{"id":"https://openalex.org/W7204058751","doi":"https://doi.org/10.56741/iistr.esl.002078","title":"Ensemble Machine Learning Models for Accurate Prediction of the Carbon Footprint of SCM-Blended Concrete","display_name":"Ensemble Machine Learning Models for Accurate Prediction of the Carbon Footprint of SCM-Blended Concrete","publication_year":2026,"publication_date":"2026-06-17","ids":{"openalex":"https://openalex.org/W7204058751","doi":"https://doi.org/10.56741/iistr.esl.002078"},"language":"en","primary_location":{"id":"doi:10.56741/iistr.esl.002078","doi":"https://doi.org/10.56741/iistr.esl.002078","is_oa":true,"landing_page_url":"https://doi.org/10.56741/iistr.esl.002078","pdf_url":"https://journal.iistr.org/index.php/ESL/article/download/2078/1196","source":{"id":"https://openalex.org/S4387283089","display_name":"Engineering Science Letter","issn_l":"2961-872X","issn":["2961-872X","2961-8924"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Science Letter","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://journal.iistr.org/index.php/ESL/article/download/2078/1196","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078367288","display_name":"Yulis Widhiastuti","orcid":"https://orcid.org/0009-0004-2252-3432","observed_orcids":["https://orcid.org/0009-0004-2252-3432"]},"institutions":[{"id":"https://openalex.org/I4210138249","display_name":"Universitas Bojonegoro","ror":"https://ror.org/03dxh7253","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210138249"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Yulis Widhiastuti","raw_affiliation_strings":["Universitas Bojonegoro"],"raw_orcid":"https://orcid.org/0009-0004-2252-3432","affiliations":[{"raw_affiliation_string":"Universitas Bojonegoro","institution_ids":["https://openalex.org/I4210138249"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065167541","display_name":"Eko Wahyu Abryandoko","orcid":"https://orcid.org/0000-0003-0480-6135","observed_orcids":["https://orcid.org/0000-0003-0480-6135"]},"institutions":[{"id":"https://openalex.org/I4210138249","display_name":"Universitas Bojonegoro","ror":"https://ror.org/03dxh7253","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210138249"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"Eko wahyu Abryandoko","raw_affiliation_strings":["Universitas Bojonegoro"],"raw_orcid":"https://orcid.org/0000-0003-0480-6135","affiliations":[{"raw_affiliation_string":"Universitas Bojonegoro","institution_ids":["https://openalex.org/I4210138249"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037185069","display_name":"Laily Agustina Rahmawati","orcid":"https://orcid.org/0000-0001-7694-4938","observed_orcids":["https://orcid.org/0000-0001-7694-4938"]},"institutions":[{"id":"https://openalex.org/I4210138249","display_name":"Universitas Bojonegoro","ror":"https://ror.org/03dxh7253","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210138249"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Laily Agustina Rahmawati","raw_affiliation_strings":["Universitas Bojonegoro"],"raw_orcid":"https://orcid.org/0000-0001-7694-4938","affiliations":[{"raw_affiliation_string":"Universitas Bojonegoro","institution_ids":["https://openalex.org/I4210138249"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5147883287","display_name":"Ocha Silvia Kencana","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210138249","display_name":"Universitas Bojonegoro","ror":"https://ror.org/03dxh7253","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210138249"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Ocha Silvia Kencana","raw_affiliation_strings":["Universitas Bojonegoro"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universitas Bojonegoro","institution_ids":["https://openalex.org/I4210138249"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5147884676","display_name":"Putri Puja Pratiwi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210138249","display_name":"Universitas Bojonegoro","ror":"https://ror.org/03dxh7253","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210138249"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Putri Puja Pratiwi","raw_affiliation_strings":["Universitas Bojonegoro"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universitas Bojonegoro","institution_ids":["https://openalex.org/I4210138249"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5065167541"],"corresponding_institution_ids":["https://openalex.org/I4210138249"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":"02","first_page":"81","last_page":"90"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10033","display_name":"Concrete