Analysing of nano-silica usage with fly ash for grouts with artificial neural network models

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dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.contributor.authorCelik, Fatih
dc.contributor.authorYildiz, Oguzhan
dc.contributor.authorColak, Andac Batur
dc.contributor.authorBozkir, Samet Mufit
dc.date.accessioned2024-11-07T13:24:29Z
dc.date.available2024-11-07T13:24:29Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractWhen grout is used to penetrate voids and cracks in soils and rock layers, easy and effective pumping of the grouts is vital, especially for grouting works during geotechnical improvements. For this reason, improving the rheological parameters of cement-based grouts and increasing their fluidity are important for effective grouting injection. In this study, an experimental investigation and analysis using artificial neural network (ANN) models were used to discover how nano silica (n-SiO2) together with fly ash affects the rheological behaviour of cement-based grouts. The effects of nano silica (n-SiO2) additions at different contents by mass (0.0%, 0.3%, 0.6%, 0.9%, 1.2% and 1.5%) on the plastic viscosity and yield stress values of cement-based grouts incorporating fly ash as a mineral additive at different amounts (0% - as a control, 5%, 10%, 15%, 20%, 25% and 30%) were investigated. Using the experimental data obtained, a feed-forward (FF) back-propagation (BP) multi-layer perceptron (MLP) artificial neural network (ANN) was developed to predict the plastic viscosity and yield stress of cement-based grouts with nano silica nanoparticle additives. The ANN model developed can predict the plastic viscosity and yield stress values of cement-based grouts containing nano silica nanoparticle-doped fly ash with high accuracy.
dc.description.sponsorshipScientific and Technological Research Council of Turkey -TUBITAK [219M522]; TUBITAK
dc.description.sponsorshipThis study was funded by the Scientific and Technological Research Council of Turkey -TUBITAK (grant number: 219M522). The authors would like to thank TUBITAK for its great support.
dc.identifier.doi10.1680/jadcr.21.00180
dc.identifier.endpage206
dc.identifier.issn0951-7197
dc.identifier.issn1751-7605
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85137876862
dc.identifier.scopusqualityQ2
dc.identifier.startpage191
dc.identifier.urihttps://doi.org/10.1680/jadcr.21.00180
dc.identifier.urihttps://hdl.handle.net/11480/14149
dc.identifier.volume35
dc.identifier.wosWOS:000863618500001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherEmerald Group Publishing Ltd
dc.relation.ispartofAdvances in Cement Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectfly ash (PFA)
dc.subjectnanostructure
dc.subjectnano silica
dc.subjectrheological properties
dc.titleAnalysing of nano-silica usage with fly ash for grouts with artificial neural network models
dc.typeArticle

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