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<p>This study investigates the potential of Ground Granulated Blast Furnace Slag (GGBS), limestone powder (LSP), and ceramic waste (CW) as sustainable alternatives in mortar production. Six mix proportions were prepared by partially substituting cement and fine aggregates to evaluate dry density, water absorption, and compressive strength (CS). The results showed that the optimum performance was achieved at Bio3 (6% GGBS, 6% limestone, and 30% ceramic waste), where dry density reached 2255.36&#xa0;kg/m³, water absorption peaked at 3.47%, and CS improved to 39.82&#xa0;N/mm² compared to the control mix. Beyond this level, strength and density decreased due to excessive replacement, leading to higher porosity. To enhance predictive capability, Response Surface Methodology (RSM) was applied, yielding high accuracy with R² values of 0.9439 for density, 0.9439 for water absorption, and 0.9293 for strength. Optimization revealed the optimal mix of 1.01% GGBS and 13.14% ceramic waste, with a desirability index of 1. The integration of experimental and machine learning approaches confirms the feasibility of eco-friendly construction composites.</p>

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Performance optimization of sustainable mortar with GGBS and ceramic waste using response surface methodology

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • Sumant Nivarutti Shinde,
  • A. Sujatha,
  • Ajim Shabbir Sutar,
  • Manish Kumar Sinha,
  • Naveed Akhtar,
  • N. Lingeshwaran

摘要

This study investigates the potential of Ground Granulated Blast Furnace Slag (GGBS), limestone powder (LSP), and ceramic waste (CW) as sustainable alternatives in mortar production. Six mix proportions were prepared by partially substituting cement and fine aggregates to evaluate dry density, water absorption, and compressive strength (CS). The results showed that the optimum performance was achieved at Bio3 (6% GGBS, 6% limestone, and 30% ceramic waste), where dry density reached 2255.36 kg/m³, water absorption peaked at 3.47%, and CS improved to 39.82 N/mm² compared to the control mix. Beyond this level, strength and density decreased due to excessive replacement, leading to higher porosity. To enhance predictive capability, Response Surface Methodology (RSM) was applied, yielding high accuracy with R² values of 0.9439 for density, 0.9439 for water absorption, and 0.9293 for strength. Optimization revealed the optimal mix of 1.01% GGBS and 13.14% ceramic waste, with a desirability index of 1. The integration of experimental and machine learning approaches confirms the feasibility of eco-friendly construction composites.