{"id":"https://openalex.org/W3205156928","doi":"https://doi.org/10.1145/3474963.3474982","title":"Shallow-neural-network Optimization for Predicting Plasticity Index of Loess with Cone Penetration Test Data","display_name":"Shallow-neural-network Optimization for Predicting Plasticity Index of Loess with Cone Penetration Test Data","publication_year":2021,"publication_date":"2021-06-25","ids":{"openalex":"https://openalex.org/W3205156928","doi":"https://doi.org/10.1145/3474963.3474982","mag":"3205156928"},"language":"en","primary_location":{"id":"doi:10.1145/3474963.3474982","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474963.3474982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 The 13th International Conference on Computer Modeling and Simulation","raw_type":"proceedings-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/A5100440549","display_name":"Siyuan Wang","orcid":"https://orcid.org/0000-0002-5506-7451"},"institutions":[{"id":"https://openalex.org/I23171815","display_name":"Zhengzhou University of Light Industry","ror":"https://ror.org/05fwr8z16","country_code":"CN","type":"education","lineage":["https://openalex.org/I23171815"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Siyuan Wang","raw_affiliation_strings":["Zhengzhou Univ.of Light Industry, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhengzhou Univ.of Light Industry, China","institution_ids":["https://openalex.org/I23171815"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101601884","display_name":"Xinjian Wang","orcid":"https://orcid.org/0000-0001-7435-3165"},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinjian Wang","raw_affiliation_strings":["North China Univ.of Water Resources and Electric power, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North China Univ.of Water Resources and Electric power, China","institution_ids":["https://openalex.org/I198645480"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101500035","display_name":"Zhongnan Wang","orcid":"https://orcid.org/0000-0001-5704-3042"},"institutions":[{"id":"https://openalex.org/I198645480","display_name":"North China University of Water Resources and Electric Power","ror":"https://ror.org/03acrzv41","country_code":"CN","type":"education","lineage":["https://openalex.org/I198645480"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongnan Wang","raw_affiliation_strings":["North China Univ.of Water Resources and Electric power, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"North China Univ.of Water Resources and Electric power, China","institution_ids":["https://openalex.org/I198645480"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17279851,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"133","last_page":"139"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10233","display_name":"Geotechnical Engineering and Soil Mechanics","score":0.9987999796867371,"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/T10233","display_name":"Geotechnical Engineering and Soil Mechanics","score":0.9987999796867371,"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/T11046","display_name":"Geotechnical Engineering and Analysis","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T10716","display_name":"Soil and Unsaturated Flow","score":0.9983000159263611,"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/overfitting","display_name":"Overfitting","score":0.8782626390457153},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6393563747406006},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5385997891426086},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4937950074672699},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.47933873534202576},{"id":"https://openalex.org/keywords/cone-penetration-test","display_name":"Cone penetration test","score":0.4683147370815277},{"id":"https://openalex.org/keywords/plasticity","display_name":"Plasticity","score":0.45901620388031006},{"id":"https://openalex.org/keywords/test-set","display_name":"Test set","score":0.44326621294021606},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4158550798892975},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40978485345840454},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34270021319389343},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27854955196380615},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23842933773994446},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21826589107513428},{"id":"https://openalex.org/keywords/geotechnical-engineering","display_name":"Geotechnical engineering","score":0.20427197217941284}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8782626390457153},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6393563747406006},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5385997891426086},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4937950074672699},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.47933873534202576},{"id":"https://openalex.org/C176071119","wikidata":"https://www.wikidata.org/wiki/Q1261042","display_name":"Cone