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<p>The growing demand for sustainable and resilient infrastructure has increased interest in hemp–lime concrete as a low-carbon, bio-based construction material. However, its inherent heterogeneity makes accurate prediction of mechanical performance, particularly compressive strength, challenging. This study applies machine learning techniques to predict the 28-day compressive strength of hemp–lime concrete using Neural Networks and Random Forest models. A comprehensive dataset combining experimental and literature data is developed, incorporating formulation parameters, hemp properties, and specimen geometry. Model performance is evaluated using k-fold cross-validation. The Random Forest model achieves superior accuracy with R<sup>2</sup> ≈ 0.90, while feature importance analysis identifies the key parameters influencing compressive strength. The proposed data-driven framework supports reliable performance prediction and mix design optimization for sustainable infrastructure applications.</p>

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Machine learning prediction of compressive strength of hemp lime concrete for sustainable and resilient infrastructure

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月5日

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  • Nisrine Berdai,
  • Youssef Oukhouya Ali,
  • Jamila Elhaini,
  • Hassane Moustachbir

摘要

The growing demand for sustainable and resilient infrastructure has increased interest in hemp–lime concrete as a low-carbon, bio-based construction material. However, its inherent heterogeneity makes accurate prediction of mechanical performance, particularly compressive strength, challenging. This study applies machine learning techniques to predict the 28-day compressive strength of hemp–lime concrete using Neural Networks and Random Forest models. A comprehensive dataset combining experimental and literature data is developed, incorporating formulation parameters, hemp properties, and specimen geometry. Model performance is evaluated using k-fold cross-validation. The Random Forest model achieves superior accuracy with R2 ≈ 0.90, while feature importance analysis identifies the key parameters influencing compressive strength. The proposed data-driven framework supports reliable performance prediction and mix design optimization for sustainable infrastructure applications.