{"id":"https://openalex.org/W7082306993","doi":"https://doi.org/10.1061/jccee5.cpeng-6822","title":"Multitask Sparse Bayesian Machine Learning with Applications in Modeling of Seismic Attenuation and Clay Parameters with Small Data Sets","display_name":"Multitask Sparse Bayesian Machine Learning with Applications in Modeling of Seismic Attenuation and Clay Parameters with Small Data Sets","publication_year":2025,"publication_date":"2025-09-19","ids":{"openalex":"https://openalex.org/W7082306993","doi":"https://doi.org/10.1061/jccee5.cpeng-6822"},"language":"en","primary_location":{"id":"doi:10.1061/jccee5.cpeng-6822","is_oa":false,"landing_page_url":"https://doi.org/10.1061/jccee5.cpeng-6822","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":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jingze Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingze Gao","raw_affiliation_strings":["Harbin Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shiyin Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiyin Wei","raw_affiliation_strings":["Harbin Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yong Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Huang","raw_affiliation_strings":["Harbin Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":1.396,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.8853414,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"40","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6478000283241272,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6478000283241272,"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/T13067","display_name":"Geological Modeling and Analysis","score":0.025100000202655792,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14311","display_name":"Electrical and Electromagnetic Research","score":0.021700000390410423,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5199999809265137},{"id":"https://openalex.org/keywords/small-data","display_name":"Small data","score":0.4767000079154968},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4059999883174896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39959999918937683},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.38659998774528503},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.33230000734329224},{"id":"https://openalex.org/keywords/attenuation","display_name":"Attenuation","score":0.3303999900817871}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5785999894142151},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5702000260353088},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5199999809265137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5121999979019165},{"id":"https://openalex.org/C2779280203","wikidata":"https://www.wikidata.org/wiki/Q17121211","display_name":"Small data","level":2,"score":0.4767000079154968},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4059999883174896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39959999918937683},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.38659998774528503},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3652999997138977},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C184652730","wikidata":"https://www.wikidata.org/wiki/Q2357982","display_name":"Attenuation","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.26089999079704285},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2581999897956848}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1061/jccee5.cpeng-6822","is_oa":false,"landing_page_url":"https://doi.org/10.1061/jccee5.cpeng-6822","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":111,"referenced_works":["https://openalex.org/W60278794","https://openalex.org/W1648445109","https://openalex.org/W1859665057","https://openalex.org/W1928379838","https://openalex.org/W1941412501","https://openalex.org/W1963790253","https://openalex.org/W1974774078","https://openalex.org/W1977568055","https://openalex.org/W1987634820","https://openalex.org/W1991031161","https://openalex.org/W1992088291","https://openalex.org/W1996640396","https://openalex.org/W2004941813","https://openalex.org/W2015940557","https://openalex.org/W2016446422","https://openalex.org/W2023965427","https://openalex.org/W2025130480","https://openalex.org/W2029590430","https://openalex.org/W2040647667","https://openalex.org/W2062258832","https://openalex.org/W2065180801","https://openalex.org/W2078419860","https://openalex.org/W2082179468","https://openalex.org/W2087752560","https://openalex.org/W2109563136","https://openalex.org/W2111072639","https://openalex.org/W2119821739","https://openalex.org/W2135046866","https://openalex.org/W2145856765","https://openalex.org/W2165761389","https://openalex.org/W2166670624","https://openalex.org/W2443985597","https://openalex.org/W2478899349","https://openalex.org/W2508782677","https://openalex.org/W2517608900","https://openalex.org/W2526755141","https://openalex.org/W2527141715","https://openalex.org/W2547960182","https://openalex.org/W2554277894","https://openalex.org/W2577761826","https://openalex.org/W2600181989","https://openalex.org/W2738226240","https://openalex.org/W2753709519","https://openalex.org/W2791210712","https://openalex.org/W2793513544","https://openalex.org/W2888180323","https://openalex.org/W2903155537","https://openalex.org/W2913219687","https://openalex.org/W2913340405","https://openalex.org/W2915864118","https://openalex.org/W2919115771","https://openalex.org/W2919946988","https://openalex.org/W2931365531","https://openalex.org/W2970199871","https://openalex.org/W2971230250","https://openalex.org/W2983840653","https://openalex.org/W2999772316","https://openalex.org/W3006472956","https://openalex.org/W3015830056","https://openalex.org/W3022781815","https://openalex.org/W3029143162","https://openalex.org/W3033652307","https://openalex.org/W3033856829","https://openalex.org/W3035750201","https://openalex.org/W3035846893","https://openalex.org/W3110031974","https://openalex.org/W3114136449","https://openalex.org/W3114155316","https://openalex.org/W3156339798","https://openalex.org/W3159331633","https://openalex.org/W3159788356","https://openalex.org/W3161361986","https://openalex.org/W3175227713","https://openalex.org/W3183678589","https://openalex.org/W3197712350","https://openalex.org/W3199513339","https://openalex.org/W3206169934","https://openalex.org/W3206998631","https://openalex.org/W3209708922","https://openalex.org/W4225403255","https://openalex.org/W4226410129","https://openalex.org/W4246210153","https://openalex.org/W4281779931","https://openalex.org/W4281933943","https://openalex.org/W4281958889","https://openalex.org/W4283587983","https://openalex.org/W4299551239","https://openalex.org/W4308737249","https://openalex.org/W4312068531","https://openalex.org/W4317214206","https://openalex.org/W4317693756","https://openalex.org/W4319590843","https://openalex.org/W4321113524","https://openalex.org/W4323817161","https://openalex.org/W4360979822","https://openalex.org/W4366254448","https://openalex.org/W4378216200","https://openalex.org/W4378840198","https://openalex.org/W4380077532","https://openalex.org/W4383265334","https://openalex.org/W4383619442","https://openalex.org/W4386633921","https://openalex.org/W4387123922","https://openalex.org/W4387418631","https://openalex.org/W4388773795","https://openalex.org/W4389781255","https://openalex.org/W4390737410","https://openalex.org/W4400968302","https://openalex.org/W4401047452","https://openalex.org/W4404552028","https://openalex.org/W4407029457"],"related_works":[],"abstract_inverted_index":{"Multitask":[0],"sparse":[1,23,108],"Bayesian":[2,94,109],"learning":[3,21,101,111],"(SBL)":[4],"has":[5],"received":[6],"extensive":[7],"attention":[8],"because":[9],"it":[10,68],"makes":[11],"full":[12],"use":[13,72],"of":[14,17,22,31,39,119,123,143,163,169,173,183,196,214,232],"multiple":[15,26,121],"groups":[16,122],"data":[18,155,208],"by":[19,98],"joint":[20],"representations":[24],"for":[25,147,228],"models.":[27],"It":[28],"is":[29,46,69,117,198,210,224],"capable":[30,118],"producing":[32],"superior":[33],"performance,":[34],"especially":[35,204],"in":[36,165,236],"the":[37,73,84,99,141,144,170,174,194,206,215,220],"case":[38],"insufficient":[40],"training":[41,154,207],"data.":[42],"However,":[43],"multitask":[44,75,86,107],"SBL":[45,76,87],"only":[47],"applicable":[48],"to":[49,71,89],"regression":[50,66,149,234],"problems":[51,150,235],"that":[52,193,219],"have":[53],"a":[54,90,106,152,187,225,229],"linear":[55],"relationship":[56],"between":[57],"primary":[58],"inputs":[59],"and":[60,104,176,179,238],"target":[61],"outputs.":[62],"For":[63],"general":[64],"nonlinear":[65,124,148,233],"problems,":[67],"infeasible":[70],"existing":[74],"framework":[77,88],"directly.":[78],"In":[79],"this":[80],"study,":[81],"we":[82],"extend":[83],"traditional":[85],"feature-based,":[91],"single":[92],"hidden-layer":[93],"neural":[95],"network":[96],"inspired":[97],"extreme":[100,110],"machine":[102,112],"(ELM)":[103],"propose":[105],"(MT-SBELM).":[113],"The":[114,190,212],"proposed":[115,145,221],"method":[116],"modeling":[120,168,180],"functions":[125],"with":[126,151],"satisfactory":[127],"precision":[128],"from":[129,134],"noisy":[130],"incomplete":[131],"information":[132],"measured":[133],"similar":[135],"scenes.":[136],"A":[137],"numerical":[138],"example":[139],"validates":[140],"effectiveness":[142],"MT-SBELM":[146,164,197,222],"small":[153],"set.":[156],"We":[157],"also":[158],"illustrate":[159],"two":[160],"potential":[161],"applications":[162,217],"civil":[166],"engineering:":[167],"seismic":[171],"attenuation":[172],"mainshock":[175],"subsequent":[177],"aftershocks,":[178],"multivariate":[181],"correlation":[182],"clay":[184],"parameters":[185],"at":[186],"field":[188],"site.":[189],"results":[191],"demonstrate":[192],"performance":[195],"significantly":[199],"better":[200],"than":[201],"single-task":[202],"SBELM,":[203],"when":[205],"set":[209],"small.":[211],"success":[213],"presented":[216],"suggests":[218],"algorithm":[223],"promising":[226],"one":[227],"broad":[230],"range":[231],"science":[237],"technology.":[239]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-14T06:11:07.267592","created_date":"2025-10-10T00:00:00"}
