Novel analytical method for mix design and performance prediction of high calcium fly ash geopolymer concrete

Gunasekara, Chamila and Atzarakis, Peter and Lokuge, Weena ORCID: https://orcid.org/0000-0003-1370-1976 and Law, David W. and Setunge, Sujeeva (2021) Novel analytical method for mix design and performance prediction of high calcium fly ash geopolymer concrete. Polymers, 13 (6):900.

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Abstract

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 Bayes-ian 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 geo-polymer 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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons. org/licenses/by/4.0/).
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -)
Faculty/School / Institute/Centre: Current - Institute for Advanced Engineering and Space Sciences - Centre for Future Materials (1 Jan 2017 -)
Date Deposited: 21 Apr 2021 00:22
Last Modified: 12 Jul 2021 05:11
Uncontrolled Keywords: high calcium fly ash; geopolymer concrete; artificial neural network; mix design; compressive strength; regression analysis
Fields of Research (2008): 09 Engineering > 0905 Civil Engineering > 090503 Construction Materials
09 Engineering > 0912 Materials Engineering > 091202 Composite and Hybrid Materials
Fields of Research (2020): 40 ENGINEERING > 4016 Materials engineering > 401602 Composite and hybrid materials
40 ENGINEERING > 4005 Civil engineering > 400505 Construction materials
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Socio-Economic Objectives (2020): 12 CONSTRUCTION > 1203 Construction materials performance and processes > 120301 Cement and concrete materials
Identification Number or DOI: https://doi.org/10.3390/polym13060900
URI: http://eprints.usq.edu.au/id/eprint/41833

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