{"id":"https://openalex.org/W2750102582","doi":"https://doi.org/10.1061/(asce)cp.1943-5487.0000712","title":"Bridge Type Classification: Supervised Learning on a Modified NBI Data Set","display_name":"Bridge Type Classification: Supervised Learning on a Modified NBI Data Set","publication_year":2017,"publication_date":"2017-08-28","ids":{"openalex":"https://openalex.org/W2750102582","doi":"https://doi.org/10.1061/(asce)cp.1943-5487.0000712","mag":"2750102582"},"language":"en","primary_location":{"id":"doi:10.1061/(asce)cp.1943-5487.0000712","is_oa":false,"landing_page_url":"https://doi.org/10.1061/(asce)cp.1943-5487.0000712","pdf_url":null,"source":{"id":"https://openalex.org/S176637136","display_name":"Journal of Computing in Civil Engineering","issn_l":"0887-3801","issn":["0887-3801","1943-5487"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315747","host_organization_name":"American Society of Civil Engineers","host_organization_lineage":["https://openalex.org/P4310315747"],"host_organization_lineage_names":["American Society of Civil Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computing in Civil Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1803.04478","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Achyuthan Jootoo","orcid":null},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Achyuthan Jootoo","raw_affiliation_strings":["Ph.D. Candidate, Dept. of Civil, Environmental, and Infrastructure Engineering, George Mason Univ., Fairfax, VA 22030"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ph.D. Candidate, Dept. of Civil, Environmental, and Infrastructure Engineering, George Mason Univ., Fairfax, VA 22030","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":null,"display_name":"David Lattanzi","orcid":"https://orcid.org/0000-0001-9247-0680"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Lattanzi","raw_affiliation_strings":["Assistant Professor, Dept. of Civil, Environmental, and Infrastructure Engineering, George Mason Univ., Fairfax, VA 22030 (corresponding author). ORCID: "],"raw_orcid":"https://orcid.org/0000-0001-9247-0680","affiliations":[{"raw_affiliation_string":"Assistant Professor, Dept. of Civil, Environmental, and Infrastructure Engineering, George Mason Univ., Fairfax, VA 22030 (corresponding author). ORCID: ","institution_ids":["https://openalex.org/I162714631"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162714631"],"apc_list":null,"apc_paid":null,"fwci":0.4988,"has_fulltext":false,"cited_by_count":27,"citation_normalized_percentile":{"value":0.68495556,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"31","issue":"6","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":0.37700000405311584,"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":0.37700000405311584,"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/T11006","display_name":"BIM and Construction Integration","score":0.034699998795986176,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.03449999913573265,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.6464999914169312},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.6237999796867371},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4918999969959259},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4575999975204468},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4147999882698059},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3953999876976013},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3853999972343445},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.37279999256134033},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.3718000054359436}],"concepts":[{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.6464999914169312},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.6237999796867371},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6161999702453613},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5932999849319458},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4918999969959259},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48089998960494995},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4790000021457672},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4575999975204468},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3853999972343445},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.37279999256134033},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3718000054359436},{"id":"https://openalex.org/C34972735","wikidata":"https://www.wikidata.org/wiki/Q2920267","display_name":"Engineering design process","level":2,"score":0.3634999990463257},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C2776247918","wikidata":"https://www.wikidata.org/wiki/Q1423713","display_name":"Structural health monitoring","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.34860000014305115},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3409000039100647},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.325300008058548},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.29510000348091125},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2797999978065491},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1061/(asce)cp.1943-5487.0000712","is_oa":false,"landing_page_url":"https://doi.org/10.1061/(asce)cp.1943-5487.0000712","pdf_url":null,"source":{"id":"https://openalex.org/S176637136","display_name":"Journal