Machine learning for efficient CO2 sequestration in cementitious materials: a data-driven method
Abstract Extensive experimental work has proved that CO2 sequestration by cementitious materials offers a promising venue for addressing the rising carbon emissions problem. However, relying merely on experiments on specific materials or some simple empirical methods makes it difficult to provide a...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | npj Materials Sustainability |
| Online Access: | https://doi.org/10.1038/s44296-025-00053-z |
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| author | Yanjie SUN Chen ZHANG Yuan-Hao WEI Haoliang JIN Peiliang SHEN Chi Sun POON He YAN Xiao-Yong WEI |
| author_facet | Yanjie SUN Chen ZHANG Yuan-Hao WEI Haoliang JIN Peiliang SHEN Chi Sun POON He YAN Xiao-Yong WEI |
| author_sort | Yanjie SUN |
| collection | DOAJ |
| description | Abstract Extensive experimental work has proved that CO2 sequestration by cementitious materials offers a promising venue for addressing the rising carbon emissions problem. However, relying merely on experiments on specific materials or some simple empirical methods makes it difficult to provide a comprehensive understanding. To address these challenges, this paper applies three advanced machine-learning techniques (Decision Tree, Random Forest, and eXtreme Gradient Boosting (XGBoost)), with existing datasets coupling with data collected from the literature. The results show that the XGBoost model significantly outperforms traditional linear regression approaches. In addition, aiding in the SHapley Additive exPlanations(SHAP), apart from the widely recognized factors, cement type was also investigated and shown its crucial role in affecting carbonation depth. CEM II/B-LL and CEM II/B-M are two types having high carbonation potential. The results enable the identification of key factors influencing CO2 sequestration through cement and provide insights into optimizing experimental design. |
| format | Article |
| id | doaj-art-ddc0aa98a4484f69a506b095a8ff6a44 |
| institution | OA Journals |
| issn | 2948-1775 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Materials Sustainability |
| spelling | doaj-art-ddc0aa98a4484f69a506b095a8ff6a442025-08-20T01:54:22ZengNature Portfolionpj Materials Sustainability2948-17752025-04-01311910.1038/s44296-025-00053-zMachine learning for efficient CO2 sequestration in cementitious materials: a data-driven methodYanjie SUN0Chen ZHANG1Yuan-Hao WEI2Haoliang JIN3Peiliang SHEN4Chi Sun POON5He YAN6Xiao-Yong WEI7Department of Computing, The Hong Kong Polytechnic UniversityDepartment of Computing, The Hong Kong Polytechnic UniversityDepartment of Computing, The Hong Kong Polytechnic UniversityDepartment of Materials Science and Engineering, University of SheffieldDepartment of Civil and Environmental Engineering, The Hong Kong Polytechnic UniversityDepartment of Civil and Environmental Engineering, The Hong Kong Polytechnic UniversityDepartment of Chemistry, Hong Kong University of Science and TechnologyDepartment of Computing, The Hong Kong Polytechnic UniversityAbstract Extensive experimental work has proved that CO2 sequestration by cementitious materials offers a promising venue for addressing the rising carbon emissions problem. However, relying merely on experiments on specific materials or some simple empirical methods makes it difficult to provide a comprehensive understanding. To address these challenges, this paper applies three advanced machine-learning techniques (Decision Tree, Random Forest, and eXtreme Gradient Boosting (XGBoost)), with existing datasets coupling with data collected from the literature. The results show that the XGBoost model significantly outperforms traditional linear regression approaches. In addition, aiding in the SHapley Additive exPlanations(SHAP), apart from the widely recognized factors, cement type was also investigated and shown its crucial role in affecting carbonation depth. CEM II/B-LL and CEM II/B-M are two types having high carbonation potential. The results enable the identification of key factors influencing CO2 sequestration through cement and provide insights into optimizing experimental design.https://doi.org/10.1038/s44296-025-00053-z |
| spellingShingle | Yanjie SUN Chen ZHANG Yuan-Hao WEI Haoliang JIN Peiliang SHEN Chi Sun POON He YAN Xiao-Yong WEI Machine learning for efficient CO2 sequestration in cementitious materials: a data-driven method npj Materials Sustainability |
| title | Machine learning for efficient CO2 sequestration in cementitious materials: a data-driven method |
| title_full | Machine learning for efficient CO2 sequestration in cementitious materials: a data-driven method |
| title_fullStr | Machine learning for efficient CO2 sequestration in cementitious materials: a data-driven method |
| title_full_unstemmed | Machine learning for efficient CO2 sequestration in cementitious materials: a data-driven method |
| title_short | Machine learning for efficient CO2 sequestration in cementitious materials: a data-driven method |
| title_sort | machine learning for efficient co2 sequestration in cementitious materials a data driven method |
| url | https://doi.org/10.1038/s44296-025-00053-z |
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