"given":[330],"configuration.":[332],"correlation":[334,377],"between":[335],"accuracy":[337],"amount":[340],"diversity":[342,371],"used":[346],"quantified":[351],"effect":[354],"architectures":[363],"investigated.":[365],"It":[366],"observed":[368],"that":[369],"decreases":[375],"increases":[378],"increase":[381],"number":[384]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}