Prediction of compressive strengths of pumice-and diatomite-containing cement mortars with artificial intelligence-based applications

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dc.authoridkocak, burak/0000-0002-7307-396Xen_US
dc.authoridPINARCI, Ibrahim/0000-0002-9318-4325en_US
dc.authorscopusid58123471100en_US
dc.authorscopusid57362284800en_US
dc.authorscopusid25651286200en_US
dc.authorscopusid36117863900en_US
dc.authorwosidPınarcı, İbrahim/GLR-5762-2022en_US
dc.authorwosidkocak, burak/A-4749-2012en_US
dc.contributor.authorKocak, Burak
dc.contributor.authorPinarci, Brahim
dc.contributor.authorGuvenc, Ugur
dc.contributor.authorKocak, Yilmaz
dc.date.accessioned2024-08-23T16:04:56Z
dc.date.available2024-08-23T16:04:56Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractIn this study, two different Artificial neural networks (ANN) and two different adaptive network-based fuzzy inference systems (ANFIS) models were constructed to predict the compressive strength of 7 different cement mortar samples with or without pumice and/or diatomite on different days. Five parameters including day, PC, pumice, diatomite and water were employed as the inputs, and the compressive strength was used as the output variable. The compressive strengths used in the model construction were obtained from laboratory experiments accounting for a total of 168 data. Statistical methods such as R2, RMS and MAPE preferred in the literature were used to compare the four different models. According to the test results obtained from R2, RMS and MAPE, ANN and ANFIS models were able to make very good predictions performance. For this reason, it can be said that these cement mortars' compressive strength can be estimated with a very small error and in a short time with both ANN and ANFIS models.en_US
dc.description.sponsorshipDuzce University Research Fund [2021.06.08.1190]en_US
dc.description.sponsorshipAcknowledgement Material analyses carried out for this study were supported by Duzce University Research Fund (Project Code No: 2021.06.08.1190) . In addition, the authors would like to thank the Eskisehir CIMSA cement factory managers and employees for their invaluable contributions to the performance of compressive strength tests.en_US
dc.identifier.doi10.1016/j.conbuildmat.2023.131516
dc.identifier.issn0950-0618
dc.identifier.issn1879-0526
dc.identifier.scopus2-s2.0-85153514522en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.conbuildmat.2023.131516
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14398
dc.identifier.volume385en_US
dc.identifier.wosWOS:000989755400001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofConstruction and Building Materialsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPumiceen_US
dc.subjectDiatomiteen_US
dc.subjectCompressive strengthen_US
dc.subjectANNen_US
dc.subjectANFISen_US
dc.subjectMechanical-Propertiesen_US
dc.subjectFlexural Strengthen_US
dc.subjectConcreteen_US
dc.subjectPerformanceen_US
dc.subjectRegressionen_US
dc.subjectZeoliteen_US
dc.subjectFuzzyen_US
dc.titlePrediction of compressive strengths of pumice-and diatomite-containing cement mortars with artificial intelligence-based applicationsen_US
dc.typeArticleen_US

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