{"id":"https://openalex.org/W2600181989","doi":"https://doi.org/10.1061/(asce)cp.1943-5487.0000668","title":"Novel Sparse Bayesian Learning and Its Application to Ground Motion Pattern Recognition","display_name":"Novel Sparse Bayesian Learning and Its Application to Ground Motion Pattern Recognition","publication_year":2017,"publication_date":"2017-03-31","ids":{"openalex":"https://openalex.org/W2600181989","doi":"https://doi.org/10.1061/(asce)cp.1943-5487.0000668","mag":"2600181989"},"language":"en","primary_location":{"id":"doi:10.1061/(asce)cp.1943-5487.0000668","is_oa":false,"landing_page_url":"https://doi.org/10.1061/(asce)cp.1943-5487.0000668","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":"https://openalex.org/A5091866062","display_name":"He\u2010Qing Mu","orcid":"https://orcid.org/0000-0001-8358-6032"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"He-Qing Mu","raw_affiliation_strings":["Associate Professor, School of Civil Engineering and Transportation, South China Univ. of Technology, Guangzhou 510640, P.R. China; Associate Professor, State Key Laboratory of Subtropical Building Science, South China Univ. of Technology, Guangzhou 510640, P.R. China","Associate Professor, School of Civil Engineering and Transportation, South China Univ. of Technology, Guangzhou 510640, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Associate Professor, School of Civil Engineering and Transportation, South China Univ. of Technology, Guangzhou 510640, P.R. China; Associate Professor, State Key Laboratory of Subtropical Building Science, South China Univ. of Technology, Guangzhou 510640, P.R. China","institution_ids":["https://openalex.org/I90610280"]},{"raw_affiliation_string":"Associate Professor, School of Civil Engineering and Transportation, South China Univ. of Technology, Guangzhou 510640, P.R. China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083222672","display_name":"Ka\u2010Veng Yuen","orcid":"https://orcid.org/0000-0002-1755-6668"},"institutions":[{"id":"https://openalex.org/I111950717","display_name":"Macau University of Science and Technology","ror":"https://ror.org/03jqs2n27","country_code":"MO","type":"education","lineage":["https://openalex.org/I111950717","https://openalex.org/I4391767947"]},{"id":"https://openalex.org/I204512498","display_name":"University of Macau","ror":"https://ror.org/01r4q9n85","country_code":"MO","type":"education","lineage":["https://openalex.org/I204512498"]}],"countries":["MO"],"is_corresponding":true,"raw_author_name":"Ka-Veng Yuen","raw_affiliation_strings":["Professor, Faculty of Science and Technology, Univ. of Macau, Macao, China (corresponding author)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Professor, Faculty of Science and Technology, Univ. of Macau, Macao, China (corresponding author)","institution_ids":["https://openalex.org/I111950717","https://openalex.org/I204512498"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5083222672"],"corresponding_institution_ids":["https://openalex.org/I111950717","https://openalex.org/I204512498"],"apc_list":null,"apc_paid":null,"fwci":2.2451,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.85925001,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"31","issue":"5","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10160","display_name":"Seismic Performance and Analysis","score":0.9998000264167786,"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/T10160","display_name":"Seismic Performance and Analysis","score":0.9998000264167786,"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":0.9991999864578247,"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/T12293","display_name":"Dam Engineering and Safety","score":0.9911999702453613,"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/robustness","display_name":"Robustness (evolution)","score":0.6359637379646301},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5984464883804321},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5677594542503357},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49788427352905273},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4888278841972351},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.443086177110672},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4280281662940979},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.40997403860092163},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34564778208732605}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6359637379646301},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5984464883804321},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5677594542503357},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49788427352905273},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4888278841972351},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.443086177110672},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4280281662940979},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.40997403860092163},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34564778208732605},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1061/(asce)cp.1943-5487.0000668","is_oa":false