Prediction of compressive strengths of Portland cement with random forest, support vector machine and gradient boosting models

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dc.contributor.authorÖzcan, Giyasettin
dc.contributor.authorGülbandilar, Eyyüp
dc.contributor.authorKocak, Yilmaz
dc.date.accessioned2025-10-11T20:45:19Z
dc.date.available2025-10-11T20:45:19Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis study presents machine learning models to predict compressive strengths of 924 CEM I 42.5 R type Portland cements. Particularly the utilized machine learning algorithms are adaptive network-based fuzzy inference systems, Random Forest, Support Vector Machine, Extreme Gradient Boosting, Light Gradient Boosting and Categorical Boosting. For machine learning, collected data contained 15 input features that show the physical and chemical properties of the cements. The compressive strengths at 1, 2, 7 and 28 days were defined as the output parameters. Models for each hydration day were trained with 748 data points and tested with 176 data points. Then, compressive strength test results and machine learning predictions were compared using statistical methods such as R-squared, mean absolute percentage error and root-mean-square error. The results indicate that Gradient Boosting models, in particular, accurately predict compressive strength, demonstrating that it is possible to estimate compressive strength without mechanical tests. In our developed Gradient Boosting model, the RMSE accuracy exceeds 95%, further supporting its reliability. The developed machine learning models offer substantial savings in both time and cost for compressive strength estimation. © 2025 Elsevier B.V., All rights reserved.en_US
dc.identifier.doi10.1007/s00521-025-11536-4
dc.identifier.issn1433-3058
dc.identifier.issn0941-0643
dc.identifier.scopus2-s2.0-105012865184en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-025-11536-4
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21272
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_Scopus_20250911
dc.subjectCompressive Strengthen_US
dc.subjectGradient Boostingen_US
dc.subjectMachine Learningen_US
dc.subjectPortland Cementen_US
dc.subjectAdaptive Boostingen_US
dc.subjectForecastingen_US
dc.subjectFuzzy Inferenceen_US
dc.subjectFuzzy Systemsen_US
dc.subjectHydrationen_US
dc.subjectLearning Systemsen_US
dc.subjectMean Square Erroren_US
dc.subjectRandom Forestsen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectAdaptive Network-based Fuzzy Inference Systemen_US
dc.subjectAdaptive-network- Based Fuzzy Inference Systemsen_US
dc.subjectDatapointsen_US
dc.subjectGradient Boostingen_US
dc.subjectLight Gradientsen_US
dc.subjectMachine Learning Algorithmsen_US
dc.subjectMachine Learning Modelsen_US
dc.subjectMachine-learningen_US
dc.subjectSupport Vectors Machineen_US
dc.subjectCompressive Strengthen_US
dc.subjectPortland Cementen_US
dc.titlePrediction of compressive strengths of Portland cement with random forest, support vector machine and gradient boosting modelsen_US
dc.typeArticleen_US

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