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Article

Novel Analytical Method for Mix Design and Performance Prediction of High Calcium Fly Ash Geopolymer Concrete

1
School of Engineering, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3000, Australia
2
School of Civil Engineering and Surveying, University of Southern Queensland, Springfield, QSL 4300, Australia
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Author to whom correspondence should be addressed.
Academic Editors: Enzo Martinelli and Luciano Feo
Polymers 2021, 13(6), 900; https://doi.org/10.3390/polym13060900
Received: 3 February 2021 / Revised: 3 March 2021 / Accepted: 8 March 2021 / Published: 15 March 2021
Despite extensive in-depth research into high calcium fly ash geopolymer concretes and a number of proposed methods to calculate the mix proportions, no universally applicable method to determine the mix proportions has been developed. This paper uses an artificial neural network (ANN) machine learning toolbox in a MATLAB programming environment together with a Bayesian regularization algorithm, the Levenberg-Marquardt algorithm and a scaled conjugate gradient algorithm to attain a specified target compressive strength at 28 days. The relationship between the four key parameters, namely water/solid ratio, alkaline activator/binder ratio, Na2SiO3/NaOH ratio and NaOH molarity, and the compressive strength of geopolymer concrete is determined. The geopolymer concrete mix proportions based on the ANN algorithm model and contour plots developed were experimentally validated. Thus, the proposed method can be used to determine mix designs for high calcium fly ash geopolymer concrete in the range 25–45 MPa at 28 days. In addition, the design equations developed using the statistical regression model provide an insight to predict tensile strength and elastic modulus for a given compressive strength. View Full-Text
Keywords: high calcium fly ash; geopolymer concrete; artificial neural network; mix design; compressive strength; regression analysis high calcium fly ash; geopolymer concrete; artificial neural network; mix design; compressive strength; regression analysis
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MDPI and ACS Style

Gunasekara, C.; Atzarakis, P.; Lokuge, W.; Law, D.W.; Setunge, S. Novel Analytical Method for Mix Design and Performance Prediction of High Calcium Fly Ash Geopolymer Concrete. Polymers 2021, 13, 900. https://doi.org/10.3390/polym13060900

AMA Style

Gunasekara C, Atzarakis P, Lokuge W, Law DW, Setunge S. Novel Analytical Method for Mix Design and Performance Prediction of High Calcium Fly Ash Geopolymer Concrete. Polymers. 2021; 13(6):900. https://doi.org/10.3390/polym13060900

Chicago/Turabian Style

Gunasekara, Chamila, Peter Atzarakis, Weena Lokuge, David W. Law, and Sujeeva Setunge. 2021. "Novel Analytical Method for Mix Design and Performance Prediction of High Calcium Fly Ash Geopolymer Concrete" Polymers 13, no. 6: 900. https://doi.org/10.3390/polym13060900

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