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<p>Concrete production accounts for a significant share of global CO<sub>2</sub> emissions, underscoring the need for sustainable supplementary cementitious materials. This study evaluates a ternary cementitious system incorporating extracted micro-silica (EMS) and rice husk ash (RHA) as partial cement replacements to enhance compressive strength and reduce cement dependency. An experimental program was conducted on mixtures with varying EMS and RHA dosages, followed by predictive modelling and optimization using Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). Optimal mixtures containing 10–15% EMS and 15–25% RHA achieved higher compressive strength than the control mix, whereas higher replacement levels reduced strength due to particle agglomeration and weak hydration products. SEM analysis confirmed the improved microstructure in the optimized mixture, characterized by refined C-S-H networks and reduced porosity. The RSM model achieved strong predictive accuracy (R² = 0.95, RMSE = 2.7&#xa0;MPa), while the ANN model achieved R² = 0.98 and RMSE = 1.9&#xa0;MPa. These findings provide valuable insights for designing high-performance, sustainable concrete that relies less on traditional cementitious materials. Future work should focus on evaluating the long-term durability and environmental impact of the optimized mixtures in real-world applications.</p>

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Modeling and optimization of sustainable ternary concrete incorporating rice husk ash and extracted micro silica

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月12日

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  • Muhammad Fahad Ullah,
  • Hesheng Tang,
  • Asad Ullah,
  • Shoaib Ahmad,
  • Abdullah Alzlfawi,
  • Mahmood Ahmad,
  • Zsolt Tóth

摘要

Concrete production accounts for a significant share of global CO2 emissions, underscoring the need for sustainable supplementary cementitious materials. This study evaluates a ternary cementitious system incorporating extracted micro-silica (EMS) and rice husk ash (RHA) as partial cement replacements to enhance compressive strength and reduce cement dependency. An experimental program was conducted on mixtures with varying EMS and RHA dosages, followed by predictive modelling and optimization using Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). Optimal mixtures containing 10–15% EMS and 15–25% RHA achieved higher compressive strength than the control mix, whereas higher replacement levels reduced strength due to particle agglomeration and weak hydration products. SEM analysis confirmed the improved microstructure in the optimized mixture, characterized by refined C-S-H networks and reduced porosity. The RSM model achieved strong predictive accuracy (R² = 0.95, RMSE = 2.7 MPa), while the ANN model achieved R² = 0.98 and RMSE = 1.9 MPa. These findings provide valuable insights for designing high-performance, sustainable concrete that relies less on traditional cementitious materials. Future work should focus on evaluating the long-term durability and environmental impact of the optimized mixtures in real-world applications.