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<p>Artificial Intelligence (AI) is transforming concrete research. This review explores various AI techniques that drive cutting-edge solutions across all stages of concrete lifecycle, from material, mixture, and process optimization to quality control and performance prediction. Meta-analysis shows that XGBoost model excels in predicting workability (<i>R</i><sup>2</sup> = 0.98), while ensemble models provide the best strength predictions (<i>R</i><sup>2</sup> = 0.93). The study highlights trends, gaps, and future AI opportunities in concrete technology.</p>

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Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月17日

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  • Dayou Luo,
  • Kejin Wang,
  • Dongming Wang,
  • Anuj Sharma,
  • Wengui Li,
  • In Ho Choi

摘要

Artificial Intelligence (AI) is transforming concrete research. This review explores various AI techniques that drive cutting-edge solutions across all stages of concrete lifecycle, from material, mixture, and process optimization to quality control and performance prediction. Meta-analysis shows that XGBoost model excels in predicting workability (R2 = 0.98), while ensemble models provide the best strength predictions (R2 = 0.93). The study highlights trends, gaps, and future AI opportunities in concrete technology.