NGBoost-based prediction of carbonation in Alkali-activated slag concrete enhanced by metaheuristic algorithms
Concrete carbonation represents a critical deterioration mechanism that significantly compromises the long-term structural integrity of reinforced concrete systems. Traditional methods for assessing carbonation depth (CD) in Alkali-activated slag concrete (AASC) are labor-intensive, costly, and dema...
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| Veröffentlicht in: | Construction & building materials Jg. 500; S. 144257 |
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| Sprache: | Englisch |
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21.11.2025
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| ISSN: | 0950-0618 |
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| Abstract | Concrete carbonation represents a critical deterioration mechanism that significantly compromises the long-term structural integrity of reinforced concrete systems. Traditional methods for assessing carbonation depth (CD) in Alkali-activated slag concrete (AASC) are labor-intensive, costly, and demand specialized expertise. To address the current gap in applying machine learning (ML) techniques for accurate CD prediction in AASC, this study developed ML frameworks for carbonation forecasting by integrating natural gradient boosting (NGBoost) with three bio-inspired optimization techniques: beluga whale optimization (BWO), remora optimization (ROA), and crayfish optimization (COA). The models were trained on a dataset of 136 data points with 9 input variables. The study employed a comprehensive evaluation framework incorporating four key metrics (Coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE)) and Taylor diagram analysis to assess comparative model performance. The results showed that the COA-NGBoost algorithm achieved superior performance with R2 of 0.946, MAPE of 0.145, MAE of 1.451 mm, and RMSE of 2.099 mm. Shapley additive explanations (SHAP) analysis identified environmental conditions, specifically CO2 concentration and exposure time, as key factors influencing CD. Furthermore, an interactive software tool incorporating the COA-NGBoost framework was developed to facilitate probabilistic CD prediction and optimize AASC mixture proportioning. This investigation demonstrates the successful implementation of the proposed framework in parametric analysis of CD factors within AASC systems, providing valuable insights for durability-based design strategies.
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•A novel natural gradient boosting (NGBoost) model accurately predicts carbonation depth (CD) in alkali-activated slag concrete (AASC).•Hyperparameter optimization using three novel bio-inspired algorithms, significantly improves NGBoost model performance.•The optimized COA-NGBoost model demonstrates exceptional accuracy (R2 = 0.946) and low error metrics.•SHAP analysis reveals CO2 concentration and exposure time as primary drivers of AASC carbonation.•A GUI enables easy prediction of AASC-CD progression, facilitating practical implementation in infrastructure management. |
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| AbstractList | Concrete carbonation represents a critical deterioration mechanism that significantly compromises the long-term structural integrity of reinforced concrete systems. Traditional methods for assessing carbonation depth (CD) in Alkali-activated slag concrete (AASC) are labor-intensive, costly, and demand specialized expertise. To address the current gap in applying machine learning (ML) techniques for accurate CD prediction in AASC, this study developed ML frameworks for carbonation forecasting by integrating natural gradient boosting (NGBoost) with three bio-inspired optimization techniques: beluga whale optimization (BWO), remora optimization (ROA), and crayfish optimization (COA). The models were trained on a dataset of 136 data points with 9 input variables. The study employed a comprehensive evaluation framework incorporating four key metrics (Coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE)) and Taylor diagram analysis to assess comparative model performance. The results showed that the COA-NGBoost algorithm achieved superior performance with R2 of 0.946, MAPE of 0.145, MAE of 1.451 mm, and RMSE of 2.099 mm. Shapley additive explanations (SHAP) analysis identified environmental conditions, specifically CO2 concentration and exposure time, as key factors influencing CD. Furthermore, an interactive software tool incorporating the COA-NGBoost framework was developed to facilitate probabilistic CD prediction and optimize AASC mixture proportioning. This investigation demonstrates the successful implementation of the proposed framework in parametric analysis of CD factors within AASC systems, providing valuable insights for durability-based design strategies.
[Display omitted]
•A novel natural gradient boosting (NGBoost) model accurately predicts carbonation depth (CD) in alkali-activated slag concrete (AASC).•Hyperparameter optimization using three novel bio-inspired algorithms, significantly improves NGBoost model performance.•The optimized COA-NGBoost model demonstrates exceptional accuracy (R2 = 0.946) and low error metrics.•SHAP analysis reveals CO2 concentration and exposure time as primary drivers of AASC carbonation.•A GUI enables easy prediction of AASC-CD progression, facilitating practical implementation in infrastructure management. |
| ArticleNumber | 144257 |
| Author | Fu, Qianwang Zheng, Wenbo Chen, Yun |
| Author_xml | – sequence: 1 givenname: Yun surname: Chen fullname: Chen, Yun organization: School of Civil Engineering and Architecture, Hainan University, Haikou, Hainan 570228, China – sequence: 2 givenname: Qianwang orcidid: 0000-0002-1124-4735 surname: Fu fullname: Fu, Qianwang email: fqw324@hainanu.edu.cn organization: School of Civil Engineering and Architecture, Hainan University, Haikou, Hainan 570228, China – sequence: 3 givenname: Wenbo surname: Zheng fullname: Zheng, Wenbo organization: School of Civil Engineering and Architecture, Hainan University, Haikou, Hainan 570228, China |
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| Keywords | Beluga Whale Optimization algorithm Natural gradient boosting Alkali-activated slag concrete Remora Optimization Algorithm Carbonation depth Crayfish Optimization Algorithm |
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| SubjectTerms | Alkali-activated slag concrete Beluga Whale Optimization algorithm Carbonation depth Crayfish Optimization Algorithm Natural gradient boosting Remora Optimization Algorithm |
| Title | NGBoost-based prediction of carbonation in Alkali-activated slag concrete enhanced by metaheuristic algorithms |
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