,"landing_page_url":"https://doi.org/10.1061/(asce)cp.1943-5487.0000668","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":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":70,"referenced_works":["https://openalex.org/W17920237","https://openalex.org/W60278794","https://openalex.org/W1589547593","https://openalex.org/W1633751774","https://openalex.org/W1648445109","https://openalex.org/W1723619723","https://openalex.org/W1809885800","https://openalex.org/W1882988315","https://openalex.org/W1928379838","https://openalex.org/W1968842194","https://openalex.org/W1968985853","https://openalex.org/W1972023825","https://openalex.org/W1972451532","https://openalex.org/W1973149880","https://openalex.org/W1974964201","https://openalex.org/W1989769999","https://openalex.org/W1992602466","https://openalex.org/W1992649778","https://openalex.org/W1994914099","https://openalex.org/W2003202931","https://openalex.org/W2008206416","https://openalex.org/W2011847976","https://openalex.org/W2014235350","https://openalex.org/W2017711438","https://openalex.org/W2020805690","https://openalex.org/W2021497665","https://openalex.org/W2035315685","https://openalex.org/W2039488112","https://openalex.org/W2040928062","https://openalex.org/W2041342522","https://openalex.org/W2045363046","https://openalex.org/W2048997388","https://openalex.org/W2062258832","https://openalex.org/W2063191173","https://openalex.org/W2072853624","https://openalex.org/W2081518822","https://openalex.org/W2083815608","https://openalex.org/W2087752560","https://openalex.org/W2090598788","https://openalex.org/W2105544002","https://openalex.org/W2108118704","https://openalex.org/W2113523889","https://openalex.org/W2115606304","https://openalex.org/W2115802677","https://openalex.org/W2116326094","https://openalex.org/W2121521319","https://openalex.org/W2134181680","https://openalex.org/W2146859678","https://openalex.org/W2148154358","https://openalex.org/W2151443132","https://openalex.org/W2154400911","https://openalex.org/W2154492927","https://openalex.org/W2157501616","https://openalex.org/W2158118517","https://openalex.org/W2162824461","https://openalex.org/W2169643310","https://openalex.org/W2171577279","https://openalex.org/W2182806504","https://openalex.org/W2290750851","https://openalex.org/W2503126809","https://openalex.org/W2510300149","https://openalex.org/W2530035167","https://openalex.org/W2911546748","https://openalex.org/W3124399172","https://openalex.org/W4237716531","https://openalex.org/W4237842153","https://openalex.org/W4285719527","https://openalex.org/W4300029251","https://openalex.org/W6676940277","https://openalex.org/W6724408841"],"related_works":["https://openalex.org/W2407375987","https://openalex.org/W2505726097","https://openalex.org/W2950975704","https://openalex.org/W2010643158","https://openalex.org/W3049691116","https://openalex.org/W2106867672","https://openalex.org/W4310268968","https://openalex.org/W3081214562","https://openalex.org/W2753713401","https://openalex.org/W2053745677"],"abstract_inverted_index":{"A":[0],"novel":[1],"sparse":[2],"Bayesian":[3],"learning":[4,123],"for":[5,15,53],"correlated":[6],"error":[7,117],"(SBL-CE)":[8],"algorithm":[9,39,81,143,172],"is":[10,40,111,125,144,163,173,182],"proposed":[11,37,79,141,170],"to":[12,42,67,97,135,147,176],"automatically":[13],"search":[14,103],"an":[16],"optimal":[17,49,101],"model":[18,50,102,167],"class":[19],"with":[20,197],"relevance":[21],"features":[22,110],"in":[23,46,90,120,156,192],"regression":[24,87],"problems":[25],"of":[26,72,75,108,151,160,184,189],"pattern":[27,56,138,188],"recognition":[28],"based":[29],"on":[30,85],"measured":[31],"data":[32],"and":[33,200],"extracted":[34,76,109],"features.":[35,77],"The":[36,78,140],"SBL-CE":[38,80,142,171],"designed":[41],"overcome":[43],"the":[44,47,86,91,106,115,121,128,157,166,169,177,187,193],"disadvantage":[45],"traditional":[48,122,178],"searching":[51],"approach":[52,124],"ground":[54,92,136,153,190],"motion":[55,93,137,154,191],"recognition,":[57],"which":[58],"requires":[59],"a":[60,69,99,149],"huge":[61],"or":[62],"even":[63,104],"intractable":[64],"computational":[65],"effort":[66],"examine":[68],"large":[70],"number":[71,107],"different":[73],"combinations":[74],"introduces":[82],"sophisticated":[83],"hyperparameterization":[84],"parameter":[88],"vector":[89],"prediction":[94,116],"model,":[95],"aiming":[96],"conduct":[98],"continuous":[100],"when":[105],"large.":[112],"In":[113],"addition,":[114],"independence":[118],"assumption":[119],"relaxed,":[126],"so":[127],"derived":[129],"optimization":[130],"strategy":[131],"can":[132],"be":[133],"applied":[134],"recognition.":[139],"then":[145],"used":[146],"analyze":[148],"database":[150],"strong":[152],"records":[155],"Tangshan":[158],"region":[159,196],"China.":[161],"It":[162],"shown":[164],"that":[165],"by":[168],"superior":[174],"compared":[175],"models":[179],"because":[180],"it":[181],"capable":[183],"properly":[185],"recognizing":[186],"target":[194],"seismic":[195],"high":[198],"accuracy":[199],"robustness.":[201]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":6}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
