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<p>In the context of sustainable construction, this study quantifies and predicts the compressive strength of cement mortar incorporating zirconia (ZrO₂) as a partial cement replacement (1–5% by mass) and rice husk ash (RHA) as a partial sand replacement (10–50% by mass). Mortars were proportioned at 1:3 (binder:sand) with a constant w/c = 0.50; 70.6&#xa0;mm cubes were cast and tested at 3, 7, 14, 28, and 56&#xa0;days following IS 516:2018. The best-performing mix within the investigated range—3% ZrO₂ and 10% RHA—exhibited a 33.8% increase in 28-day compressive strength relative to the control, attributed to zirconia’s micro-filling/nucleation effects and the pozzolanic reactivity of RHA. To predict strength from mix proportions and curing age, four machine-learning models (CatBoost, XGBoost, Random Forest, LightGBM) were trained using tenfold cross-validation on 130 experimental observations and evaluated on a held-out test set. CatBoost and XGBoost achieved Test R<sup>2</sup> = 0.998 (RMSE ≈ 0.30&#xa0;MPa), Random Forest Test R<sup>2</sup> = 0.990 (RMSE ≈ 0.75&#xa0;MPa), and LightGBM Test R<sup>2</sup> = 0.975 (RMSE ≈ 1.15&#xa0;MPa). Feature-importance analyses consistently identified curing age as the most influential variable in strength development. The results demonstrate a reproducible laboratory–data workflow for evaluating and predicting the strength of ZrO₂–RHA mortars without claiming mix-design optimization beyond the tested ranges.</p>

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Compressive strength evaluation and predictive modeling of mortar incorporating zirconia and rice husk ash using ensemble machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月28日

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  • Ankit Ankit,
  • Aditya Kumar Tiwary,
  • Abhishek Sharma,
  • Amenjor Senagah

摘要

In the context of sustainable construction, this study quantifies and predicts the compressive strength of cement mortar incorporating zirconia (ZrO₂) as a partial cement replacement (1–5% by mass) and rice husk ash (RHA) as a partial sand replacement (10–50% by mass). Mortars were proportioned at 1:3 (binder:sand) with a constant w/c = 0.50; 70.6 mm cubes were cast and tested at 3, 7, 14, 28, and 56 days following IS 516:2018. The best-performing mix within the investigated range—3% ZrO₂ and 10% RHA—exhibited a 33.8% increase in 28-day compressive strength relative to the control, attributed to zirconia’s micro-filling/nucleation effects and the pozzolanic reactivity of RHA. To predict strength from mix proportions and curing age, four machine-learning models (CatBoost, XGBoost, Random Forest, LightGBM) were trained using tenfold cross-validation on 130 experimental observations and evaluated on a held-out test set. CatBoost and XGBoost achieved Test R2 = 0.998 (RMSE ≈ 0.30 MPa), Random Forest Test R2 = 0.990 (RMSE ≈ 0.75 MPa), and LightGBM Test R2 = 0.975 (RMSE ≈ 1.15 MPa). Feature-importance analyses consistently identified curing age as the most influential variable in strength development. The results demonstrate a reproducible laboratory–data workflow for evaluating and predicting the strength of ZrO₂–RHA mortars without claiming mix-design optimization beyond the tested ranges.