Machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite
Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before reusing WG, the flexural strength (FS) and alkali–silica reaction (ASR) expansion of WG concrete are two ess...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | English |
| Published: |
MDPI
2021
|
| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90922 |
| _version_ | 1848765461511012352 |
|---|---|
| author | Sun, Junbo Wang, Y. Yao, X. Ren, Z. Zhang, G. Zhang, C. Chen, X. Ma, W. Wang, Xiangyu |
| author_facet | Sun, Junbo Wang, Y. Yao, X. Ren, Z. Zhang, G. Zhang, C. Chen, X. Ma, W. Wang, Xiangyu |
| author_sort | Sun, Junbo |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before reusing WG, the flexural strength (FS) and alkali–silica reaction (ASR) expansion of WG concrete are two essential properties that must be investigated. This study produced mortar containing activated glass powder using mechanical, chemical, and mechanical–chemical (combined) approaches. The results showed that mortar containing 30% WG powder using the combined method was optimal for improving the FS and mitigating the ASR expansion. The microstructure analysis was implemented to explore the activation effect on the glass powder and mortar. Moreover, a random forest (RF) model was proposed with hyperparameters tuned by beetle antennae search (BAS), aiming at predicting FS and ASR expansion precisely. A large database was established from the experimental results based on 549 samples prepared for the FS test and 183 samples produced for the expansion test. The BAS-RF model presented high correlation coefficients for both FS (0.9545) and ASR (0.9416) data sets, showing much higher accuracy than multiple linear regression and logistic regression. Finally, a sensitivity analysis was conducted to rank the variables based on importance. Apart from the curing time, the particle granularity and content of WG were demonstrated to be the most sensitive variable for FS and expansion, respectively. |
| first_indexed | 2025-11-14T11:35:37Z |
| format | Journal Article |
| id | curtin-20.500.11937-90922 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:35:37Z |
| publishDate | 2021 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-909222023-05-08T07:43:54Z Machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite Sun, Junbo Wang, Y. Yao, X. Ren, Z. Zhang, G. Zhang, C. Chen, X. Ma, W. Wang, Xiangyu Science & Technology Physical Sciences Technology Chemistry, Multidisciplinary Engineering, Multidisciplinary Materials Science, Multidisciplinary Physics, Applied Chemistry Engineering Materials Science Physics random forest beetle antennae search activation methodology machine learning flexural strength COMPRESSIVE STRENGTH CONCRETE KNOWLEDGE FRAMEWORK BEHAVIOR SEARCH SILICA SAND Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before reusing WG, the flexural strength (FS) and alkali–silica reaction (ASR) expansion of WG concrete are two essential properties that must be investigated. This study produced mortar containing activated glass powder using mechanical, chemical, and mechanical–chemical (combined) approaches. The results showed that mortar containing 30% WG powder using the combined method was optimal for improving the FS and mitigating the ASR expansion. The microstructure analysis was implemented to explore the activation effect on the glass powder and mortar. Moreover, a random forest (RF) model was proposed with hyperparameters tuned by beetle antennae search (BAS), aiming at predicting FS and ASR expansion precisely. A large database was established from the experimental results based on 549 samples prepared for the FS test and 183 samples produced for the expansion test. The BAS-RF model presented high correlation coefficients for both FS (0.9545) and ASR (0.9416) data sets, showing much higher accuracy than multiple linear regression and logistic regression. Finally, a sensitivity analysis was conducted to rank the variables based on importance. Apart from the curing time, the particle granularity and content of WG were demonstrated to be the most sensitive variable for FS and expansion, respectively. 2021 Journal Article http://hdl.handle.net/20.500.11937/90922 10.3390/app11156686 English http://purl.org/au-research/grants/arc/LP180100222 http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Science & Technology Physical Sciences Technology Chemistry, Multidisciplinary Engineering, Multidisciplinary Materials Science, Multidisciplinary Physics, Applied Chemistry Engineering Materials Science Physics random forest beetle antennae search activation methodology machine learning flexural strength COMPRESSIVE STRENGTH CONCRETE KNOWLEDGE FRAMEWORK BEHAVIOR SEARCH SILICA SAND Sun, Junbo Wang, Y. Yao, X. Ren, Z. Zhang, G. Zhang, C. Chen, X. Ma, W. Wang, Xiangyu Machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite |
| title | Machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite |
| title_full | Machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite |
| title_fullStr | Machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite |
| title_full_unstemmed | Machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite |
| title_short | Machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite |
| title_sort | machine-learning-aided prediction of flexural strength and asr expansion for waste glass cementitious composite |
| topic | Science & Technology Physical Sciences Technology Chemistry, Multidisciplinary Engineering, Multidisciplinary Materials Science, Multidisciplinary Physics, Applied Chemistry Engineering Materials Science Physics random forest beetle antennae search activation methodology machine learning flexural strength COMPRESSIVE STRENGTH CONCRETE KNOWLEDGE FRAMEWORK BEHAVIOR SEARCH SILICA SAND |
| url | http://purl.org/au-research/grants/arc/LP180100222 http://hdl.handle.net/20.500.11937/90922 |