{"id":"https://openalex.org/W7147396506","doi":"https://doi.org/10.48550/arxiv.2603.28225","title":"Detecting the Unexpected: AI-Driven Anomaly Detection in Smart Bridge Monitoring","display_name":"Detecting the Unexpected: AI-Driven Anomaly Detection in Smart Bridge Monitoring","publication_year":2026,"publication_date":"2026-03-30","ids":{"openalex":"https://openalex.org/W7147396506","doi":"https://doi.org/10.48550/arxiv.2603.28225"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.28225","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28225","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.28225","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132703456","display_name":"Rahul Jaiswal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jaiswal, Rahul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132545393","display_name":"Joakim Hellum","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hellum, Joakim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132598607","display_name":"Halvor Heiberg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heiberg, Halvor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.31690001487731934,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.31690001487731934,"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"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.2759999930858612,"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":0.2029999941587448,"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/bridge","display_name":"Bridge (graph theory)","score":0.8287000060081482},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7472000122070312},{"id":"https://openalex.org/keywords/safety-monitoring","display_name":"Safety monitoring","score":0.5109999775886536},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4846999943256378},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44269999861717224},{"id":"https://openalex.org/keywords/intelligent-sensor","display_name":"Intelligent sensor","score":0.3756999969482422},{"id":"https://openalex.org/keywords/smart-city","display_name":"Smart city","score":0.3736000061035156}],"concepts":[{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.8287000060081482},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7472000122070312},{"id":"https://openalex.org/C2777488183","wikidata":"https://www.wikidata.org/wiki/Q6900510","display_name":"Safety monitoring","level":2,"score":0.5109999775886536},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4948999881744385},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4846999943256378},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.45249998569488525},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44269999861717224},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.4212999939918518},{"id":"https://openalex.org/C176563091","wikidata":"https://www.wikidata.org/wiki/Q669238","display_name":"Intelligent sensor","level":3,"score":0.3756999969482422},{"id":"https://openalex.org/C2777103469","wikidata":"https://www.wikidata.org/wiki/Q1231558","display_name":"Smart city","level":3,"score":0.3736000061035156},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.34779998660087585},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3190999925136566},{"id":"https://openalex.org/C2776247918","wikidata":"https://www.wikidata.org/wiki/Q1423713","display_name":"Structural health monitoring","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3091000020503998},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.2574000060558319}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.28225","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28225","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.28225","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.28225","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.5391806960105896,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Bridges":[0],"are":[1,37],"critical":[2],"components":[3],"of":[4,101,139],"national":[5],"infrastructure":[6],"and":[7,20,39,43,129],"smart":[8,11,56,126],"cities.":[9],"Therefore,":[10],"bridge":[12,27,57,81,127],"monitoring":[13,28,128],"is":[14,66,87,123],"essential":[15],"for":[16,55,125],"ensuring":[17],"public":[18,132],"safety":[19,133],"preventing":[21],"catastrophic":[22],"failures":[23],"or":[24],"accidents.":[25],"Traditional":[26],"methods":[29],"rely":[30],"heavily":[31],"on":[32,79],"human":[33],"visual":[34],"inspections,":[35],"which":[36],"time-consuming":[38],"prone":[40],"to":[41],"subjectivity":[42],"error.":[44],"This":[45],"paper":[46],"proposes":[47],"an":[48],"artificial":[49],"intelligence":[50],"(AI)-driven":[51],"anomaly":[52],"detection":[53,138],"approach":[54],"monitoring.":[58],"Specifically,":[59],"a":[60,80],"simple":[61],"machine":[62],"learning":[63],"(ML)":[64],"model":[65,86,106,122],"developed":[67],"using":[68],"real-time":[69],"sensor":[70,76],"data":[71],"collected":[72],"by":[73,134],"the":[74,97,111,120,136],"iBridge":[75],"devices":[77],"installed":[78],"in":[82,108],"Norway.":[83],"The":[84],"proposed":[85,121],"evaluated":[88],"against":[89],"different":[90],"ML":[91],"models.":[92],"Experimental":[93],"results":[94],"demonstrate":[95],"that":[96,119],"density-based":[98],"spatial":[99],"clustering":[100],"applications":[102],"with":[103],"noise":[104],"(DBSCAN)-based":[105],"outperforms":[107],"accurately":[109],"detecting":[110],"anomalous":[112],"events":[113],"(bridge":[114],"accident).":[115],"These":[116],"findings":[117],"indicate":[118],"well-suited":[124],"can":[130],"enhance":[131],"enabling":[135],"timely":[137],"unforeseen":[140],"incidents.":[141]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-02T00:00:00"}
