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<p>High performance concrete (HPC) properties depend on both its constituent materials and their interaction. This study presents a machine learning framework to quantify the effects of constituents on HPC compressive strength. We first develop a stochastic constitutive model using experimental data and subsequently employ an uncertainty quantification method to identify key parameters in relation to the compressive strength of HPC. The resultant sensitivity indices indicate that fly ash content has the strongest influence on compressive strength, followed by concrete age at test and blast surface slag content.</p>

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A Machine Learning based uncertainty quantification for compressive strength of high-performance concrete

  • 期刊论文

  • 发表日期：

    2025年5月30日

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  • Nam Vu-Bac,
  • Tuan Le-Anh,
  • Timon Rabczuk

摘要

High performance concrete (HPC) properties depend on both its constituent materials and their interaction. This study presents a machine learning framework to quantify the effects of constituents on HPC compressive strength. We first develop a stochastic constitutive model using experimental data and subsequently employ an uncertainty quantification method to identify key parameters in relation to the compressive strength of HPC. The resultant sensitivity indices indicate that fly ash content has the strongest influence on compressive strength, followed by concrete age at test and blast surface slag content.