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<p>The goal of the current study is to close a significant gap in the literature about the representation of concrete produced from industrial waste. In example, prior research experiences low levels of model explainability, limited generalizability, and limited data, especially because of their inability to capture nonlinear interactions among features. In order to overcome these limitations, authors apply seven supervised machine learning models to a large dataset of 711 observations. The two main new features of the suggested strategy include a thorough, systematic assessment of ensemble learners and SHAP-based model interpretability. With a maximum test accuracy of R<sup>2</sup> = 0.881, RMSE = 5.65&#xa0;MPa, MAE = 4.17&#xa0;MPa, and MAPE = 25.15%, the Gradient Boosting Regressor (GBR) outperformed other cutting-edge models such as CatBoost (R<sup>2</sup> = 0.879) and Histogram Gradient Boosting (R<sup>2</sup> = 0.878). Based on the findings from the SHAP analysis, it is clear that the machine learning model shows more sensitivity to the fine aggregate content than to cement and other materials, making it the most influential factor in the global model. Water content and curing age follow. In actual use, the suggested model and its intuitive Python-based graphical user interface (GUI) can quickly and affordably estimate compressive strength without requiring expensive and time-consuming laboratory testing, guaranteeing the successful incorporation of industrial waste products into the concrete mixture.</p>

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Optimizing the mechanical performance of sustainable industrial waste modified concrete using supervised machine learning modeling and feature importance analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月19日

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  • Mohamed AbdelMongy,
  • Md. Alhaz Uddin,
  • Md. Habibur Rahman Sobuz,
  • Samir Mazumder,
  • Mohammad Azad,
  • Md. Kawsarul Islam Kabbo,
  • Mohammed Jameel,
  • Sani Aliyu Abubakar

摘要

The goal of the current study is to close a significant gap in the literature about the representation of concrete produced from industrial waste. In example, prior research experiences low levels of model explainability, limited generalizability, and limited data, especially because of their inability to capture nonlinear interactions among features. In order to overcome these limitations, authors apply seven supervised machine learning models to a large dataset of 711 observations. The two main new features of the suggested strategy include a thorough, systematic assessment of ensemble learners and SHAP-based model interpretability. With a maximum test accuracy of R2 = 0.881, RMSE = 5.65 MPa, MAE = 4.17 MPa, and MAPE = 25.15%, the Gradient Boosting Regressor (GBR) outperformed other cutting-edge models such as CatBoost (R2 = 0.879) and Histogram Gradient Boosting (R2 = 0.878). Based on the findings from the SHAP analysis, it is clear that the machine learning model shows more sensitivity to the fine aggregate content than to cement and other materials, making it the most influential factor in the global model. Water content and curing age follow. In actual use, the suggested model and its intuitive Python-based graphical user interface (GUI) can quickly and affordably estimate compressive strength without requiring expensive and time-consuming laboratory testing, guaranteeing the successful incorporation of industrial waste products into the concrete mixture.