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<p>This study investigates the potential of incorporating alternative waste materials—limestone (LS) powder and construction and demolition waste (CDW)—in sustainable brick production under real-time curing conditions. Cement was partially replaced with LS powder in the range of 0–20% by weight, while natural aggregates were substituted with CDW at 0–100%. Experimental evaluations were conducted to assess the mechanical properties of the fabricated bricks. The results demonstrated a notable enhancement in performance compared to conventional bricks, with a maximum compressive strength of 14.99&#xa0;N/mm<sup>2</sup> observed at 2% LS and 10% CDW replacement levels. Additionally, a significant reduction in water absorption was recorded. Machine learning models, including Random Forest and Gradient Boosting, were employed to predict mechanical behavior. The findings underscore the feasibility of producing eco-efficient bricks by valorizing industrial waste within a circular construction framework.</p>

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Evaluation of net-zero materials in mortar bricks with predictive modelling using random forest and gradient boosting techniques

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月27日

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  • Golla Uday kiran,
  • Nakkeeran Ganesan,
  • Dipankar Roy,
  • Sumant Nivarutti Shinde,
  • Mamillapalli Indumathi,
  • George Uwadiegwu Alaneme,
  • Val Hyginus Udoka Eze,
  • Kuzmin Anton

摘要

This study investigates the potential of incorporating alternative waste materials—limestone (LS) powder and construction and demolition waste (CDW)—in sustainable brick production under real-time curing conditions. Cement was partially replaced with LS powder in the range of 0–20% by weight, while natural aggregates were substituted with CDW at 0–100%. Experimental evaluations were conducted to assess the mechanical properties of the fabricated bricks. The results demonstrated a notable enhancement in performance compared to conventional bricks, with a maximum compressive strength of 14.99 N/mm2 observed at 2% LS and 10% CDW replacement levels. Additionally, a significant reduction in water absorption was recorded. Machine learning models, including Random Forest and Gradient Boosting, were employed to predict mechanical behavior. The findings underscore the feasibility of producing eco-efficient bricks by valorizing industrial waste within a circular construction framework.