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Influence of Aluminosilicate for the Prediction of Mechanical Properties of Geopolymer Concrete – Artificial Neural Network

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Abstract

In this paper, details and results of experimental and predictive studies carried out to determine the mechanical properties of Aluminosilicate materials like Ground Granulated Blast furnace Slag (GGBS) and Fly Ash (FA) based geopolymer concrete specimens are presented and discussed. The major parameters considered in the experimental study are the percentages of GGBS and Fly ash and the percentage of manufactured sand (m-sand) used to replace conventional river sand used in the production of geopolymer concrete. Sodium hydroxide and sodium silicate solutions were used as the activator in the production of geopolymer concrete. The mechanical properties of the geopolymer concrete determined were the compressive strength, split-tensile strength and flexural strength. The test results showed that the mechanical properties of geopolymer concrete improved with increase in the percentage use of GGBS. Also, it was observed from the test results that increase in the percentage use of m-sand increased the mechanical properties of the geopolymer concrete up to an optimum dosage beyond which reduction in the mechanical properties was observed. The predicted mechanical properties of the geopolymer concrete using Artificial Neural Network (ANN) was found to be in good agreement with the test results.

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Acknowledgements

The author gratefully acknowledging Dr.S.Elavenil for her fabulous guidance and greater support for valuable suggestions on time. The author also acknowledges sincere thanks to the laboratory staffs of Vellore Institute of Technology, Chennai for their kind support.

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  1. School of Mechanical and Building Sciences, VIT, Chennai Campus, Chennai, 127, India

    S. Nagajothi & S. Elavenil

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  1. S. Nagajothi
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Correspondence to S. Nagajothi.

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Nagajothi, S., Elavenil, S. Influence of Aluminosilicate for the Prediction of Mechanical Properties of Geopolymer Concrete – Artificial Neural Network. Silicon 12, 1011–1021 (2020). https://doi.org/10.1007/s12633-019-00203-8

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  • DOI: https://doi.org/10.1007/s12633-019-00203-8

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