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

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dc.authorscopusid50161145100
dc.authorscopusid57212405947
dc.contributor.authorCihan, Mehmet Timur
dc.contributor.authorAral, İbrahim Feda
dc.date.accessioned2023-04-20T08:05:55Z
dc.date.available2023-04-20T08:05:55Z
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 (fc), flexural strength (fr), 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, fc, and fr of mortars produced using waste powders instead of standard sand (R2 of UPV, fc and ff 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 (R2=0.889) but the prediction accuracy of fc and fr was low (R2fc=0.612 and R2ff=0.334). Copyright © 2022 Techno-Press, Ltd.
dc.description.sponsorshipThe research described in this paper was financially supported by the Tekirda? Nam?k Kemal University.
dc.description.sponsorshipThe research described in this paper was financially supported by the Tekirdağ Namık Kemal University.
dc.identifier.doi10.12989/cac.2022.29.3.189
dc.identifier.endpage199
dc.identifier.issn1598-8198
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85129181580
dc.identifier.scopusqualityQ1
dc.identifier.startpage187
dc.identifier.urihttps://doi.org/10.12989/cac.2022.29.3.189
dc.identifier.urihttps://hdl.handle.net/20.500.11776/11098
dc.identifier.volume29
dc.indekslendigikaynakScopus
dc.institutionauthorCihan, Mehmet Timur
dc.institutionauthorAral, İbrahim F.
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.subjectCements
dc.subjectCompressive strength
dc.subjectForecasting
dc.subjectFreezing
dc.subjectFuzzy inference
dc.subjectFuzzy neural networks
dc.subjectFuzzy systems
dc.subjectLime
dc.subjectPowders
dc.subjectThawing
dc.subjectApplications of AI
dc.subjectCondition
dc.subjectFreeze-thaw effects
dc.subjectFreeze/thaw
dc.subjectPowder material
dc.subjectPredicting properties
dc.subjectPrediction accuracy
dc.subjectSustainable production
dc.subjectUltrasonic pulse velocity
dc.subjectWaste powder
dc.subjectMortar
dc.titleApplication of AI models for predicting properties of mortars incorporating waste powders under Freeze-Thaw condition
dc.typeArticle

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