and Cement Materials Research","score":0.9887999892234802,"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/T10033","display_name":"Concrete and Cement Materials Research","score":0.9887999892234802,"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":0.002300000051036477,"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/T10435","display_name":"Environmental Impact and Sustainability","score":0.00139999995008111,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/supplementary-cementitious-materials","display_name":"supplementary cementitious materials","score":0.9789999723434448},{"id":"https://openalex.org/keywords/blended-cement-concrete","display_name":"blended concrete","score":0.8799999952316284},{"id":"https://openalex.org/keywords/partial-cement-replacement","display_name":"partial cement replacement","score":0.7839999794960022},{"id":"https://openalex.org/keywords/co2-emissions","display_name":"CO2 emissions","score":0.8500000238418579},{"id":"https://openalex.org/keywords/concrete-mix-design","display_name":"concrete mix design","score":0.8149999976158142},{"id":"https://openalex.org/keywords/gradient-boosting-regression","display_name":"gradient boosting regression","score":0.7730000019073486},{"id":"https://openalex.org/keywords/support-vector-regression","display_name":"support vector regression","score":0.7839999794960022},{"id":"https://openalex.org/keywords/random-forest-regression","display_name":"random forest regression","score":0.9120000004768372},{"id":"https://openalex.org/keywords/regression-trees","display_name":"regression trees","score":0.972000002861023},{"id":"https://openalex.org/keywords/feature-importance","display_name":"feature importance","score":0.6470000147819519},{"id":"https://openalex.org/keywords/sensitivity-analysis","display_name":"sensitivity analysis","score":0.8270000219345093}],"concepts":[{"id":"https://openalex.org/C2780936489","wikidata":"https://www.wikidata.org/wiki/Q310667","display_name":"Carbon footprint","level":3,"score":0.741100013256073},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7293000221252441},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6776000261306763},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6711999773979187},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5860000252723694},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.54830002784729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.527999997138977},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.47870001196861267},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.47510001063346863},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.46540001034736633},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.46459999680519104},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4514999985694885},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38670000433921814},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.38589999079704285},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3686000108718872},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.36559998989105225},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.3617999851703644},{"id":"https://openalex.org/C44882253","wikidata":"https://www.wikidata.org/wiki/Q3455882","display_name":"Multivariate adaptive regression splines","level":4,"score":0.3450999855995178},{"id":"https://openalex.org/C106192678","wikidata":"https://www.wikidata.org/wiki/Q1419761","display_name":"Collinearity","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33160001039505005},{"id":"https://openalex.org/C64946054","wikidata":"https://www.wikidata.org/wiki/Q4874476","display_name":"Bayesian multivariate linear regression","level":3,"score":0.32499998807907104},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3197000026702881},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2994999885559082},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.28850001096725464},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2858999967575073},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2727999985218048},{"id":"https://openalex.org/C2779666059","wikidata":"https://www.wikidata.org/wiki/Q45190","display_name":"Cementitious","level":3,"score":0.26330000162124634}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.56741/iistr.esl.002078","doi":"https://doi.org/10.56741/iistr.esl.002078","is_oa":true,"landing_page_url":"https://doi.org/10.56741/iistr.esl.002078","pdf_url":"https://journal.iistr.org/index.php/ESL/article/download/2078/1196","source":{"id":"https://openalex.org/S4387283089","display_name":"Engineering