Machine Learning Prediction of Residual Mechanical Strength of Hybrid-Fiber-Reinforced Self-consolidating Concrete Exposed to Elevated Temperature

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dc.authoridTANYILDIZI, Harun/0000-0002-7585-2609
dc.authoridKina, Ceren/0000-0002-2054-3323
dc.authoridNehdi, Moncef/0000-0002-2561-993X
dc.authoridKina, Ceren/0000-0002-2054-3323
dc.authorwosidTANYILDIZI, Harun/A-1950-2016
dc.authorwosidKina, Ceren/KIE-5891-2024
dc.authorwosidNehdi, Moncef/P-3725-2019
dc.authorwosidKina, Ceren/KRP-5310-2024
dc.contributor.authorTurk, Kazim
dc.contributor.authorKina, Ceren
dc.contributor.authorTanyildizi, Harun
dc.contributor.authorBalalan, Esma
dc.contributor.authorNehdi, Moncef L. L.
dc.date.accessioned2024-08-04T20:54:28Z
dc.date.available2024-08-04T20:54:28Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractEstablishing the engineering properties of cement-based composites at elevated temperature requires costly, laborious, and time-consuming experimental work. Data-driven models can provide a robust and efficient alternative. In this study, extreme learning machine (ELM), support vector machine (SVM), artificial neural network (ANN), and decision tree (DT) models were trained to predict the residual compressive, splitting tensile, and flexural strengths of hybrid fiber-reinforced self-compacting concrete (HFR-SCC) exposed to high temperatures. Mixtures including macro and micro steel fibers, polyvinyl alcohol (PVA), and polypropylene (PP) were subjected to different temperature levels, leading to an experimental database of 360 specimens. Eleven input parameters including cement, fly ash, water, sand, gravel, fiber type, water reducer, and temperature were deployed. The residual mechanical strengths were targeted as output parameters. ANOVA was used to explore the influence of input parameters. Temperature was found to be the most influential parameter. Dataset consisting of 114 instances was retrieved from pertinent literature and used along with the authors' experimentally generated dataset for residual strength prediction. The experimental results were compared with predictions of ELM, SVM, ANN, and DT. ELM achieved superior performance and can offer a robust tool for predicting the residual mechanical strengths of HFR-SCC upon exposure to high temperature.en_US
dc.identifier.doi10.1007/s10694-023-01457-w
dc.identifier.endpage2923en_US
dc.identifier.issn0015-2684
dc.identifier.issn1572-8099
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85163842995en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2877en_US
dc.identifier.urihttps://doi.org/10.1007/s10694-023-01457-w
dc.identifier.urihttps://hdl.handle.net/11616/101441
dc.identifier.volume59en_US
dc.identifier.wosWOS:001023643800001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofFire Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHigh temperatureen_US
dc.subjectResidual strengthen_US
dc.subjectFiber reinforceden_US
dc.subjectSelf-compacting concreteen_US
dc.subjectMachine learningen_US
dc.subjectANOVAen_US
dc.subjectStrengthen_US
dc.subjectPredictionen_US
dc.titleMachine Learning Prediction of Residual Mechanical Strength of Hybrid-Fiber-Reinforced Self-consolidating Concrete Exposed to Elevated Temperatureen_US
dc.typeArticleen_US

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