{"id":"https://openalex.org/W7127959552","doi":"https://doi.org/10.1051/e3sconf/202669203001/pdf","title":"Intelligent Digital Twin Framework for Real-Time Structural Health Monitoring and Optimization of Mechanical Systems Using Reinforcement Learning","display_name":"Intelligent Digital Twin Framework for Real-Time Structural Health Monitoring and Optimization of Mechanical Systems Using Reinforcement Learning","publication_year":2026,"publication_date":"2026-02-04","ids":{"openalex":"https://openalex.org/W7127959552","doi":"https://doi.org/10.1051/e3sconf/202669203001/pdf"},"language":"en","primary_location":{"id":"pmh:oai:edpsciences.org:dkey/10.1051/e3sconf/202669203001","doi":"https://doi.org/10.1051/e3sconf/202669203001/pdf","is_oa":true,"landing_page_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202669203001/pdf","pdf_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202669203001/pdf","source":{"id":"https://openalex.org/S4306400744","display_name":"Springer Link (Chiba Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I8488066","host_organization_name":"Chiba Institute of Technology","host_organization_lineage":["https://openalex.org/I8488066"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1051/e3sconf/202669203001","raw_type":"Text"},"volume":null,"type":"article","indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202669203001/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125002131","display_name":"Santosh Kumar S","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Santosh Kumar S","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125043676","display_name":"Veena G N","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Veena G N","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125062283","display_name":"Kavitha B N","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kavitha B N","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125015103","display_name":"Sarala S M","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sarala S M","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124995949","display_name":"Arjun Kumar G B","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arjun Kumar G B","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125049977","display_name":"Trisha Y S","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Trisha Y S","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":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05202563,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10876","display_name":"Fault Detection and Control Systems","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/intelligent-digital-twins","display_name":"intelligent digital twins","score":0.9309999942779541},{"id":"https://openalex.org/keywords/structural-health-monitoring","display_name":"structural health monitoring","score":0.996999979019165},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"reinforcement learning","score":0.828000009059906},{"id":"https://openalex.org/keywords/real-time-monitoring","display_name":"real-time monitoring","score":0.5350000262260437},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.800000011920929},{"id":"https://openalex.org/keywords/state-estimation","display_name":"state estimation","score":0.734000027179718},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"anomaly detection","score":0.8550000190734863},{"id":"https://openalex.org/keywords/fault-detection","display_name":"fault detection","score":0.6610000133514404},{"id":"https://openalex.org/keywords/maintenance-strategy-optimization","display_name":"maintenance strategy optimization","score":0.5649999976158142},{"id":"https://openalex.org/keywords/damage-sensitive-features","display_name":"damage-sensitive features","score":0.5490000247955322}],"concepts":[{"id":"https://openalex.org/C2776247918","wikidata":"https://www.wikidata.org/wiki/Q1423713","display_name":"Structural health monitoring","level":2,"score":0.7509999871253967},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.7052000164985657},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6442999839782715},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5113000273704529},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.4643999934196472},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.44589999318122864},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.42309999465942383},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3400999903678894},{"id":"https://openalex.org/C135628077","wikidata":"https://www.wikidata.org/wiki/Q220184","display_name":"Finite element method","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C176563091","wikidata":"https://www.wikidata.org/wiki/Q669238","display_name":"Intelligent sensor","level":3,"score":0.33399999141693115},{"id":"https://openalex.org/C2775846686","wikidata":"https://www.wikidata.org/wiki/Q643012","display_name":"Condition monitoring","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C171912257","wikidata":"https://www.wikidata.org/wiki/Q11019","display_name":"Mechanical