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<p>The development of sustainable construction materials has led to increased interest in geopolymer concrete as an alternative to Ordinary Portland Cement (OPC). This study evaluates the mechanical, durability, and predictive modeling aspects of coconut fiber-reinforced GGBS-based geopolymer concrete (CFR-GPC). Experimental analysis was conducted for varying Na₂SiO₃/NaOH ratios (0.5, 1.0, 1.5, 2.0) and fiber contents (0.25%, 0.5%) to assess compressive and flexural strength, workability, and durability. The highest compressive strength of 33.66&#xa0;MPa and flexural strength of 7.00&#xa0;MPa were obtained for a Na₂SiO₃/NaOH ratio of 2.0 with 0.25% fiber content. Durability tests confirmed excellent resistance to acidic and sulfate-rich environments, with minimal weight loss and superior strength retention. A Random Forest Regressor machine learning model was developed to predict compressive strength, achieving high accuracy (R2 = 0.956, MSE = 0.1547). The findings highlight CFR-GPC as a viable, eco-friendly alternative to OPC-based concrete, suitable for pavements, precast elements, and marine structures. The integration of machine learning enables rapid mix optimization, reducing reliance on extensive laboratory testing. Future research should focus on long-term durability and real-world applications to establish CFR-GPC as a mainstream sustainable material.</p>

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Advanced geopolymer concrete with coconut fiber reinforcement: optimizing strength, durability, and predictive modelling for sustainable construction

  • 期刊论文

  • 发表日期：

    2025年5月21日

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  • Aditya Agrawal,
  • Narayan Malviya

摘要

The development of sustainable construction materials has led to increased interest in geopolymer concrete as an alternative to Ordinary Portland Cement (OPC). This study evaluates the mechanical, durability, and predictive modeling aspects of coconut fiber-reinforced GGBS-based geopolymer concrete (CFR-GPC). Experimental analysis was conducted for varying Na₂SiO₃/NaOH ratios (0.5, 1.0, 1.5, 2.0) and fiber contents (0.25%, 0.5%) to assess compressive and flexural strength, workability, and durability. The highest compressive strength of 33.66 MPa and flexural strength of 7.00 MPa were obtained for a Na₂SiO₃/NaOH ratio of 2.0 with 0.25% fiber content. Durability tests confirmed excellent resistance to acidic and sulfate-rich environments, with minimal weight loss and superior strength retention. A Random Forest Regressor machine learning model was developed to predict compressive strength, achieving high accuracy (R2 = 0.956, MSE = 0.1547). The findings highlight CFR-GPC as a viable, eco-friendly alternative to OPC-based concrete, suitable for pavements, precast elements, and marine structures. The integration of machine learning enables rapid mix optimization, reducing reliance on extensive laboratory testing. Future research should focus on long-term durability and real-world applications to establish CFR-GPC as a mainstream sustainable material.