Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete
This paper used machine learning to model the prediction of carbonation depth and the analysis of feature parameters for recycled aggregate concrete (RAC). Specifically, a database containing 579 sets of RAC carbonation test data was developed. Twelve parameters representing material characteristics...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Case Studies in Construction Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509524013147 |
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| author | Xuyong Chen Xuan Liu Shukai Cheng Xiaoya Bian Xixuan Bai Xin Zheng Xiong Xu Zhifeng Xu |
| author_facet | Xuyong Chen Xuan Liu Shukai Cheng Xiaoya Bian Xixuan Bai Xin Zheng Xiong Xu Zhifeng Xu |
| author_sort | Xuyong Chen |
| collection | DOAJ |
| description | This paper used machine learning to model the prediction of carbonation depth and the analysis of feature parameters for recycled aggregate concrete (RAC). Specifically, a database containing 579 sets of RAC carbonation test data was developed. Twelve parameters representing material characteristics and environmental conditions were input, along with one output parameter. On this basis, six machine learning models were employed to predict RAC carbonation depth: Artificial Neural Network, Decision Tree, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting. Different types of analysis, including statistical measures, shapley additive explanations (SHAP) sensitivity analysis, SHAP parametric study, and comparison study, were used to examine the performance of the developed models and the effects of the input parameters on predictions. The results show that the extreme gradient boosting (XGB) model exhibited the highest accuracy with an R² of 0.99 and a mean absolute percentage error (MAPE) of 6.632 on the training set, and an R² of 0.953 and a MAPE of 13.243 on the test set. The variable importance analysis shows that the carbonation depth for RAC was determined by both internal and external factors. The top five factors impacting RAC carbonation depth are exposure time, water-to-binder ratio, CO2 concentration, coarse aggregate density, and cement content. RAC carbonation depth correlates positively with the former three factors. In contrast, it exhibits a negative correlation with the remaining two variables. In addition, a graphical user interface (GUI) for RAC carbonation depth prediction was designed. |
| format | Article |
| id | doaj-art-81ed9d5e6caf4cd9b76f89f6bf8b0364 |
| institution | Kabale University |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-81ed9d5e6caf4cd9b76f89f6bf8b03642025-01-03T04:08:43ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e04162Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concreteXuyong Chen0Xuan Liu1Shukai Cheng2Xiaoya Bian3Xixuan Bai4Xin Zheng5Xiong Xu6Zhifeng Xu7School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China; Hubei Engineering Research Center for Green Civil Engineering Materials and Structures, Wuhan 430074, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China; Hubei Engineering Research Center for Green Civil Engineering Materials and Structures, Wuhan 430074, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China; Hubei Engineering Research Center for Green Civil Engineering Materials and Structures, Wuhan 430074, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China; Hubei Engineering Research Center for Green Civil Engineering Materials and Structures, Wuhan 430074, China; Corresponding author at: School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China; Hubei Engineering Research Center for Green Civil Engineering Materials and Structures, Wuhan 430074, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China; Hubei Engineering Research Center for Green Civil Engineering Materials and Structures, Wuhan 430074, ChinaThis paper used machine learning to model the prediction of carbonation depth and the analysis of feature parameters for recycled aggregate concrete (RAC). Specifically, a database containing 579 sets of RAC carbonation test data was developed. Twelve parameters representing material characteristics and environmental conditions were input, along with one output parameter. On this basis, six machine learning models were employed to predict RAC carbonation depth: Artificial Neural Network, Decision Tree, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting. Different types of analysis, including statistical measures, shapley additive explanations (SHAP) sensitivity analysis, SHAP parametric study, and comparison study, were used to examine the performance of the developed models and the effects of the input parameters on predictions. The results show that the extreme gradient boosting (XGB) model exhibited the highest accuracy with an R² of 0.99 and a mean absolute percentage error (MAPE) of 6.632 on the training set, and an R² of 0.953 and a MAPE of 13.243 on the test set. The variable importance analysis shows that the carbonation depth for RAC was determined by both internal and external factors. The top five factors impacting RAC carbonation depth are exposure time, water-to-binder ratio, CO2 concentration, coarse aggregate density, and cement content. RAC carbonation depth correlates positively with the former three factors. In contrast, it exhibits a negative correlation with the remaining two variables. In addition, a graphical user interface (GUI) for RAC carbonation depth prediction was designed.http://www.sciencedirect.com/science/article/pii/S2214509524013147Recycled aggregate concreteCarbonation depthMachine learningSHAP analysis |
| spellingShingle | Xuyong Chen Xuan Liu Shukai Cheng Xiaoya Bian Xixuan Bai Xin Zheng Xiong Xu Zhifeng Xu Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete Case Studies in Construction Materials Recycled aggregate concrete Carbonation depth Machine learning SHAP analysis |
| title | Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete |
| title_full | Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete |
| title_fullStr | Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete |
| title_full_unstemmed | Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete |
| title_short | Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete |
| title_sort | machine learning based modelling and analysis of carbonation depth of recycled aggregate concrete |
| topic | Recycled aggregate concrete Carbonation depth Machine learning SHAP analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2214509524013147 |
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