Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms
Chloride penetration and carbonation resistance are critical durability attributes that assess concrete's ability to withstand challenging environmental conditions. However, determining these parameters requires time-consuming and resource-intensive physical experiments. Accordingly, this study...
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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/S2214509525000087 |
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| author | Bo Fu Hua Lei Irfan Ullah Mohammed El-Meligy Khalil El Hindi Muhammad Faisal Javed Furqan Ahmad |
| author_facet | Bo Fu Hua Lei Irfan Ullah Mohammed El-Meligy Khalil El Hindi Muhammad Faisal Javed Furqan Ahmad |
| author_sort | Bo Fu |
| collection | DOAJ |
| description | Chloride penetration and carbonation resistance are critical durability attributes that assess concrete's ability to withstand challenging environmental conditions. However, determining these parameters requires time-consuming and resource-intensive physical experiments. Accordingly, this study employed gene expression programming (GEP) and multi-expression programming (MEP) to develop a robust model for predicting these parameters, providing mathematical equations for their estimation. Additionally, the study to develop a graphical user interface that would allow for predictions based solely on input values, thereby eliminating the need for extensive physical testing. To thoroughly assess the effectiveness of the proposed GEP and MEP models, a range of statistical metrics were employed, including the coefficient of determination (R²), adjusted R², root mean square error (RMSE), mean absolute error (MAE), and root mean square error to observation’s standard deviation ratio (RSR), along with engineering indices like the a10-index and a20-index. Both GEP and MEP models consistently demonstrated outstanding performance across all statistical indicators for both carbonation rate and chloride penetration. The GEP model showed high precision in modeling chloride penetration with an R² of 0.954, MAE of 0.252, and RMSE of 1.050, and for carbonation rate with an R² of 0.99, MAE of 0.230, and RMSE of 1.100. Similarly, the MEP model performed well, achieving an R² of 0.913, MAE of 0.489, and RMSE of 1.434 for chloride penetration, and an R² of 0.985, MAE of 0.560, and RMSE of 1.440 for carbonation rate. In addition, the SHapley Additive exPlanation (SHAP) method was employed to comprehend the model estimations. In predicting chloride penetration, cement to water ratio (C/B) emerged as the most impactful feature, followed by silica fume to binder ratio (SF/B) and water to binder ratio (W/B) in terms of importance. For carbonation rate, W/B stood out as the most influential, with C/B and fly ash to binder ratio (FA/B) being the subsequent key factors. These intuitions are further supported by partial dependence plots (PDPs). Furthermore, the SHAP summary plots distinctly reveal the relationships between the various parameters and the estimated characteristics. |
| format | Article |
| id | doaj-art-a32d71e404fc44c5b9e2ab5e2980dec7 |
| institution | Kabale University |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
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| series | Case Studies in Construction Materials |
| spelling | doaj-art-a32d71e404fc44c5b9e2ab5e2980dec72025-01-11T06:41:25ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e04209Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithmsBo Fu0Hua Lei1Irfan Ullah2Mohammed El-Meligy3Khalil El Hindi4Muhammad Faisal Javed5Furqan Ahmad6Collage of Civil Engineering, North Minzu University, Yinchuan 750021, China; Corresponding authors.Collage of Civil Engineering, North Minzu University, Yinchuan 750021, ChinaDepartment of Civil and Transportation Engineering, Hohai University, Nanjing, ChinaApplied Science Research Center, Applied Science Private University, Amman, Jordan; Jadara University Research Center, Jadara University, PO Box 733, Irbid, JordanDepartment of Computer Science, College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaDepartment of Civil Engineering, GIK Institute of Engineering Sciences and Technology, Swabi 23640, Pakistan; Western Caspian University, Baku, AzerbaijanUNHCR, Afghanistan; Corresponding authors.Chloride penetration and carbonation resistance are critical durability attributes that assess concrete's ability to withstand challenging environmental conditions. However, determining these parameters requires time-consuming and resource-intensive physical experiments. Accordingly, this study employed gene expression programming (GEP) and multi-expression programming (MEP) to develop a robust model for predicting these parameters, providing mathematical equations for their estimation. Additionally, the study to develop a graphical user interface that would allow for predictions based solely on input values, thereby eliminating the need for extensive physical testing. To thoroughly assess the effectiveness of the proposed GEP and MEP models, a range of statistical metrics were employed, including the coefficient of determination (R²), adjusted R², root mean square error (RMSE), mean absolute error (MAE), and root mean square error to observation’s standard deviation ratio (RSR), along with engineering indices like the a10-index and a20-index. Both GEP and MEP models consistently demonstrated outstanding performance across all statistical indicators for both carbonation rate and chloride penetration. The GEP model showed high precision in modeling chloride penetration with an R² of 0.954, MAE of 0.252, and RMSE of 1.050, and for carbonation rate with an R² of 0.99, MAE of 0.230, and RMSE of 1.100. Similarly, the MEP model performed well, achieving an R² of 0.913, MAE of 0.489, and RMSE of 1.434 for chloride penetration, and an R² of 0.985, MAE of 0.560, and RMSE of 1.440 for carbonation rate. In addition, the SHapley Additive exPlanation (SHAP) method was employed to comprehend the model estimations. In predicting chloride penetration, cement to water ratio (C/B) emerged as the most impactful feature, followed by silica fume to binder ratio (SF/B) and water to binder ratio (W/B) in terms of importance. For carbonation rate, W/B stood out as the most influential, with C/B and fly ash to binder ratio (FA/B) being the subsequent key factors. These intuitions are further supported by partial dependence plots (PDPs). Furthermore, the SHAP summary plots distinctly reveal the relationships between the various parameters and the estimated characteristics.http://www.sciencedirect.com/science/article/pii/S2214509525000087Blended cement concreteMachine learningGene expression programmingMultiple expression programming |
| spellingShingle | Bo Fu Hua Lei Irfan Ullah Mohammed El-Meligy Khalil El Hindi Muhammad Faisal Javed Furqan Ahmad Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms Case Studies in Construction Materials Blended cement concrete Machine learning Gene expression programming Multiple expression programming |
| title | Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms |
| title_full | Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms |
| title_fullStr | Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms |
| title_full_unstemmed | Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms |
| title_short | Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms |
| title_sort | predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms |
| topic | Blended cement concrete Machine learning Gene expression programming Multiple expression programming |
| url | http://www.sciencedirect.com/science/article/pii/S2214509525000087 |
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