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<p>This study presents a novel approach consisting of integrating experimental mechanics and machine learning (ML) to predict the dynamic compressive strength of plain and steel fibre reinforced concrete (SFRC) under high strain rates. It addresses key challenges of conventional Hopkinson bar experiments, including high costs, limited accessibility to specialized equipment, and difficulties in replicating extreme conditions. A comprehensive database of 157 experimental datasets was compiled to develop robust predictive models, including random forest, gradient boosting (GB), extreme gradient boosting, and categorical boosting. Among these, GB demonstrated the highest predictive accuracy, emphasizing the dominant influence of strain rate. A key contribution of this study is the development of a user-friendly graphical user interface, which transforms these ML models into a practical tool for researchers and civil engineers, enabling cost-effective and time-efficient estimation of SFRC’s compressive strength under dynamic loading. This work highlights the transformative potential of ML-driven approaches in civil engineering, offering innovative solutions to long-standing experimental challenges.</p>

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An intelligent GUI for machine learning-based prediction of SFRC compressive strength under high strain rates

  • 期刊论文

  • 发表日期：

    2025年5月8日

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  • Hadjer Belkadi,
  • Abdelkrim Bourzam,
  • Messaoud Saidani,
  • Souad Mekbel

摘要

This study presents a novel approach consisting of integrating experimental mechanics and machine learning (ML) to predict the dynamic compressive strength of plain and steel fibre reinforced concrete (SFRC) under high strain rates. It addresses key challenges of conventional Hopkinson bar experiments, including high costs, limited accessibility to specialized equipment, and difficulties in replicating extreme conditions. A comprehensive database of 157 experimental datasets was compiled to develop robust predictive models, including random forest, gradient boosting (GB), extreme gradient boosting, and categorical boosting. Among these, GB demonstrated the highest predictive accuracy, emphasizing the dominant influence of strain rate. A key contribution of this study is the development of a user-friendly graphical user interface, which transforms these ML models into a practical tool for researchers and civil engineers, enabling cost-effective and time-efficient estimation of SFRC’s compressive strength under dynamic loading. This work highlights the transformative potential of ML-driven approaches in civil engineering, offering innovative solutions to long-standing experimental challenges.