Efficient machine learning models for estimation of compressive strengths of zeolite and diatomite substituting concrete in sodium chloride solution

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dc.authoridGulbandilar, Eyyup/0000-0001-5559-5281en_US
dc.authorwosidGulbandilar, Eyyup/H-1746-2015en_US
dc.contributor.authorOzcan, Giyasettin
dc.contributor.authorKocak, Burak
dc.contributor.authorGulbandilar, Eyyup
dc.contributor.authorKocak, Yilmaz
dc.date.accessioned2024-08-23T16:07:03Z
dc.date.available2024-08-23T16:07:03Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis study implements a set of machine learning algorithms to building material science, which predict the compressive strength of zeolite and diatomite substituting concrete mixes in sodium chloride solution. Particularly, Random Forest, Support Vector Machine, Extreme Gradient Boosting, Light Gradient Boosting, and Categorical Boosting algorithms are exploited and their optimal parameters are tuned. In the training and testing of these models, 28 day, 56 day, and 90 day compressive strength observations of 63 samples of 7 different concrete mixtures substituting Portland cement, zeolite, diatomite, zeolite + diatomite were used. Consequently, compressive strength experimentation results and machine learning predictions were compared through statistical methods such as RMSE, MAPE, and R 2. Results denote that the prediction performance of machine learning is improving with tuned models. Particularly, RMSE, MAPE, R 2 scores of Categorical Boosting are, respectively, 1.15, 1.45%, and 98.03% after parameter tuning design. The results denote that presented machine learning model can provide an advantage in the cost and duration of the compressive strength experiments.en_US
dc.description.sponsorshipDuzce University Presidency of Scientific Research Projects [2011.03.HD.009]en_US
dc.description.sponsorshipThis study is financially supported by Duzce University Presidency of Scientific Research Projects with the project code number 2011.03.HD.009. Furthermore, Duezce Yigitler Beton supported the research by providing facilities for experiments to be carried out. The authors would like to thank both supporters.en_US
dc.identifier.doi10.1007/s13369-024-09042-1
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.urihttps://doi.org/10.1007/s13369-024-09042-1
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14449
dc.identifier.wosWOS:001205482600006en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofArabian Journal For Science and Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectZeoliteen_US
dc.subjectDiatomiteen_US
dc.subjectCompressive strengthen_US
dc.subjectRandom foresten_US
dc.subjectGradient boostingen_US
dc.subjectMachine learningen_US
dc.titleEfficient machine learning models for estimation of compressive strengths of zeolite and diatomite substituting concrete in sodium chloride solutionen_US
dc.typeArticleen_US

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