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<p>This research explores how machine learning (ML) models can predict the durability of geopolymer concrete (GPC) produced using construction and demolition waste (CDW) as fine aggregate and artificial lightweight coarse aggregates. Durability was assessed under harsh exposure conditions, including sulphate attack, chloride penetration, and freeze–thaw cycles. The study involved 90 experimental samples across varying CDW replacement levels. Five regression models, such as Linear Regression, Support Vector Regression, Random Forest, XGBoost, and Artificial Neural Networks, were trained to forecast strength retention based on mix composition and environmental exposure. Among them, XGBoost demonstrated the highest predictive accuracy (R<sup>2</sup> = 0.96) and the lowest error values (MAE = 1.1, RMSE = 1.6). SHAP analysis was used to explain model predictions and identify key influencing parameters, with CDW content and the activator-to-binder ratio emerging as critical factors. The findings show that moderate CDW incorporation enhances durability while reducing environmental impact, and that interpretable ML tools can assist in optimising mix designs for long-term performance in aggressive environments. It is recommended that construction professionals consider incorporating machine learning tools, particularly XGBoost, into durability planning processes. This approach can support faster mix design decisions, reduce waste, and improve the long-term performance of sustainable concrete in real-world applications.</p>

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Machine Learning Prediction Model for Durability of Geopolymer Concrete with CDW and Artificial Aggregates under Harsh Environmental Exposure

  • 期刊论文

  • 发表日期：

    2025年8月4日

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  • Atul S. Kurzekar,
  • Uday P. Waghe,
  • Prajakta Waghe

摘要

This research explores how machine learning (ML) models can predict the durability of geopolymer concrete (GPC) produced using construction and demolition waste (CDW) as fine aggregate and artificial lightweight coarse aggregates. Durability was assessed under harsh exposure conditions, including sulphate attack, chloride penetration, and freeze–thaw cycles. The study involved 90 experimental samples across varying CDW replacement levels. Five regression models, such as Linear Regression, Support Vector Regression, Random Forest, XGBoost, and Artificial Neural Networks, were trained to forecast strength retention based on mix composition and environmental exposure. Among them, XGBoost demonstrated the highest predictive accuracy (R2 = 0.96) and the lowest error values (MAE = 1.1, RMSE = 1.6). SHAP analysis was used to explain model predictions and identify key influencing parameters, with CDW content and the activator-to-binder ratio emerging as critical factors. The findings show that moderate CDW incorporation enhances durability while reducing environmental impact, and that interpretable ML tools can assist in optimising mix designs for long-term performance in aggressive environments. It is recommended that construction professionals consider incorporating machine learning tools, particularly XGBoost, into durability planning processes. This approach can support faster mix design decisions, reduce waste, and improve the long-term performance of sustainable concrete in real-world applications.