of Computing in Civil Engineering","issn_l":"0887-3801","issn":["0887-3801","1943-5487"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315747","host_organization_name":"American Society of Civil Engineers","host_organization_lineage":["https://openalex.org/P4310315747"],"host_organization_lineage_names":["American Society of Civil Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computing in Civil Engineering","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1803.04478","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1803.04478","pdf_url":"https://arxiv.org/pdf/1803.04478","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1803.04478","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1803.04478","pdf_url":"https://arxiv.org/pdf/1803.04478","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W165893037","https://openalex.org/W1871981171","https://openalex.org/W1943768448","https://openalex.org/W1965100663","https://openalex.org/W1987264988","https://openalex.org/W1988975246","https://openalex.org/W1990919773","https://openalex.org/W2017281898","https://openalex.org/W2026184121","https://openalex.org/W2038808304","https://openalex.org/W2039488112","https://openalex.org/W2067426547","https://openalex.org/W2067743442","https://openalex.org/W2068893448","https://openalex.org/W2072459226","https://openalex.org/W2072871886","https://openalex.org/W2076668284","https://openalex.org/W2083780116","https://openalex.org/W2100294832","https://openalex.org/W2109563136","https://openalex.org/W2117423391","https://openalex.org/W2119315254","https://openalex.org/W2124657875","https://openalex.org/W2132996843","https://openalex.org/W2148681431","https://openalex.org/W2164933988","https://openalex.org/W4236354166","https://openalex.org/W4246598646"],"related_works":[],"abstract_inverted_index":{"A":[0],"key":[1],"phase":[2],"in":[3,168,243],"the":[4,9,12,51,70,79,105,117,138,160,181,212,223,226,234,251],"bridge":[5,73,118,161],"design":[6,41,60,84,162,174,231],"process":[7],"is":[8],"selection":[10,126],"of":[11,40,45,53,81,140,208,225],"structural":[13,33],"system.":[14],"Due":[15,164],"to":[16,49,65,69,75,92,165,221],"budget":[17],"and":[18,27,86,134,143,152,173,196,233,248,256,262,267,269],"time":[19],"constraints,":[20],"engineers":[21,66],"typically":[22],"rely":[23],"on":[24,137,180,202],"engineering":[25],"judgment":[26],"prior":[28],"experience":[29],"when":[30],"selecting":[31],"a":[32,37,58],"system,":[34,97],"often":[35],"considering":[36],"limited":[38],"range":[39],"alternatives.":[42],"The":[43,216,245],"objective":[44],"this":[46,94],"study":[47],"was":[48],"explore":[50],"suitability":[52],"supervised":[54,95,176,235],"machine":[55],"learning":[56,96,177,191,236],"as":[57],"preliminary":[59],"aid":[61],"that":[62],"provides":[63],"guidance":[64],"with":[67],"regards":[68],"statistically":[71],"optimal":[72],"type":[74,120],"choose,":[76],"ultimately":[77],"improving":[78],"likelihood":[80],"optimized":[82],"design,":[83],"standardization,":[85],"reduced":[87],"maintenance":[88],"costs.":[89],"In":[90],"order":[91],"devise":[93],"data":[98,136,183,203,210,217],"for":[99,115,158,204,250],"more":[100,229],"than":[101],"600,000":[102],"bridges":[103],"from":[104],"National":[106],"Bridge":[107],"Inventory":[108],"(NBI)":[109],"database":[110],"were":[111,121,145,156,193,218,254],"analyzed.":[112],"Key":[113],"attributes":[114,130],"determining":[116],"structure":[119],"identified":[122],"through":[123],"three":[124],"feature":[125],"techniques.":[127],"Potentially":[128],"useful":[129],"like":[131],"seismic":[132,209],"intensity":[133],"historic":[135],"cost":[139],"materials":[141],"(steel":[142],"concrete)":[144],"then":[146,194,219],"added.":[147],"Decision":[148],"tree,":[149],"Bayes":[150],"network,":[151],"support":[153],"vector":[154],"machines":[155],"used":[157],"predicting":[159],"type.":[163],"state-to-state":[166],"variations":[167],"material":[169,171],"availability,":[170],"costs,":[172],"codes,":[175],"models":[178,192,227,237,253],"based":[179],"complete":[182],"set":[184],"did":[185],"not":[186],"yield":[187],"favorable":[188],"results.":[189],"Supervised":[190],"trained":[195],"tested":[197],"using":[198,258,264,271],"10-fold":[199],"cross":[200],"validation":[201],"each":[205],"state.":[206],"Inclusion":[207],"improved":[211],"model":[213],"performance":[214],"noticeably.":[215],"resampled":[220],"reduce":[222],"bias":[224],"toward":[228],"common":[230],"types,":[232],"thus":[238],"constructed":[239],"showed":[240],"further":[241],"improvements":[242],"performance.":[244],"average":[246],"recall":[247],"precision":[249],"state":[252],"88.6":[255],"88.0%":[257],"decision":[259],"trees,":[260],"84.0":[261],"83.7%":[263],"Bayesian":[265],"networks,":[266],"80.8":[268],"75.6%":[270],"SVM.":[272]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2017-08-31T00:00:00"}
