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<p>The global shortage of natural aggregates and the rise of manufactured sand (M-sand) necessitate innovative approaches for concrete mix proportioning. This study proposes an integrated, data-driven decision-support framework combining XGBoost-based performance prediction, SHAP-based interpretability, quantile gradient boosting for uncertainty quantification, and NSGA-II multi-objective optimization. Based on 1200 original laboratory datasets (C30-C45 grade, northern China, limestone stone powder) validated against 6350 m<sup>3</sup> of field placement, the XGBoost model achieves test-set R<sup>2</sup>=0.989 and RMSE = 0.789&#xa0;MPa for 28-day compressive strength—a 39.2% improvement over a W/B linear regression baseline and 90.1% over the JGJ 55-2011 Abrams formula. SHAP analysis identifies water-binder ratio as the dominant predictor (80.7% mean absolute contribution), with stone powder content exhibiting a mechanistically explained three-stage response. Quantile gradient boosting provides 90% prediction intervals; empirical coverage falls below the nominal target (PICP = 68.8% for 28-day strength), reported honestly as a current limitation. NSGA-II Pareto optimization identifies solutions reducing carbon emissions by 11.6% and material cost by 3.2% while exceeding C40 requirements by ≥ 17%. Field validation confirmed mean prediction error below 5%, with a lab-to-field transfer function (R<sup>2</sup>=0.94). This framework provides a replicable methodological template; direct model transfer requires domain-specific retraining.</p>

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Interpretable XGBoost framework for multi-objective manufactured sand concrete mix design

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月7日

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  • Lihao Zhang,
  • Tie Li

摘要

The global shortage of natural aggregates and the rise of manufactured sand (M-sand) necessitate innovative approaches for concrete mix proportioning. This study proposes an integrated, data-driven decision-support framework combining XGBoost-based performance prediction, SHAP-based interpretability, quantile gradient boosting for uncertainty quantification, and NSGA-II multi-objective optimization. Based on 1200 original laboratory datasets (C30-C45 grade, northern China, limestone stone powder) validated against 6350 m3 of field placement, the XGBoost model achieves test-set R2=0.989 and RMSE = 0.789 MPa for 28-day compressive strength—a 39.2% improvement over a W/B linear regression baseline and 90.1% over the JGJ 55-2011 Abrams formula. SHAP analysis identifies water-binder ratio as the dominant predictor (80.7% mean absolute contribution), with stone powder content exhibiting a mechanistically explained three-stage response. Quantile gradient boosting provides 90% prediction intervals; empirical coverage falls below the nominal target (PICP = 68.8% for 28-day strength), reported honestly as a current limitation. NSGA-II Pareto optimization identifies solutions reducing carbon emissions by 11.6% and material cost by 3.2% while exceeding C40 requirements by ≥ 17%. Field validation confirmed mean prediction error below 5%, with a lab-to-field transfer function (R2=0.94). This framework provides a replicable methodological template; direct model transfer requires domain-specific retraining.