Interpretable machine learning models for evaluating strength of ternary geopolymers
Ternary geopolymers incorporating multiple solid wastes such as steel slag (SS), fly ash (FA), and granulated blast furnace slag (GBFS) are considered environmentally friendly and exhibit enhanced performance. However, the mechanisms governing strength development and the design of optimal mixtures...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-12-01
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| Series: | Artificial Intelligence in Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666544125000243 |
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| author | Junfei Zhang Huisheng Cheng Ninghui Sun Zehui Huo Junlin Chen |
| author_facet | Junfei Zhang Huisheng Cheng Ninghui Sun Zehui Huo Junlin Chen |
| author_sort | Junfei Zhang |
| collection | DOAJ |
| description | Ternary geopolymers incorporating multiple solid wastes such as steel slag (SS), fly ash (FA), and granulated blast furnace slag (GBFS) are considered environmentally friendly and exhibit enhanced performance. However, the mechanisms governing strength development and the design of optimal mixtures are not fully understood due to the complexity of their components. This study presents the development of four machine learning models—Artificial Neural Network (ANN), Support Vector Regression (SVR), Extremely Randomized Tree (ERT), and Gradient Boosting Regression (GBR)—for predicting the unconfined compressive strength (UCS) of ternary geopolymers. The models were trained using a dataset comprising 120 mixtures derived from laboratory tests. Shapley Additive Explanations analysis was employed to interpret the machine learning models and elucidate the influence of different components on the properties of ternary geopolymers. The results indicate that ANN exhibits the highest predictive accuracy for UCS (R = 0.949). Furthermore, the UCS of ternary geopolymers is most sensitive to the content of GBFS. This study provides valuable insights for optimizing the mix proportions in ternary blended geopolymer mixtures. |
| format | Article |
| id | doaj-art-5cc60826e577446aa36ef5732f2680bd |
| institution | DOAJ |
| issn | 2666-5441 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Geosciences |
| spelling | doaj-art-5cc60826e577446aa36ef5732f2680bd2025-08-20T03:15:03ZengKeAi Communications Co. Ltd.Artificial Intelligence in Geosciences2666-54412025-12-016210012810.1016/j.aiig.2025.100128Interpretable machine learning models for evaluating strength of ternary geopolymersJunfei Zhang0Huisheng Cheng1Ninghui Sun2Zehui Huo3Junlin Chen4Corresponding author.; School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, China; School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW 2052, Australia; School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China; CCCC Tianjin Port Engineering Design & Consulting Co., Ltd., Tianjin, 300461, ChinaSchool of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, China; School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW 2052, Australia; School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China; CCCC Tianjin Port Engineering Design & Consulting Co., Ltd., Tianjin, 300461, ChinaCorresponding author.; School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, China; School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW 2052, Australia; School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China; CCCC Tianjin Port Engineering Design & Consulting Co., Ltd., Tianjin, 300461, ChinaSchool of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, China; School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW 2052, Australia; School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China; CCCC Tianjin Port Engineering Design & Consulting Co., Ltd., Tianjin, 300461, ChinaSchool of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, China; School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW 2052, Australia; School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China; CCCC Tianjin Port Engineering Design & Consulting Co., Ltd., Tianjin, 300461, ChinaTernary geopolymers incorporating multiple solid wastes such as steel slag (SS), fly ash (FA), and granulated blast furnace slag (GBFS) are considered environmentally friendly and exhibit enhanced performance. However, the mechanisms governing strength development and the design of optimal mixtures are not fully understood due to the complexity of their components. This study presents the development of four machine learning models—Artificial Neural Network (ANN), Support Vector Regression (SVR), Extremely Randomized Tree (ERT), and Gradient Boosting Regression (GBR)—for predicting the unconfined compressive strength (UCS) of ternary geopolymers. The models were trained using a dataset comprising 120 mixtures derived from laboratory tests. Shapley Additive Explanations analysis was employed to interpret the machine learning models and elucidate the influence of different components on the properties of ternary geopolymers. The results indicate that ANN exhibits the highest predictive accuracy for UCS (R = 0.949). Furthermore, the UCS of ternary geopolymers is most sensitive to the content of GBFS. This study provides valuable insights for optimizing the mix proportions in ternary blended geopolymer mixtures.http://www.sciencedirect.com/science/article/pii/S2666544125000243GeopolymerSolid wasteMix proportionMachine learningUnconfined compressive strength |
| spellingShingle | Junfei Zhang Huisheng Cheng Ninghui Sun Zehui Huo Junlin Chen Interpretable machine learning models for evaluating strength of ternary geopolymers Artificial Intelligence in Geosciences Geopolymer Solid waste Mix proportion Machine learning Unconfined compressive strength |
| title | Interpretable machine learning models for evaluating strength of ternary geopolymers |
| title_full | Interpretable machine learning models for evaluating strength of ternary geopolymers |
| title_fullStr | Interpretable machine learning models for evaluating strength of ternary geopolymers |
| title_full_unstemmed | Interpretable machine learning models for evaluating strength of ternary geopolymers |
| title_short | Interpretable machine learning models for evaluating strength of ternary geopolymers |
| title_sort | interpretable machine learning models for evaluating strength of ternary geopolymers |
| topic | Geopolymer Solid waste Mix proportion Machine learning Unconfined compressive strength |
| url | http://www.sciencedirect.com/science/article/pii/S2666544125000243 |
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