{"id":"https://openalex.org/W4413394084","doi":"https://doi.org/10.1109/access.2025.3601409","title":"Analysis of Seismic Elastic Steel Structures Using a New Hybrid Optimization Deep Learning Model","display_name":"Analysis of Seismic Elastic Steel Structures Using a New Hybrid Optimization Deep Learning Model","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4413394084","doi":"https://doi.org/10.1109/access.2025.3601409"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3601409","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3601409","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3601409","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ping Li","orcid":"https://orcid.org/0009-0002-4880-5839"},"institutions":[{"id":"https://openalex.org/I3132882781","display_name":"Longdong University","ror":"https://ror.org/03wcn4h12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3132882781"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ping Li","raw_affiliation_strings":["College of Civil Engineering, Longdong University, Qingyang, China"],"raw_orcid":"https://orcid.org/0009-0002-4880-5839","affiliations":[{"raw_affiliation_string":"College of Civil Engineering, Longdong University, Qingyang, China","institution_ids":["https://openalex.org/I3132882781"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011614212","display_name":"Wanfeng Liu","orcid":"https://orcid.org/0000-0002-3789-1113"},"institutions":[{"id":"https://openalex.org/I3132882781","display_name":"Longdong University","ror":"https://ror.org/03wcn4h12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3132882781"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanfeng Liu","raw_affiliation_strings":["College of Civil Engineering, Longdong University, Qingyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Civil Engineering, Longdong University, Qingyang, China","institution_ids":["https://openalex.org/I3132882781"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113862479","display_name":"Tiecheng Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I3132882781","display_name":"Longdong University","ror":"https://ror.org/03wcn4h12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3132882781"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tiecheng Yan","raw_affiliation_strings":["College of Civil Engineering, Longdong University, Qingyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Civil Engineering, Longdong University, Qingyang, China","institution_ids":["https://openalex.org/I3132882781"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3132882781"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.2130804,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"147714","last_page":"147728"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9261999726295471,"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":0.9261999726295471,"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/computer-science","display_name":"Computer science","score":0.6255147457122803},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49193263053894043},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.3481285870075226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3274521827697754},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11693757772445679}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6255147457122803},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49193263053894043},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.3481285870075226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3274521827697754},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11693757772445679}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3601409","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3601409","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2f1d8a57bcf146849322ab0bcd38e4e0","is_oa":true,"landing_page_url":"https://doaj.org/article/2f1d8a57bcf146849322ab0bcd38e4e0","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 147714-147728 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3601409","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3601409","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W3147309678","https://openalex.org/W3195285932","https://openalex.org/W3200700157","https://openalex.org/W3208642197","https://openalex.org/W3214961090","https://openalex.org/W4220862819","https://openalex.org/W4224441352","https://openalex.org/W4285116075","https://openalex.org/W4285121591","https://openalex.org/W4289519204","https://openalex.org/W4290714301","https://openalex.org/W4292114702","https://openalex.org/W4293370789","https://openalex.org/W4303709937","https://openalex.org/W4308916695","https://openalex.org/W4312728416","https://openalex.org/W4317796687","https://openalex.org/W4319777846","https://openalex.org/W4321372691","https://openalex.org/W4323338516","https://openalex.org/W4323661854","https://openalex.org/W4360615697","https://openalex.org/W4362496432","https://openalex.org/W4380786758","https://openalex.org/W4385356417","https://openalex.org/W4385686902","https://openalex.org/W4386615496","https://openalex.org/W4389212233","https://openalex.org/W4391791398","https://openalex.org/W4396642366","https://openalex.org/W4399050145","https://openalex.org/W4399712225","https://openalex.org/W4405672241","https://openalex.org/W4407499312"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W3086377361"],"abstract_inverted_index":{"In":[0,92,115,174],"response":[1],"to":[2,98,160],"the":[3,50,71,80,96,119,161,176,180],"insufficient":[4],"performance":[5],"of":[6,54,106,171,182,211],"traditional":[7,68,113,183],"seismic":[8,209],"elastic":[9],"analysis":[10,210],"models":[11],"for":[12,131,167,208],"steel":[13,107,132,213],"structures,":[14],"this":[15,55],"study":[16],"proposes":[17],"a":[18,26,88,203],"deep":[19],"layered":[20],"feature":[21,195],"learning":[22],"network":[23],"model":[24,56,120,178],"in":[25,57,185],"mixed":[27],"optimization":[28,199],"point":[29],"set":[30],"metric":[31],"space":[32],"by":[33,83,155],"combining":[34],"cross":[35],"attention":[36],"mechanism,":[37],"graph":[38],"neural":[39],"network,":[40],"and":[41,102,134,139,145,157,190,197,205],"random":[42],"sampling":[43],"consistency":[44],"algorithm.":[45],"The":[46,141],"experiment":[47],"showed":[48],"that":[49],"average":[51],"F1":[52],"score":[53],"cloud":[58],"matching":[59,72],"tasks":[60],"was":[61,64,76,137],"0.919,":[62],"which":[63,164],"16.5%-22.6%":[65],"higher":[66,78,111],"than":[67,79,85,112],"methods.":[69,114],"Moreover,":[70],"success":[73],"rate":[74],"(55.43%)":[75],"still":[77],"comparison":[81,162],"method":[82],"more":[84],"15%":[86],"at":[87,150],"50%":[89],"occlusion":[90],"rate.":[91],"semantic":[93],"segmentation":[94],"tasks,":[95],"intersection":[97],"union":[99],"ratio":[100],"(0.798)":[101],"Dice":[103],"coefficient":[104],"(0.879)":[105],"pipe":[108],"components":[109],"were":[110,153],"actual":[116],"scenario":[117],"testing,":[118],"achieved":[121],"sub-millimeter":[122],"level":[123],"deformation":[124,128,191],"detection":[125,129],"(the":[126],"minimum":[127],"amount":[130],"pipes":[133],"welded":[135],"balls":[136],"0.44mm":[138],"0.31mm).":[140],"generalization":[142],"error":[143],"(0.06mm)":[144],"memory":[146],"peak":[147],"value":[148],"(11.3GB":[149],"10K":[151],"points)":[152],"reduced":[154],"76.9%":[156],"53.9%":[158],"compared":[159],"method,":[163],"is":[165],"suitable":[166],"real-time":[168],"monitoring":[169],"needs":[170],"edge":[172],"devices.":[173],"summary,":[175],"proposed":[177],"addresses":[179],"shortcomings":[181],"methods":[184],"dynamic":[186],"interaction,":[187],"noise":[188],"suppression,":[189],"sensitivity":[192],"through":[193],"multimodal":[194],"fusion":[196],"hybrid":[198],"strategies.":[200],"This":[201],"provides":[202],"high-precision":[204],"low-energy":[206],"solution":[207],"complex":[212],"structures.":[214]},"counts_by_year":[],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
