Enhancing Fly Ash-Based Geopolymer Concrete Performance through Machine Learning: A Predictive Model for Compressive Strength and Workability Optimization.
Paper
Rathnayaka, Madushan, Wijesundara, Kushan, Gunasekara, Chamila, Law, David W, Karunasinghe, Dulakshi and Lokuge, Weena. 2024. "Enhancing Fly Ash-Based Geopolymer Concrete Performance through Machine Learning: A Predictive Model for Compressive Strength and Workability Optimization." 3R International Scientific Conference on Material Cycles and Waste Management (3RINCs 2024). Sydney, Australia 15 - 17 Mar 2024 Australia.
| Paper/Presentation Title | Enhancing Fly Ash-Based Geopolymer Concrete Performance through Machine Learning: A Predictive Model for Compressive Strength and Workability Optimization. |
|---|---|
| Presentation Type | Paper |
| Authors | Rathnayaka, Madushan, Wijesundara, Kushan, Gunasekara, Chamila, Law, David W, Karunasinghe, Dulakshi and Lokuge, Weena |
| Journal or Proceedings Title | Proceedings of the Annual Conference of Japan Society of Material Cycles and Waste Management |
| Number of Pages | 2 |
| Year | 2024 |
| Place of Publication | Australia |
| Web Address (URL) of Paper | https://www.jstage.jst.go.jp/article/jsmcwm/t3rincs2024/0/t3rincs2024_43/_article/-char/ja/ |
| Conference/Event | 3R International Scientific Conference on Material Cycles and Waste Management (3RINCs 2024) |
| Event Details | 3R International Scientific Conference on Material Cycles and Waste Management (3RINCs 2024) Delivery In person Event Date 15 to end of 17 Mar 2024 Event Location Sydney, Australia Event Venue Hilton Hotel, Sydney Event Web Address (URL) |
| Abstract | Geopolymer concrete (GPC) offers a promising solution to the problems associated with OPC-based concrete, such as greenhouse gas emissions and natural resource depletion, by utilizing waste materials, like fly ash, to replace the cement in concrete (Ahmed et al., 2020). However, the heterogeneous nature of the fly ash has complicated the development of mixed design and the implementation of GPC in industrial applications (Luan et al., 2021). This study focuses on developing a mix design procedure to achieve a target compressive strength and suitable workability using advanced computational modeling tools, namely machine learning algorithms, data clustering, and optimization. This approach capture the complexities of the aluminum silicate material and develop standard mix proportions, irrespective of the source of fly ash. |
| Keywords | geopolymer cocnrete; fly ash; machine learning; compressive strength; workability |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 400505. Construction materials |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions, but may be accessed online. Please see the link in the URL field. |
| Byline Affiliations | Royal Melbourne Institute of Technology (RMIT) |
| University of Peradeniya, Sri Lanka | |
| School of Engineering |
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