penetration test","level":2,"score":0.4683147370815277},{"id":"https://openalex.org/C79186407","wikidata":"https://www.wikidata.org/wiki/Q472074","display_name":"Plasticity","level":2,"score":0.45901620388031006},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.44326621294021606},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4158550798892975},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40978485345840454},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34270021319389343},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27854955196380615},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23842933773994446},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21826589107513428},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.20427197217941284},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3474963.3474982","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474963.3474982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 The 13th International Conference on Computer Modeling and Simulation","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[{"id":"https://openalex.org/G6808183098","display_name":null,"funder_award_id":"201308410082","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"}],"funders":[{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1558271586","https://openalex.org/W1560731656","https://openalex.org/W1992706817","https://openalex.org/W2015573707","https://openalex.org/W2052007416","https://openalex.org/W2134278855","https://openalex.org/W2152935540","https://openalex.org/W2157831143","https://openalex.org/W2167912586","https://openalex.org/W2272514285","https://openalex.org/W2527105207","https://openalex.org/W2605625639","https://openalex.org/W2792600884","https://openalex.org/W2796532835","https://openalex.org/W2897659968","https://openalex.org/W2994043525","https://openalex.org/W3124921603"],"related_works":["https://openalex.org/W3208882810","https://openalex.org/W2972862903","https://openalex.org/W3099765033","https://openalex.org/W2921259037","https://openalex.org/W2970699417","https://openalex.org/W4288419306","https://openalex.org/W2987302549","https://openalex.org/W2396366225","https://openalex.org/W2384344231","https://openalex.org/W3126813375"],"abstract_inverted_index":{"Plasticity":[0],"index":[1,62],"is":[2,131,215],"essential":[3],"for":[4,71,157],"engineering":[5],"applications,":[6],"obtaining":[7],"which":[8,191],"would":[9],"be":[10],"carried":[11],"out":[12],"from":[13,86],"situ-fields":[14],"to":[15,109,153],"the":[16,114,162,168,176,195,210],"laboratory":[17,73,98],"costly":[18],"and":[19,28,37,40,46,83,100,112,120,149,171,175,179,187,213],"time-consuming.":[20],"Cone":[21],"penetration":[22],"tests":[23,102],"(CPTs),":[24],"fast,":[25],"low-cost,":[26],"reliable":[27],"output":[29],"near-continuous":[30],"measurement,":[31],"are":[32,184],"widely":[33],"used":[34,108],"in":[35,88,92,198],"geological":[36],"geotechnical":[38],"engineering,":[39],"shallow":[41],"neural":[42,68],"networks":[43,69],"can":[44],"learn":[45],"build":[47],"models":[48,117],"of":[49,58,80,97],"complex":[50],"nonlinear":[51],"relationships.":[52],"This":[53],"paper":[54],"presents":[55],"a":[56,77,200],"methodology":[57],"predicting":[59],"soil":[60],"plasticity":[61],"by":[63,134],"CPT":[64,101],"using":[65],"optimized":[66],"artificial":[67],"(SNNs)":[70],"reducing":[72],"work":[74],"that":[75],"represents":[76],"significant":[78],"saving":[79],"both":[81],"time":[82],"money.":[84],"Gathered":[85],"fields":[87],"Western":[89],"Henan":[90],"province":[91],"central":[93],"China,":[94],"237":[95],"sets":[96],"results":[99,163],"divided":[103],"into":[104],"20":[105,154],"groups":[106],"were":[107],"train,":[110],"test,":[111],"validate":[113],"optimization":[115,139],"ANN":[116],"with":[118,164,199],"single":[119,201],"double":[121,165],"hidden":[122,159,166],"layers.":[123,160],"A":[124],"criterion":[125],"ensuring":[126],"without":[127],"underfitting":[128],"or":[129],"overfitting":[130],"set":[132],"up":[133],"regression":[135,173,211],"coefficient":[136],"distribution.":[137],"The":[138,207],"covers":[140],"12":[141],"train":[142],"functions,":[143,146],"four":[144],"process":[145],"divide":[147,150],"functions":[148],"models,":[151],"2":[152],"neurons":[155],"selected":[156],"two":[158],"Of":[161],"layers,":[167],"largest":[169],"minimum":[170],"2-norm":[172,180],"coefficients":[174],"least":[177],"maximum":[178],"mean":[181],"square":[182],"errors":[183],"0.640,":[185],"1.318":[186],"0.775,":[188],"1.078":[189],"individually,":[190],"distinctly":[192],"larger":[193],"than":[194],"corresponding":[196],"values":[197,212],"layer,":[202],"thus":[203],"indicates":[204],"improved":[205],"performances.":[206],"influence":[208],"on":[209],"MSEs":[214],"presented.":[216]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}