{"id":"https://openalex.org/W7165361719","doi":"https://doi.org/10.48550/arxiv.2606.20015","title":"Adaptive Distance-Aware Trunk Deep Operator Learning for Long-Span Roadway Bridges","display_name":"Adaptive Distance-Aware Trunk Deep Operator Learning for Long-Span Roadway Bridges","publication_year":2026,"publication_date":"2026-06-18","ids":{"openalex":"https://openalex.org/W7165361719","doi":"https://doi.org/10.48550/arxiv.2606.20015"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.20015","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20015","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":null,"license_id":null,"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.2606.20015","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138973370","display_name":"Bilal Ahmed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmed, Bilal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015479227","display_name":"Diab Abueidda","orcid":"https://orcid.org/0000-0003-3594-2455"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abueidda, Diab W.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137605312","display_name":"Waleed El-Sekelly","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"El-Sekelly, Waleed","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137590393","display_name":"Tarek Abdoun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdoun, Tarek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5061440922","display_name":"Mostafa E. Mobasher","orcid":"https://orcid.org/0000-0002-5108-4713"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mobasher, Mostafa E.","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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.8672999739646912,"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":0.8672999739646912,"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/T12487","display_name":"Structural Engineering and Vibration Analysis","score":0.027000000700354576,"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/T10842","display_name":"Railway Engineering and Dynamics","score":0.02319999970495701,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/benchmark","display_name":"Benchmark (surveying)","score":0.5042999982833862},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.47530001401901245},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.4417000114917755},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.41690000891685486},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4059999883174896},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.38119998574256897},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.36579999327659607},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3418999910354614},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.33230000734329224}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6067000031471252},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5042999982833862},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.47530001401901245},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4417000114917755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43639999628067017},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.351500004529953},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C2776247918","wikidata":"https://www.wikidata.org/wiki/Q1423713","display_name":"Structural health monitoring","level":2,"score":0.33160001039505005},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.328000009059906},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.32440000772476196},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.3133000135421753},{"id":"https://openalex.org/C88282795","wikidata":"https://www.wikidata.org/wiki/Q2448108","display_name":"Structural system","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3052000105381012},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.28439998626708984},{"id":"https://openalex.org/C206423108","wikidata":"https://www.wikidata.org/wiki/Q214728","display_name":"B\u00e9zier curve","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C2777831296","wikidata":"https://www.wikidata.org/wiki/Q12518","display_name":"Tower","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.25429999828338623},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.20015","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20015","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.20015","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20015","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.4507835805416107,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Long-span":[0],"roadway":[1],"bridges":[2],"exhibit":[3],"highly":[4],"localized":[5,36,49],"structural":[6,25,50,75,96,121],"responses":[7],"under":[8,223],"vehicular":[9,225],"loading,":[10],"making":[11],"repeated":[12],"FE":[13],"analysis":[14,234],"computationally":[15],"expensive":[16],"for":[17,48,231],"applications":[18],"such":[19],"as":[20],"influence":[21,76,218,221],"surface":[22],"generation":[23,214],"and":[24,95,159,220,235],"digital":[26,236],"twins.":[27],"Existing":[28],"SciML":[29],"approaches":[30],"struggle":[31],"to":[32,72,115,118,200],"accurately":[33],"capture":[34],"these":[35],"responses.":[37],"To":[38,123],"address":[39],"this":[40,42],"challenge,":[41],"study":[43],"proposes":[44],"an":[45],"adaptive-trunk":[46],"DeepONet":[47],"response":[51,127,181],"prediction":[52],"in":[53],"large-scale":[54,232],"bridge":[55,157,233],"systems.":[56],"The":[57,78,148],"framework":[58,150,211],"dynamically":[59],"constructs":[60],"a":[61,66,105,132,155],"load-dependent":[62],"learning":[63],"domain":[64],"using":[65,84,131],"KNN":[67],"strategy,":[68],"allowing":[69],"the":[70,89,93,119,139,160,167,179,191,195,210],"network":[71,80],"focus":[73],"on":[74,153],"zones.":[77],"trunk":[79],"is":[81,102,151,198],"further":[82],"enhanced":[83],"distance-aware":[85],"features":[86],"that":[87,137,166],"encode":[88],"geometric":[90],"relationship":[91],"between":[92],"load":[94],"nodes.":[97],"A":[98],"physics-based":[99],"full-field":[100,185,216],"reconstruction":[101,193],"incorporated":[103],"through":[104],"stiffness-informed":[106],"Schur":[107],"complement":[108],"formulation,":[109],"enabling":[110],"predictions":[111],"at":[112],"adaptive":[113],"nodes":[114],"be":[116],"extended":[117],"entire":[120],"domain.":[122],"enable":[124],"scalable":[125],"training,":[126],"data":[128],"are":[129],"generated":[130],"reduced-order":[133],"equivalent":[134],"shell":[135],"model":[136,158],"preserves":[138],"dominant":[140],"global":[141],"behavior":[142],"while":[143,177],"significantly":[144],"reducing":[145,178],"computational":[146],"cost.":[147],"proposed":[149],"validated":[152],"both":[154],"benchmark":[156],"real-world":[161],"Mussafah":[162],"Bridge.":[163],"Results":[164],"show":[165],"method":[168],"achieves":[169],"FEM-level":[170],"accuracy":[171],"with":[172],"relative":[173],"errors":[174],"below":[175],"5%,":[176],"total":[180],"evaluation":[182],"time":[183],"(including":[184],"reconstruction)":[186],"by":[187],"approximately":[188],"60x;":[189],"excluding":[190],"post-processing":[192],"step,":[194],"AD-DeepONet":[196],"inference":[197],"up":[199],"four":[201],"orders":[202],"of":[203,215],"magnitude":[204],"faster":[205],"than":[206],"FEM.":[207],"In":[208],"addition,":[209],"enables":[212],"rapid":[213],"responses,":[217],"lines,":[219],"surfaces":[222],"arbitrary":[224],"loading":[226],"configurations,":[227],"demonstrating":[228],"strong":[229],"potential":[230],"twin":[237],"applications.":[238]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-20T00:00:00"}