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<p>Pine cone trash is extremely combustible, increasing forest fire risk in pine-rich areas and requiring innovative, sustainable management. This study evaluates replacing Pine Cone Scale (PCS) waste in lightweight concrete with natural coarse aggregate. The study employs a hybrid framework that utilizes machine learning (ML) techniques such as ANN, Random Forest, XGBoost, and the Stacking ensemble in conjunction with Response Surface Methodology. This method is used to model and improve the compressive strength, slump, and hardened density of the concrete. Bootstrapped datasets help ML models overcome sample limitations. Use a central composite design (CCD) to make twenty experimental concrete mixes that are based on PCS. XGBoost and stacking models were more accurate, with R² values up to 0.9999 and mean relative errors less than 0.1%. RSM performed rather well, with R² values over 0.95. The optimized PCS-I and PCS-II concrete mixes meet ASTM C 330 and IS 456:2000 standards, respectively. Their decreased density and compressive strength of more than 17&#xa0;MPa indicated that PCS may be a lightweight aggregate. This work shows the synergistic possibilities of RSM-ML integration for sustainable mix design and suggests a dual-benefit approach to turn waste biomass into an eco-efficient building material.</p>

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Machine learning and RSM-CCD analysis of sustainable concrete using pine cone waste from pine tree: towards performance and optimization

  • 期刊论文

  • 发表日期：

    2025年8月13日

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  • Anand Singh,
  • Bikarama Prasad Yadav,
  • Mayank Saklani

摘要

Pine cone trash is extremely combustible, increasing forest fire risk in pine-rich areas and requiring innovative, sustainable management. This study evaluates replacing Pine Cone Scale (PCS) waste in lightweight concrete with natural coarse aggregate. The study employs a hybrid framework that utilizes machine learning (ML) techniques such as ANN, Random Forest, XGBoost, and the Stacking ensemble in conjunction with Response Surface Methodology. This method is used to model and improve the compressive strength, slump, and hardened density of the concrete. Bootstrapped datasets help ML models overcome sample limitations. Use a central composite design (CCD) to make twenty experimental concrete mixes that are based on PCS. XGBoost and stacking models were more accurate, with R² values up to 0.9999 and mean relative errors less than 0.1%. RSM performed rather well, with R² values over 0.95. The optimized PCS-I and PCS-II concrete mixes meet ASTM C 330 and IS 456:2000 standards, respectively. Their decreased density and compressive strength of more than 17 MPa indicated that PCS may be a lightweight aggregate. This work shows the synergistic possibilities of RSM-ML integration for sustainable mix design and suggests a dual-benefit approach to turn waste biomass into an eco-efficient building material.