Novel analytical method for mix design and performance prediction of high calcium fly ash geopolymer concrete
Article
Article Title | Novel analytical method for mix design and performance |
---|---|
ERA Journal ID | 201391 |
Article Category | Article |
Authors | Gunasekara, Chamila (Author), Atzarakis, Peter (Author), Lokuge, Weena (Author), Law, David W. (Author) and Setunge, Sujeeva (Author) |
Journal Title | Polymers |
Journal Citation | 13 (6) |
Article Number | 900 |
Number of Pages | 21 |
Year | 2021 |
Publisher | MDPI AG |
Place of Publication | Basel, Switzerland |
ISSN | 2073-4360 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/polym13060900 |
Web Address (URL) | https://www.mdpi.com/2073-4360/13/6/900 |
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. |
Keywords | high calcium fly ash; geopolymer concrete; artificial neural network; mix design; compressive strength; regression analysis |
ANZSRC Field of Research 2020 | 401602. Composite and hybrid materials |
400505. Construction materials | |
Public Notes | Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article |
Byline Affiliations | Royal Melbourne Institute of Technology (RMIT) |
School of Civil Engineering and Surveying | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q64yv/novel-analytical-method-for-mix-design-and-performance-prediction-of-high-calcium-fly-ash-geopolymer-concrete
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