Explainable machine learning model for predicting compressive strength of CO2-cured concrete
Compared to conventional concrete, the factors to determine the compressive strength of CO2-cured concrete are more complex, and thus, predicting its compressive strength becomes more difficult. Herein, an explainable machine learning (ML) model was developed to predict the compressive strength of C...
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| 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/S2214509525003870 |
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| author | Jia Chu Bingbing Guo Taotao Zhong Qinghao Guan Yan Wang Ditao Niu |
| author_facet | Jia Chu Bingbing Guo Taotao Zhong Qinghao Guan Yan Wang Ditao Niu |
| author_sort | Jia Chu |
| collection | DOAJ |
| description | Compared to conventional concrete, the factors to determine the compressive strength of CO2-cured concrete are more complex, and thus, predicting its compressive strength becomes more difficult. Herein, an explainable machine learning (ML) model was developed to predict the compressive strength of CO2-cured concrete. A comprehensive database comprising 198 datasets was collected from published experimental investigations, and four ML algorithms were employed, i.e., RF (random forest), SVR (support vector regression), GBRT (gradient boosting regression tree), and XGB (extreme gradient boosting). To enhance model accuracy and efficiency, K-fold cross-validation and grid search techniques were utilized for hyper-parameter tuning. Furthermore, to resolve the black-box issue associated with ML models, the SHAP (SHapley Additive exPlanations) method was applied to explore the underlying relationships among variables. Overall, all ML models (RF, GBRT, XGB) in this study except SVR proved capable of efficiently predicting the compressive strength of CO2-cured concrete, with XGB being chosen for further analysis in combination with SHAP due to its superior generalization ability. The SHAP analysis reveals that adding cement content is the key driver for increasing the compressive strength of CO2-cured concrete. In terms of CO2 curing parameters, prolonging CO2 curing durations moderately could improve the compressive strength, which can be attributed to the enhancement of carbonation degree. However, higher CO2 pressures may decrease the strength due to the increased risk of microcrack propagation caused by the synergistic effects of excessive pressure and considerable heat from the carbonation reaction. |
| format | Article |
| id | doaj-art-eaf3df84255e4939b16b906a33f97d8b |
| institution | DOAJ |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
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| series | Case Studies in Construction Materials |
| spelling | doaj-art-eaf3df84255e4939b16b906a33f97d8b2025-08-20T02:49:22ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e0458910.1016/j.cscm.2025.e04589Explainable machine learning model for predicting compressive strength of CO2-cured concreteJia Chu0Bingbing Guo1Taotao Zhong2Qinghao Guan3Yan Wang4Ditao Niu5College of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, ChinaCollege of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China; State Key Laboratory of Green Building, Xi'an University of Architecture and Technology, Xi'an 710055, China; Corresponding author at: College of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.College of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, ChinaDepartment of Communication and Media Research, University of Zurich, Zurich 8050, Switzerland; Corresponding author.State Key Laboratory of Green Building, Xi'an University of Architecture and Technology, Xi'an 710055, China; College of Materials Science and Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, ChinaCollege of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China; State Key Laboratory of Green Building, Xi'an University of Architecture and Technology, Xi'an 710055, ChinaCompared to conventional concrete, the factors to determine the compressive strength of CO2-cured concrete are more complex, and thus, predicting its compressive strength becomes more difficult. Herein, an explainable machine learning (ML) model was developed to predict the compressive strength of CO2-cured concrete. A comprehensive database comprising 198 datasets was collected from published experimental investigations, and four ML algorithms were employed, i.e., RF (random forest), SVR (support vector regression), GBRT (gradient boosting regression tree), and XGB (extreme gradient boosting). To enhance model accuracy and efficiency, K-fold cross-validation and grid search techniques were utilized for hyper-parameter tuning. Furthermore, to resolve the black-box issue associated with ML models, the SHAP (SHapley Additive exPlanations) method was applied to explore the underlying relationships among variables. Overall, all ML models (RF, GBRT, XGB) in this study except SVR proved capable of efficiently predicting the compressive strength of CO2-cured concrete, with XGB being chosen for further analysis in combination with SHAP due to its superior generalization ability. The SHAP analysis reveals that adding cement content is the key driver for increasing the compressive strength of CO2-cured concrete. In terms of CO2 curing parameters, prolonging CO2 curing durations moderately could improve the compressive strength, which can be attributed to the enhancement of carbonation degree. However, higher CO2 pressures may decrease the strength due to the increased risk of microcrack propagation caused by the synergistic effects of excessive pressure and considerable heat from the carbonation reaction.http://www.sciencedirect.com/science/article/pii/S2214509525003870CO2-cured concreteCompressive strengthMachine learningSHapley Additive exPlanations (SHAP) |
| spellingShingle | Jia Chu Bingbing Guo Taotao Zhong Qinghao Guan Yan Wang Ditao Niu Explainable machine learning model for predicting compressive strength of CO2-cured concrete Case Studies in Construction Materials CO2-cured concrete Compressive strength Machine learning SHapley Additive exPlanations (SHAP) |
| title | Explainable machine learning model for predicting compressive strength of CO2-cured concrete |
| title_full | Explainable machine learning model for predicting compressive strength of CO2-cured concrete |
| title_fullStr | Explainable machine learning model for predicting compressive strength of CO2-cured concrete |
| title_full_unstemmed | Explainable machine learning model for predicting compressive strength of CO2-cured concrete |
| title_short | Explainable machine learning model for predicting compressive strength of CO2-cured concrete |
| title_sort | explainable machine learning model for predicting compressive strength of co2 cured concrete |
| topic | CO2-cured concrete Compressive strength Machine learning SHapley Additive exPlanations (SHAP) |
| url | http://www.sciencedirect.com/science/article/pii/S2214509525003870 |
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