system","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.298799991607666},{"id":"https://openalex.org/C158411068","wikidata":"https://www.wikidata.org/wiki/Q2720568","display_name":"Digital control","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C36390408","wikidata":"https://www.wikidata.org/wiki/Q1163067","display_name":"Digital filter","level":3,"score":0.27090001106262207},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C88282795","wikidata":"https://www.wikidata.org/wiki/Q2448108","display_name":"Structural system","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2538999915122986},{"id":"https://openalex.org/C8072696","wikidata":"https://www.wikidata.org/wiki/Q811679","display_name":"Structural element","level":2,"score":0.251800000667572},{"id":"https://openalex.org/C2987595161","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Optimization algorithm","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:edpsciences.org:dkey/10.1051/e3sconf/202669203001","doi":"https://doi.org/10.1051/e3sconf/202669203001/pdf","is_oa":true,"landing_page_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202669203001/pdf","pdf_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202669203001/pdf","source":{"id":"https://openalex.org/S4306400744","display_name":"Springer Link (Chiba Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I8488066","host_organization_name":"Chiba Institute of Technology","host_organization_lineage":["https://openalex.org/I8488066"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1051/e3sconf/202669203001","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:edpsciences.org:dkey/10.1051/e3sconf/202669203001","doi":"https://doi.org/10.1051/e3sconf/202669203001/pdf","is_oa":true,"landing_page_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202669203001/pdf","pdf_url":"https://www.e3s-conferences.org/10.1051/e3sconf/202669203001/pdf","source":{"id":"https://openalex.org/S4306400744","display_name":"Springer Link (Chiba Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I8488066","host_organization_name":"Chiba Institute of Technology","host_organization_lineage":["https://openalex.org/I8488066"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://doi.org/10.1051/e3sconf/202669203001","raw_type":"Text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":1.0}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7127959552.pdf","grobid_xml":"https://content.openalex.org/works/W7127959552.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"an":[3],"Intelligent":[4],"Digital":[5],"Twin":[6],"(IDT)":[7],"framework":[8,115],"designed":[9],"for":[10,69,183],"real-time":[11],"Structural":[12],"Health":[13],"Monitoring":[14],"(SHM)":[15],"and":[16,46,51,81,104,111,118,148,178],"optimization":[17,151],"of":[18,38,158,166],"mechanical":[19,123],"systems.":[20],"The":[21,114],"proposed":[22],"approach":[23],"integrates":[24],"high-frequency":[25],"sensor":[26],"data":[27],"with":[28,154,171],"a":[29,60,89,121,149],"physics-based":[30],"digital":[31,64,98,168],"model":[32],"to":[33,84,100,126,136,174],"provide":[34],"accurate,":[35],"continuous":[36],"assessment":[37],"structural":[39,109],"integrity.":[40],"Sensor":[41],"inputs":[42],"from":[43],"vibration,":[44],"strain,":[45],"temperature":[47],"measurements":[48],"are":[49,57],"preprocessed":[50],"transformed":[52],"into":[53,59],"damage-sensitive":[54],"features,":[55],"which":[56],"assimilated":[58],"high-fidelity":[61],"finite":[62],"element":[63],"twin":[65,99,169],"using":[66],"Kalman":[67],"filtering":[68],"precise":[70],"state":[71],"estimation.":[72],"An":[73],"anomaly":[74],"detection":[75,142],"module":[76],"evaluates":[77],"residuals":[78],"between":[79],"measured":[80],"predicted":[82],"responses":[83],"identify":[85],"potential":[86],"faults,":[87],"while":[88],"reinforcement":[90],"learning":[91],"(RL)":[92],"agent":[93],"operates":[94],"within":[95],"the":[96,164],"updated":[97],"learn":[101],"optimal":[102],"maintenance":[103],"control":[105],"strategies":[106],"that":[107],"minimize":[108],"degradation":[110],"operational":[112],"costs.":[113],"is":[116],"implemented":[117],"tested":[119],"on":[120],"scaled":[122],"testbed":[124],"subjected":[125],"dynamic":[127],"loading.":[128],"Experimental":[129],"results":[130],"demonstrate":[131],"significant":[132],"performance":[133],"improvements":[134],"compared":[135],"conventional":[137],"SHM":[138,180],"methods,":[139],"achieving":[140],"96.8%":[141],"accuracy,":[143],"95.6%":[144],"precision,":[145],"96.1%":[146],"recall,":[147],"14.7%":[150],"gain,":[152],"along":[153],"reduced":[155],"response":[156],"latency":[157],"135":[159],"ms.":[160],"These":[161],"outcomes":[162],"highlight":[163],"effectiveness":[165],"combining":[167],"technology":[170],"RL-driven":[172],"decision-making":[173],"create":[175],"adaptive,":[176],"proactive,":[177],"efficient":[179],"systems":[181],"suitable":[182],"long-term":[184],"industrial":[185],"deployment.":[186]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2026-02-07T00:00:00"}