{"id":"https://openalex.org/W7114775114","doi":"https://doi.org/10.48550/arxiv.2512.08343","title":"Soil Compaction Parameters Prediction Based on Automated Machine Learning Approach","display_name":"Soil Compaction Parameters Prediction Based on Automated Machine Learning Approach","publication_year":2025,"publication_date":"2025-12-09","ids":{"openalex":"https://openalex.org/W7114775114","doi":"https://doi.org/10.48550/arxiv.2512.08343"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2512.08343","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.08343","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2512.08343","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Erden, Caner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Erden, Caner","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Demir, Alparslan Serhat","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Demir, Alparslan Serhat","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Kokcam, Abdullah Hulusi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kokcam, Abdullah Hulusi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Kurnaz, Talas Fikret","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kurnaz, Talas Fikret","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Dagdeviren, Ugur","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dagdeviren, Ugur","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/T12293","display_name":"Dam Engineering and Safety","score":0.13850000500679016,"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/T12293","display_name":"Dam Engineering and Safety","score":0.13850000500679016,"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/T10687","display_name":"Innovative concrete reinforcement materials","score":0.11919999867677689,"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/T10716","display_name":"Soil and Unsaturated Flow","score":0.11819999665021896,"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/hyperparameter","display_name":"Hyperparameter","score":0.5551000237464905},{"id":"https://openalex.org/keywords/compaction","display_name":"Compaction","score":0.5034000277519226},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4578999876976013},{"id":"https://openalex.org/keywords/soil-compaction","display_name":"Soil compaction","score":0.4397999942302704},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.43290001153945923},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.43130001425743103},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.3797000050544739},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.36809998750686646},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.3619999885559082}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7139000296592712},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5602999925613403},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.5551000237464905},{"id":"https://openalex.org/C196715460","wikidata":"https://www.wikidata.org/wiki/Q1414356","display_name":"Compaction","level":2,"score":0.5034000277519226},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48179998993873596},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4578999876976013},{"id":"https://openalex.org/C70957220","wikidata":"https://www.wikidata.org/wiki/Q618290","display_name":"Soil compaction","level":3,"score":0.4397999942302704},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.43290001153945923},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.43130001425743103},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3467999994754791},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C133199616","wikidata":"https://www.wikidata.org/wiki/Q25386885","display_name":"Empirical modelling","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.2987000048160553},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.27720001339912415},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C24939127","wikidata":"https://www.wikidata.org/wiki/Q373499","display_name":"Water content","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2517000138759613},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2512.08343","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.08343","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2512.08343","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.08343","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Soil":[0],"compaction":[1,67,154,195],"is":[2],"critical":[3],"in":[4,152,169],"construction":[5,187],"engineering":[6],"to":[7,96,182],"ensure":[8],"the":[9,116,120,127,148,164,171,191],"stability":[10],"of":[11,133,150,166,175,193],"structures":[12],"like":[13],"road":[14],"embankments":[15],"and":[16,27,36,43,55,77,99,105,111,137,173,185],"earth":[17],"dams.":[18],"Traditional":[19],"methods":[20],"for":[21,64,135,139],"determining":[22],"optimum":[23],"moisture":[24],"content":[25],"(OMC)":[26],"maximum":[28],"dry":[29],"density":[30],"(MDD)":[31],"involve":[32],"labor-intensive":[33],"laboratory":[34],"experiments,":[35],"empirical":[37],"regression":[38],"models":[39,71],"have":[40,60],"limited":[41],"applicability":[42],"accuracy":[44,76,110],"across":[45,156],"diverse":[46],"soil":[47,85,158,194],"types.":[48,86,159],"In":[49],"recent":[50],"years,":[51],"artificial":[52],"intelligence":[53],"(AI)":[54],"machine":[56,92],"learning":[57,93],"(ML)":[58],"techniques":[59],"emerged":[61],"as":[62],"alternatives":[63],"predicting":[65,153],"these":[66],"parameters.":[68,196],"However,":[69],"ML":[70,176],"often":[72],"struggle":[73],"with":[74,80],"prediction":[75,192],"generalizability,":[78],"particularly":[79],"heterogeneous":[81,167],"datasets":[82,168],"representing":[83],"various":[84],"This":[87],"study":[88,117,161],"proposes":[89],"an":[90],"automated":[91],"(AutoML)":[94],"approach":[95],"predict":[97],"OMC":[98,140],"MDD.":[100],"AutoML":[101,151],"automates":[102],"algorithm":[103,125],"selection":[104],"hyperparameter":[106],"optimization,":[107],"potentially":[108],"improving":[109,170],"scalability.":[112],"Through":[113],"extensive":[114],"experimentation,":[115],"found":[118],"that":[119],"Extreme":[121],"Gradient":[122],"Boosting":[123],"(XGBoost)":[124],"provided":[126],"best":[128],"performance,":[129],"achieving":[130],"R-squared":[131],"values":[132],"80.4%":[134],"MDD":[136],"89.1%":[138],"on":[141],"a":[142],"separate":[143],"dataset.":[144],"These":[145],"results":[146],"demonstrate":[147],"effectiveness":[149],"parameters":[155],"different":[157],"The":[160],"also":[162],"highlights":[163],"importance":[165],"generalization":[172],"performance":[174],"models.":[177],"Ultimately,":[178],"this":[179],"research":[180],"contributes":[181],"more":[183],"efficient":[184],"reliable":[186],"practices":[188],"by":[189],"enhancing":[190]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-12-11T00:00:00"}
