A novel prediction model for durability properties of concrete modified with steel fiber and Silica Fume by using Hybridized GRELM

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dc.authoridGUL, ENES/0000-0001-9364-9738
dc.authorwosidAruntaş, Hüseyin/AHB-9250-2022
dc.authorwosidGUL, ENES/AAH-6191-2021
dc.contributor.authorCemalgil, Selim
dc.contributor.authorGul, Enes
dc.contributor.authorOnat, Onur
dc.contributor.authorAruntas, Hilseyin Yilmaz
dc.date.accessioned2024-08-04T20:51:58Z
dc.date.available2024-08-04T20:51:58Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe service life performance of conventional and modified concrete subjected to harsh climatic condition environment is directly related to durability properties of concrete like abrasion, freezing and thawing cycles. These properties are critical issues that should be predicted before performing experimental test. On this basis, the basic purpose of this paper is to predict the abrasion loss, freezing and thawing properties of concrete modified with silica fume (SF) and steel fiber (SFb) by using mix design and additional properties. From this point of view, a conducted experimental study was selected as a case study. In the control concrete (CC) mixtures, Portland cement, crushed stone aggregate, and superplasticizer (SP) were used in the selected experimental study. SP in concrete mixtures was used in the amounts of 1.0%, 1.5%, and 2.0% by weight of cement, and so modified concrete was produced with and without SFb according to the target strength of C25. Furthermore, SF and SFb were used in different amounts to modify the concrete. The SF was replaced with cement in the amounts of 7.5%, 10.0%, and 15.0%. In total, 16 different mix designs were prepared with different SP and SF ratios. In addition, SFb was added to all mixtures of designed concrete at a constant amount of 65 kg/m3. Additionally, a 16-mix design was prepared with SFb. Cumulatively, 32 different mix designs were prepared for the experimental study. Tests on the fresh, hardened, and life-cycle performance properties of the concrete were conducted. As for the metaheuristic part of this study, on the basis of the available experimental data, life-cycle performance parameters of the concrete modified with SF and SFb are predicted by using single and hybrid generalized extreme learning machine methods. Eight different data sets were generated with gradually extended input data. Two different outputs were considered: abrasion resistance (AL) and freezing/thawing (FT). Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms were used to produce binary and ternary hybrid methods. Four different models were proposed as listed: single use of Generalized Extreme Learning Machine (GRELM), binary use of GRELM-PSO, and GRELM-GWO. Finally, PSO and GWO were hybridized and integrated into GRELM. Two quality indicators, namely Root Mean Square Error (RMSE) and correlation of determination (R2), were considered to see the performance of the prediction. The results showed that the proposed ternary prediction model composed of GRELM-PSO-GWO provided more accurate results in all sets from 74% to 91% by extending input parameters, even if complicated parameters are inserted in as an input to the data set.en_US
dc.description.sponsorshipScientific Research Projects Committee of Gazi University, Ankara, Turkey [07/2002-39]en_US
dc.description.sponsorshipThe financial support for the experimental part of this study was provided by Scientific Research Projects Committee of Gazi University, Ankara, Turkey (Project no: 07/2002-39) . Their support was gratefully acknowledged.en_US
dc.identifier.doi10.1016/j.conbuildmat.2022.127856
dc.identifier.issn0950-0618
dc.identifier.issn1879-0526
dc.identifier.scopus2-s2.0-85130696537en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.conbuildmat.2022.127856
dc.identifier.urihttps://hdl.handle.net/11616/100671
dc.identifier.volume341en_US
dc.identifier.wosWOS:000805865400002en_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.subjectLife-cycle performance predictionen_US
dc.subjectFreezing-thawingen_US
dc.subjectAbrasion resistanceen_US
dc.subjectModified concreteen_US
dc.subjectGeneralized extreme learning machineen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectGrey wolf optimizationen_US
dc.titleA novel prediction model for durability properties of concrete modified with steel fiber and Silica Fume by using Hybridized GRELMen_US
dc.typeArticleen_US

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