Machine learning approaches to predict compressive strength of fly ash-based geopolymer concrete: A comprehensive review
Article
Article Title | Machine learning approaches to predict compressive strength of fly ash-based geopolymer concrete: A comprehensive review |
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ERA Journal ID | 3470 |
Article Category | Article |
Authors | Rathnayaka, Madushan, Karunasinghe, Dulakshi, Gunasekara, Chamila, Wijesundara, Kushan, Lokuge, Weena and Law, David W. |
Journal Title | Construction and Building Materials |
Journal Citation | 149 |
Article Number | 135519 |
Number of Pages | 15 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0950-0618 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.conbuildmat.2024.135519 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0950061824006603 |
Abstract | Geopolymer concrete is a sustainable replacement to the Ordinary Portland Cement (OPC) concrete as it mitigates some of the associated problems of OPC manufacturing such as greenhouse gas emission and natural resource depletion. There has been significant recent research in the design of fly ash-based geopolymer concrete using advanced machine learning techniques which can address some of the problems with classical mix design approaches. However, practical application of geopolymer concrete is limited due to lack of standard mix design procedure. This comprehensive review summarizes the current literature on machine learning methodologies to predict the compressive strength of fly ash-based geopolymer concrete. Firstly, the input parameters used for the machine learning model development are categorized based on feature selection or feature extraction. Secondly, available machine learning approaches are categorized based on analysis methods namely, nonlinear regression, ensemble learning, and evolutionary programming. The effect of hyperparameters on the individual model performance, and model comparison based on the prediction performance are also discussed to identify potentially more suitable model type and hyper parameter ranges. Further, the paper discusses the input variable’s sensitivity towards the model performance which provides guidance towards future model developments. Overall, this paper will provide an understanding of the current state of machine learning approaches to predict the compressive strength of geopolymer concrete and the gaps in research for the development of models and achieving the required performance. Hence, the summarized knowledge will be highly beneficial to design prospective research towards sustainable cement-free concrete using fly ash. |
Keywords | Fly ash ; Geopolymer concrete ; Machine leaning ; Regression analysis ; Compressive strength |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400505. Construction materials |
401602. Composite and hybrid materials | |
Byline Affiliations | Royal Melbourne Institute of Technology (RMIT) |
University of Peradeniya, Sri Lanka | |
University of Southern Queensland |
https://research.usq.edu.au/item/z5x95/machine-learning-approaches-to-predict-compressive-strength-of-fly-ash-based-geopolymer-concrete-a-comprehensive-review
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