Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools
Rubberized cementitious composites have emerged as a sustainable alternative in the construction sector by promoting circular economy principles. However, their reduced compressive strength (CS) due to the inclusion of rubber remains a significant barrier to widespread adoption. While several experi...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-08-01
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| Series: | Reviews on Advanced Materials Science |
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| Online Access: | https://doi.org/10.1515/rams-2025-0135 |
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| author | Fu Limei Xu Feng |
| author_facet | Fu Limei Xu Feng |
| author_sort | Fu Limei |
| collection | DOAJ |
| description | Rubberized cementitious composites have emerged as a sustainable alternative in the construction sector by promoting circular economy principles. However, their reduced compressive strength (CS) due to the inclusion of rubber remains a significant barrier to widespread adoption. While several experimental studies exist, there is a clear gap in utilizing data-driven strategies to efficiently predict and optimize the strength performance of such materials. This research addresses this gap by evaluating the predictability of machine learning approaches for evaluating the CS of rubberized mortar (RM) incorporating supplementary cementitious materials. Among the tested algorithms, including bagging, gradient boosting, and AdaBoost, the bagging model achieved the highest accuracy (R
2 = 0.975). SHapley Additive exPlanations analysis further identified cement and sand content as the most influential variables affecting CS. The findings were integrated into a graphical user interface for practical, real-time strength estimation. This tool can support engineers and material designers in developing sustainable RM mixes with improved performance and reduced reliance on extensive laboratory testing. |
| format | Article |
| id | doaj-art-a4030a3dbf8546d889e009d43d5f011e |
| institution | Kabale University |
| issn | 1605-8127 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Reviews on Advanced Materials Science |
| spelling | doaj-art-a4030a3dbf8546d889e009d43d5f011e2025-08-20T04:02:32ZengDe GruyterReviews on Advanced Materials Science1605-81272025-08-01641pp. 10512510.1515/rams-2025-0135Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI toolsFu Limei0Xu Feng1Accounting College, Hainan Vocational University of Science and Technology, Haikou, Hainan, 571126, ChinaAccounting College, Hainan Vocational University of Science and Technology, Haikou, Hainan, 571126, ChinaRubberized cementitious composites have emerged as a sustainable alternative in the construction sector by promoting circular economy principles. However, their reduced compressive strength (CS) due to the inclusion of rubber remains a significant barrier to widespread adoption. While several experimental studies exist, there is a clear gap in utilizing data-driven strategies to efficiently predict and optimize the strength performance of such materials. This research addresses this gap by evaluating the predictability of machine learning approaches for evaluating the CS of rubberized mortar (RM) incorporating supplementary cementitious materials. Among the tested algorithms, including bagging, gradient boosting, and AdaBoost, the bagging model achieved the highest accuracy (R 2 = 0.975). SHapley Additive exPlanations analysis further identified cement and sand content as the most influential variables affecting CS. The findings were integrated into a graphical user interface for practical, real-time strength estimation. This tool can support engineers and material designers in developing sustainable RM mixes with improved performance and reduced reliance on extensive laboratory testing.https://doi.org/10.1515/rams-2025-0135rubberized mortarcompressive strengthsupplementary cementitious materials |
| spellingShingle | Fu Limei Xu Feng Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools Reviews on Advanced Materials Science rubberized mortar compressive strength supplementary cementitious materials |
| title | Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools |
| title_full | Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools |
| title_fullStr | Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools |
| title_full_unstemmed | Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools |
| title_short | Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools |
| title_sort | establishing strength prediction models for low carbon rubberized cementitious mortar using advanced ai tools |
| topic | rubberized mortar compressive strength supplementary cementitious materials |
| url | https://doi.org/10.1515/rams-2025-0135 |
| work_keys_str_mv | AT fulimei establishingstrengthpredictionmodelsforlowcarbonrubberizedcementitiousmortarusingadvancedaitools AT xufeng establishingstrengthpredictionmodelsforlowcarbonrubberizedcementitiousmortarusingadvancedaitools |