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<p>The growing worldwide population has increased the use of electric arc furnaces (EAF), resulting in a surge of EAF slag and a huge environmental concern. EAF slag’s complex physical qualities have a considerable impact on concrete’s mechanical performance, mainly its compressive strength (CS). This study introduces a novel framework for forecasting the CS of EAF slag concrete that uses advanced machine learning models such as Extreme Gradient Boosting (XGB), AdaBoost (ADB), Random Forest (RF), Hybrid XGB-RF, and Hybrid XGB-ADB. A full dataset of 730 samples was meticulously created, containing essential input parameters such as binders, aggregates, and other necessary variables, with CS as the desired outcome. Based on the findings, the XGB model showed highest accuracy, with an test R<sup>2</sup> of 0.951, MAPE of 1.128, and RMSE of 1.393&#xa0;MPa, indicating its potential for dependable performance forecasting. In addition, the hybrid XGB–RF model also demonstrated strong predictive accuracy, achieving an R<sup>2</sup> value of 0.947 during the testing phase. Furthermore, explainability tools such as SHapley Additive ExPlanations (SHAP) and partial dependence curves (PDCs) identified the curing period and content of cement as the most influential factors to predict the CS of EAF slag concrete. The methodologies and outcomes of this study will help to reduce reliance on resource-intensive experimental methods, pave the way for efficient, precise, and ecologically conscientious concrete design.</p>

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Hybrid Machine learning-based modeling to predict and optimize the compressive strength of electric arc furnace slag-modified concrete

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Md. Alhaz Uddin,
  • Md. Habibur Rahman Sobuz,
  • Md. Kawsarul Islam Kabbo,
  • Ibrahim Y. Hakeem,
  • Ratan Lal,
  • Ali Masria,
  • Sani Aliyu Abubakar

摘要

The growing worldwide population has increased the use of electric arc furnaces (EAF), resulting in a surge of EAF slag and a huge environmental concern. EAF slag’s complex physical qualities have a considerable impact on concrete’s mechanical performance, mainly its compressive strength (CS). This study introduces a novel framework for forecasting the CS of EAF slag concrete that uses advanced machine learning models such as Extreme Gradient Boosting (XGB), AdaBoost (ADB), Random Forest (RF), Hybrid XGB-RF, and Hybrid XGB-ADB. A full dataset of 730 samples was meticulously created, containing essential input parameters such as binders, aggregates, and other necessary variables, with CS as the desired outcome. Based on the findings, the XGB model showed highest accuracy, with an test R2 of 0.951, MAPE of 1.128, and RMSE of 1.393 MPa, indicating its potential for dependable performance forecasting. In addition, the hybrid XGB–RF model also demonstrated strong predictive accuracy, achieving an R2 value of 0.947 during the testing phase. Furthermore, explainability tools such as SHapley Additive ExPlanations (SHAP) and partial dependence curves (PDCs) identified the curing period and content of cement as the most influential factors to predict the CS of EAF slag concrete. The methodologies and outcomes of this study will help to reduce reliance on resource-intensive experimental methods, pave the way for efficient, precise, and ecologically conscientious concrete design.