Science Letter","issn_l":"2961-872X","issn":["2961-872X","2961-8924"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Science Letter","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.56741/iistr.esl.002078","doi":"https://doi.org/10.56741/iistr.esl.002078","is_oa":true,"landing_page_url":"https://doi.org/10.56741/iistr.esl.002078","pdf_url":"https://journal.iistr.org/index.php/ESL/article/download/2078/1196","source":{"id":"https://openalex.org/S4387283089","display_name":"Engineering Science Letter","issn_l":"2961-872X","issn":["2961-872X","2961-8924"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Engineering Science Letter","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Climate action","score":1.0,"id":"https://metadata.un.org/sdg/13"},{"display_name":"Responsible consumption and production","score":0.8518000245094299,"id":"https://metadata.un.org/sdg/12"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204058751.pdf","grobid_xml":"https://content.openalex.org/works/W7204058751.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W1963974315","https://openalex.org/W1979582709","https://openalex.org/W2000164913","https://openalex.org/W2080859125","https://openalex.org/W2167849100","https://openalex.org/W2216946510","https://openalex.org/W2476879142","https://openalex.org/W2788650673","https://openalex.org/W2893769615","https://openalex.org/W2911964244","https://openalex.org/W2990531785","https://openalex.org/W3033772434","https://openalex.org/W3093895805","https://openalex.org/W3153764057","https://openalex.org/W4300533453","https://openalex.org/W4308105876","https://openalex.org/W4415818586","https://openalex.org/W6906650501"],"related_works":["https://openalex.org/W4410101247","https://openalex.org/W7161149277","https://openalex.org/W4414919921","https://openalex.org/W7143695550","https://openalex.org/W7134997113","https://openalex.org/W4401880293","https://openalex.org/W4415072040","https://openalex.org/W4410176808","https://openalex.org/W4412417294","https://openalex.org/W4406731854"],"abstract_inverted_index":{"Concrete":[0],"contributes":[1],"approximately":[2],"8%":[3],"of":[4,10,31,58],"global":[5,66],"CO\u2082":[6,167,171],"emissions.":[7,155],"The":[8],"incorporation":[9],"Supplementary":[11],"Cementitious":[12],"Materials":[13],"(SCMs)":[14],"as":[15,22,149],"partial":[16],"cement":[17,148,164],"replacements":[18],"is":[19],"widely":[20],"recognized":[21],"an":[23],"effective":[24],"strategy":[25],"to":[26,53,154],"reduce":[27],"the":[28,36,55,128,150],"carbon":[29,42,56],"footprint":[30,57],"concrete.":[32],"However,":[33],"accurately":[34],"quantifying":[35],"relationship":[37,162],"between":[38,163],"mix":[39,70],"composition":[40,63],"and":[41,105,166],"emissions":[43,168],"remains":[44],"challenging.":[45],"This":[46],"study":[47],"develops":[48],"a":[49,159],"machine":[50],"learning":[51],"model":[52],"predict":[54],"SCM-based":[59],"concrete":[60,182],"using":[61,113],"material":[62],"data.":[64],"A":[65],"dataset":[67],"comprising":[68],"1,456":[69],"designs":[71],"collected":[72],"from":[73],"136":[74],"publications":[75],"across":[76],"27":[77],"countries":[78],"was":[79],"compiled,":[80],"resulting":[81],"in":[82,180],"1,294":[83],"valid":[84],"samples":[85],"after":[86],"preprocessing.":[87],"Four":[88],"regression":[89],"algorithms":[90],"were":[91],"evaluated:":[92],"Support":[93],"Vector":[94],"Regression":[95,99,103,108],"(SVR),":[96],"Random":[97],"Forest":[98],"(RFR),":[100],"Decision":[101],"Tree":[102],"(DTR),":[104],"Gradient":[106],"Boosting":[107],"(GBR),":[109],"with":[110,125],"hyperparameter":[111],"tuning":[112],"5-fold":[114],"cross-validation.":[115],"All":[116],"models":[117],"achieved":[118],"high":[119],"predictive":[120],"accuracy":[121],"(R\u00b2":[122,131],">":[123],"0.998),":[124],"GBR":[126],"demonstrating":[127],"best":[129],"performance":[130],"=":[132,135,140],"0.9996;":[133],"RMSE":[134],"1.7452":[136],"kg":[137,142,170,173],"CO\u2082/m\u00b3;":[138],"MAE":[139],"1.2779":[141],"CO\u2082/m\u00b3).":[143],"Feature":[144],"importance":[145],"analysis":[146,157],"identified":[147],"dominant":[151],"contributor":[152],"(>99.8%)":[153],"Sensitivity":[156],"confirmed":[158],"strong":[160],"linear":[161],"content":[165],"(~0.82":[169],"per":[172],"cement).":[174],"These":[175],"findings":[176],"support":[177],"emission-reduction":[178],"strategies":[179],"sustainable":[181],"design.":[183]},"counts_by_year":[],"updated_date":"2026-11-13T13:42:25.865203","created_date":"2026-08-24T00:00:00"}
