Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model
Concrete carbonation is a critical factor influencing the durability and longevity of concrete structures, particularly in urban environments. Traditional methods for predicting the anti-carbonation performance (ACP) of concrete often lack precision and fail to account for complex interactions betw...
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| Format: | Article |
| Language: | English |
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Vilnius Gediminas Technical University
2025-05-01
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| Series: | Journal of Environmental Engineering and Landscape Management |
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| Online Access: | https://journals.vilniustech.lt/index.php/JEELM/article/view/23568 |
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| author | Sivaraja M. Swaminathen A. N. Kuttimarks M. S. Rajprasad J. Sakthivel M. Rex J. |
| author_facet | Sivaraja M. Swaminathen A. N. Kuttimarks M. S. Rajprasad J. Sakthivel M. Rex J. |
| author_sort | Sivaraja M. |
| collection | DOAJ |
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Concrete carbonation is a critical factor influencing the durability and longevity of concrete structures, particularly in urban environments. Traditional methods for predicting the anti-carbonation performance (ACP) of concrete often lack precision and fail to account for complex interactions between influencing factors. In this study, a novel hybrid model combining random forest (RF) regression with a least squares support vector machine (LSSVM) is proposed to enhance the accuracy of ACP predictions. The RF regression is utilized for feature selection, identifying the most significant factors affecting ACP and optimizing the input features for the LSSVM model. Our hybrid model is validated against a comprehensive dataset, demonstrating superior performance in predicting concrete carbonation resistance compared to conventional methods. Quantitative results show that the RF-LSSVM model achieves a root mean square error (RMSE) of 5e–5 and a high coefficient of determination (R-squared) of 0.999, indicating robust predictive capability and accuracy. The main novelty of this work lies in the integration of RF and LSSVM to create a robust, accurate, and practical tool for assessing the durability of concrete structures.
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| format | Article |
| id | doaj-art-e47a784fbbe94cc497e21569c98178b6 |
| institution | DOAJ |
| issn | 1648-6897 1822-4199 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Journal of Environmental Engineering and Landscape Management |
| spelling | doaj-art-e47a784fbbe94cc497e21569c98178b62025-08-20T03:08:51ZengVilnius Gediminas Technical UniversityJournal of Environmental Engineering and Landscape Management1648-68971822-41992025-05-0133210.3846/jeelm.2025.23568Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine modelSivaraja M.0Swaminathen A. N.1Kuttimarks M. S.2Rajprasad J.3Sakthivel M.4Rex J.5Department of Civil Engineering, Nehru Institute of Technology, Coimbatore, 641105 Tamilnadu, IndiaDepartment of Civil Engineering, Adi Shankara Institute of Engineering and Technology, Kalady, KeralaDepartment of Civil Engineering, Shivajirao S. Jondhle College of Engineering & Technology, Thane, MaharastraDepartment of Civil Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, 603203 Kattakulathur, Tamilnadu, IndiaDepartment of Civil Engineering, Kongunadu College of Engineering and Technology, 621215 Trichy, IndiaDepartment of Civil Engineering, CK College of Engineering & Technology, 607003 Cuddalore, Tamilnadu, India Concrete carbonation is a critical factor influencing the durability and longevity of concrete structures, particularly in urban environments. Traditional methods for predicting the anti-carbonation performance (ACP) of concrete often lack precision and fail to account for complex interactions between influencing factors. In this study, a novel hybrid model combining random forest (RF) regression with a least squares support vector machine (LSSVM) is proposed to enhance the accuracy of ACP predictions. The RF regression is utilized for feature selection, identifying the most significant factors affecting ACP and optimizing the input features for the LSSVM model. Our hybrid model is validated against a comprehensive dataset, demonstrating superior performance in predicting concrete carbonation resistance compared to conventional methods. Quantitative results show that the RF-LSSVM model achieves a root mean square error (RMSE) of 5e–5 and a high coefficient of determination (R-squared) of 0.999, indicating robust predictive capability and accuracy. The main novelty of this work lies in the integration of RF and LSSVM to create a robust, accurate, and practical tool for assessing the durability of concrete structures. https://journals.vilniustech.lt/index.php/JEELM/article/view/23568carbonation resistanceleast squares support vector machine (LSSVM) modelsafety assessmentrandom forest (RF)concrete |
| spellingShingle | Sivaraja M. Swaminathen A. N. Kuttimarks M. S. Rajprasad J. Sakthivel M. Rex J. Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model Journal of Environmental Engineering and Landscape Management carbonation resistance least squares support vector machine (LSSVM) model safety assessment random forest (RF) concrete |
| title | Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model |
| title_full | Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model |
| title_fullStr | Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model |
| title_full_unstemmed | Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model |
| title_short | Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model |
| title_sort | prediction of the anti carbonation performance of concrete based on random forest least squares support vector machine model |
| topic | carbonation resistance least squares support vector machine (LSSVM) model safety assessment random forest (RF) concrete |
| url | https://journals.vilniustech.lt/index.php/JEELM/article/view/23568 |
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