Application of Al models for predicting properties of mortars incorporating waste powders under Freeze-Thaw condition

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dc.contributor.authorCihan, Mehmet Timur
dc.contributor.authorAral, İbrahim Feda
dc.date.accessioned2022-05-11T14:03:07Z
dc.date.available2022-05-11T14:03:07Z
dc.date.issued2022
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractThe usability of waste materials as raw materials is necessary for sustainable production. This study investigates the effects of different powder materials used to replace cement (0%, 5% and 10%) and standard sand (0%, 20% and 30%) (basalt, limestone, and dolomite) on the compressive strength (f(c)), flexural strength (f(r)), and ultrasonic pulse velocity (UPV) of mortars exposed to freeze-thaw cycles (56, 86, 126, 186 and 226 cycles). Furthermore, the usability of artificial intelligence models is compared, and the prediction accuracy of the outputs is examined according to the inputs (powder type, replacement ratio, and the number of cycles). The results show that the variability of the outputs was significantly high under the freeze-thaw effect in mortars produced with waste powder instead of those produced with cement and with standard sand. The highest prediction accuracy for all outputs was obtained using the adaptive-network-based fuzzy inference system model. The significantly high prediction accuracy was obtained for the UPV, f(c), and f(r) of mortars produced using waste powders instead of standard sand (R-2 of UPV, f(c )and f(r) is 0.931, 0.759 and 0.825 respectively), when under the freeze-thaw effect. However, for the mortars produced using waste powders instead of cement, the prediction accuracy of UPV was significantly high (R-2=0.889) but the prediction accuracy of f(c) and f(r) was low (R(2)f(c)=0.612 and R(2)f(r)=0.334).
dc.description.sponsorshipTekirda Namik Kemal University
dc.description.sponsorshipThe research described in this paper was financially supported by the Tekirda Namik Kemal University.
dc.identifier.doi10.12989/cac.2022.29.3.187
dc.identifier.endpage199
dc.identifier.issn1598-8198
dc.identifier.issn1598-818X
dc.identifier.issue3en_US
dc.identifier.startpage187
dc.identifier.urihttps://doi.org/10.12989/cac.2022.29.3.187
dc.identifier.urihttps://hdl.handle.net/20.500.11776/4613
dc.identifier.volume29
dc.identifier.wosWOS:000773462100005
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.institutionauthorCihan, Mehmet Timur
dc.institutionauthorAral, İbrahim Ferda
dc.language.isoen
dc.publisherTechno-Press
dc.relation.ispartofComputers And Concrete
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectartificial intelligence
dc.subjectfreeze-thaw effect
dc.subjectmortar
dc.subjectwaste powder
dc.subjectSelf-Compacting Concrete
dc.subjectMultiple Linear-Regression
dc.subjectArtificial Neural-Network
dc.subjectCompressive Strength
dc.subjectFly-Ash
dc.subjectCement Mortars
dc.subjectPumice Powder
dc.subjectStone Dust
dc.subjectGranite
dc.subjectMarble
dc.titleApplication of Al models for predicting properties of mortars incorporating waste powders under Freeze-Thaw condition
dc.typeArticle

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