Abstract
Geopolymers are inorganic polymers produced by the alkali activation of alumina-silicate minerals. Geopolymer is an alternative cementitious binder to traditional Ordinary Portland Cement (OPC) leading to economical and sustainable construction technique by the utilisation of alumina-silicate waste materials. The strength development in fly ash-slag geopolymer mortar is dependent on the chemical composition of the raw materials. An effective way to study the effect of chemical components in geopolymer is through the evaluation of molar ratios. In this study, an Artificial Neural Network (ANN) model has been applied to predict the effect of molar ratios on the 28-day compressive strength of fly ash-slag geopolymer mortar. For this purpose, geopolymer mortar samples were prepared with different fly ash-slag composition, activator concentration, and alkaline solution ratios. The molar ratios of the geopolymer mortar samples were evaluated and given as input to ANN, and the compressive strength was obtained as the output. The accuracy of the assessed model was investigated by statistical parameters; the mean, median, and mode values of the ratio between actual and predicted strength are equal to 0.991, 0.973, and 0.991, respectively, with a 14% coefficient of variation and a correlation coefficient of 89%. Based on the mentioned findings, the proposed novel model seems reliable enough and could be used for the prediction of compressive strength of fly ash-slag geopolymer. In addition, the influence of molar compositions on the compressive strength was further investigated through parametric studies utilizing the proposed model. The percentages of Na2O and SiO2 of the source materials were observed as the dominant chemical compounds in the mix affecting the compressive strength. The influence of CaO was significant when combined with a high amount of SiO2 in alkaline solution.
Highlights
Production of Ordinary Portland Cement (OPC) is an energy-intensive process that consumes enormous amount of energy and results in emission of substantial amount of carbon dioxide into the atmosphere leading to global warming and atmospheric pollution [1,2,3]
Fly Ash (FA) is an industrial waste resulting from the burning of coal in thermal power plant with a chemical composition based essentially on SiO2 and Al2O3, and Ground Granulated Blast Furnace Slag (GGBFS) is produced from slag resulting from steel manufacturing with a chemical composition based essentially on CaO, SiO2, and Al2O3
Both FA and GGBFS are pozzolanic materials which are generally blended with OPC to produce Portland pozzolanic cement and are used as workability-improving admixtures [7]
Summary
Production of Ordinary Portland Cement (OPC) is an energy-intensive process that consumes enormous amount of energy and results in emission of substantial amount of carbon dioxide into the atmosphere leading to global warming and atmospheric pollution [1,2,3]. FA is an industrial waste resulting from the burning of coal in thermal power plant with a chemical composition based essentially on SiO2 and Al2O3, and GGBFS is produced from slag resulting from steel manufacturing with a chemical composition based essentially on CaO, SiO2, and Al2O3 Both FA and GGBFS are pozzolanic materials which are generally blended with OPC to produce Portland pozzolanic cement and are used as workability-improving admixtures [7]. E chemical composition of the source materials, alkali concentration, and percentage replacement of FA by GGBFS are major factors which influences the strength of FA-slag geopolymer. An experimental investigation was conducted to evaluate the influence of alkali concentration, ratio of various alkalis used for geopolymerization, and percentage of replacement of FA by slag on the compressive strength of FA-slag geopolymer mortar. An ANN model is proposed, able to predict the compressive strength on the basis of chemical parameters, SiO2, Al2O3, Na2O, and CaO. e reliability of the proposed model is investigated, confirming its effectiveness at least in relation to the range of findings and variables referred to the present experimental campaign
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