Multi-objective optimisation for mortar containing activated waste glass powder
Waste glass is inert and non-degradable which leads to enormous environmental and sustainability troubles, but it can be reused in concrete due to the potential of the pozzolanic activity. This study proposes methods on activity excitation of waste glass powder (WGP) including mechanical, chemical,...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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ELSEVIER
2022
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90921 |
| _version_ | 1848765461219508224 |
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| author | Sun, J. Yue, L. Xu, K. He, R. Yao, X. Chen, M. Cai, T. Wang, Xiangyu Wang, Yufei |
| author_facet | Sun, J. Yue, L. Xu, K. He, R. Yao, X. Chen, M. Cai, T. Wang, Xiangyu Wang, Yufei |
| author_sort | Sun, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Waste glass is inert and non-degradable which leads to enormous environmental and sustainability troubles, but it can be reused in concrete due to the potential of the pozzolanic activity. This study proposes methods on activity excitation of waste glass powder (WGP) including mechanical, chemical, and mechanical-chemical activation. The results showed that the mortar containing 30% 75 μm WGP activated by the mechanical-chemical method was optimal to increase the mechanical property and reduce the detrimental expansion. In addition, the microstructural analysis was conducted to explore the activation effect on WGP and WGP-cement system. An artificial intelligence (AI) based multi-objective optimisation (MOO) model was proposed to seek the optimal mix proportions for the unconfined compression strength (UCS), alkali-silica reaction (ASR), and cost. A comprehensive dataset was investigated including 549 specimens for the UCS test and 366 test results for the expansion test. Random Forest (RF) model was utilized for the prediction of UCS and ASR values with hyperparameters tuned by a firefly algorithm (FA). The high correlation coefficients (0.93 for UCS and 0.91 for ASR) verified the feasibility of FA-RF. Subsequently, the FA-RF model was extended as the objective function for the mi-objective firefly algorithm (MOFA-RF) to obtain the consequent Pareto fronts. This paper combined the results of experiments, machine learning prediction, and multi-objective optimisation design for activated WGP mortar, which provided a comprehensive basis for the practical application. |
| first_indexed | 2025-11-14T11:35:37Z |
| format | Journal Article |
| id | curtin-20.500.11937-90921 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:37Z |
| publishDate | 2022 |
| publisher | ELSEVIER |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-909212023-05-11T03:22:55Z Multi-objective optimisation for mortar containing activated waste glass powder Sun, J. Yue, L. Xu, K. He, R. Yao, X. Chen, M. Cai, T. Wang, Xiangyu Wang, Yufei Science & Technology Technology Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Materials Science Waste glass powder Activation methodology Multi-objective optimisation Machine learning Unconfined compressive strength Alkali-silica reaction RADIATION SHIELDING PROPERTIES CONCRETE STRENGTH DURABILITY PREDICTION EXPANSION ALGORITHM HYDRATION BEHAVIOR SILICA Waste glass is inert and non-degradable which leads to enormous environmental and sustainability troubles, but it can be reused in concrete due to the potential of the pozzolanic activity. This study proposes methods on activity excitation of waste glass powder (WGP) including mechanical, chemical, and mechanical-chemical activation. The results showed that the mortar containing 30% 75 μm WGP activated by the mechanical-chemical method was optimal to increase the mechanical property and reduce the detrimental expansion. In addition, the microstructural analysis was conducted to explore the activation effect on WGP and WGP-cement system. An artificial intelligence (AI) based multi-objective optimisation (MOO) model was proposed to seek the optimal mix proportions for the unconfined compression strength (UCS), alkali-silica reaction (ASR), and cost. A comprehensive dataset was investigated including 549 specimens for the UCS test and 366 test results for the expansion test. Random Forest (RF) model was utilized for the prediction of UCS and ASR values with hyperparameters tuned by a firefly algorithm (FA). The high correlation coefficients (0.93 for UCS and 0.91 for ASR) verified the feasibility of FA-RF. Subsequently, the FA-RF model was extended as the objective function for the mi-objective firefly algorithm (MOFA-RF) to obtain the consequent Pareto fronts. This paper combined the results of experiments, machine learning prediction, and multi-objective optimisation design for activated WGP mortar, which provided a comprehensive basis for the practical application. 2022 Journal Article http://hdl.handle.net/20.500.11937/90921 10.1016/j.jmrt.2022.02.123 English http://purl.org/au-research/grants/arc/LP180100222 http://creativecommons.org/licenses/by-nc-nd/4.0/ ELSEVIER fulltext |
| spellingShingle | Science & Technology Technology Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Materials Science Waste glass powder Activation methodology Multi-objective optimisation Machine learning Unconfined compressive strength Alkali-silica reaction RADIATION SHIELDING PROPERTIES CONCRETE STRENGTH DURABILITY PREDICTION EXPANSION ALGORITHM HYDRATION BEHAVIOR SILICA Sun, J. Yue, L. Xu, K. He, R. Yao, X. Chen, M. Cai, T. Wang, Xiangyu Wang, Yufei Multi-objective optimisation for mortar containing activated waste glass powder |
| title | Multi-objective optimisation for mortar containing activated waste glass powder |
| title_full | Multi-objective optimisation for mortar containing activated waste glass powder |
| title_fullStr | Multi-objective optimisation for mortar containing activated waste glass powder |
| title_full_unstemmed | Multi-objective optimisation for mortar containing activated waste glass powder |
| title_short | Multi-objective optimisation for mortar containing activated waste glass powder |
| title_sort | multi-objective optimisation for mortar containing activated waste glass powder |
| topic | Science & Technology Technology Materials Science, Multidisciplinary Metallurgy & Metallurgical Engineering Materials Science Waste glass powder Activation methodology Multi-objective optimisation Machine learning Unconfined compressive strength Alkali-silica reaction RADIATION SHIELDING PROPERTIES CONCRETE STRENGTH DURABILITY PREDICTION EXPANSION ALGORITHM HYDRATION BEHAVIOR SILICA |
| url | http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90921 |