Design of Alkali-Activated Slag-Fly Ash Concrete Mixtures Using Machine Learning
Article
Gunasekara, C., Lokuge, W., Keskic, M., Raj, N., Law, D. W. and Setunge, S.. 2020. "Design of Alkali-Activated Slag-Fly Ash Concrete Mixtures
Using Machine Learning." ACI Materials Journal. 117 (5), pp. 263-278. https://doi.org/10.14359/51727019
| Article Title | Design of Alkali-Activated Slag-Fly Ash Concrete Mixtures |
|---|---|
| ERA Journal ID | 4858 |
| Article Category | Article |
| Authors | Gunasekara, C. (Author), Lokuge, W. (Author), Keskic, M. (Author), Raj, N. (Author), Law, D. W. (Author) and Setunge, S. (Author) |
| Journal Title | ACI Materials Journal |
| Journal Citation | 117 (5), pp. 263-278 |
| Number of Pages | 17 |
| Year | 2020 |
| Place of Publication | United States |
| ISSN | 0889-325X |
| 1944-737X | |
| Digital Object Identifier (DOI) | https://doi.org/10.14359/51727019 |
| Web Address (URL) | https://www.concrete.org/publications/internationalconcreteabstractsportal.aspx?m=details&ID=51727019 |
| Abstract | So far, the alkali activated concrete has primarily focused on the effect of source material properties and ratio of mix proportions on the compressive strength development. A little research has focused on developing a standard mix design procedure for alkali activated concrete for a range of compressive strength grades. This study developed a standard mix design procedure for alkali activated slag‒fly ash (low calcium, class F) blended concrete using two machine learning techniques, Artificial Neural Networks (ANN) and Multivariate Adaptive Regression Spline (MARS). The algorithm for the predictive model for concrete mix design was developed using MATLAB programming environment by considering the five key input parameters; water/solid ratio, alkaline activator/binder ratio, Na-Silicate /NaOH ratio, fly ash/slag ratio and NaOH molarity. The targeted compressive strengths ranging from 25–45 MPa (3.63–6.53 ksi) at 28 days were achieved with laboratory testing, using the proposed machine learning mix design procedure. Thus, this tool has the capability to provide a novel approach for the design of slag-fly ash blended alkali activated concrete grades matching to the requirements of in-situ field constructions. |
| Keywords | Alkali Activated Concrete; Artificial Neural Networks; Multivariate Adaptive Regression Spline model; Mix design; Compressive strength |
| ANZSRC Field of Research 2020 | 400510. Structural engineering |
| 401602. Composite and hybrid materials | |
| 400505. Construction materials | |
| Byline Affiliations | Royal Melbourne Institute of Technology (RMIT) |
| School of Civil Engineering and Surveying | |
| School of Agricultural, Computational and Environmental Sciences | |
| Institution of Origin | University of Southern Queensland |
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