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Concrete Filled Steel Tube (CFST) is favorable to utilize in the construction of arch bridges. Since CFST are mostly intended to carry compression loads, the axial compressive capacity is of primary importance in the of CFST columns. Current standards limit their applicability to conventional material strength and geometric dimensions of CFST, sometimes failing to meet the requirements of modern arch bridges. This study has developed a probabilistic Machine Learning (ML) model based on the NGBoost algorithm. The results demonstrate that the NGBoost model with a LogNormal distribution provides both accurate and probabilistic predictions, surpassing the performance of the XGBoost model and the NGBoost model with a Normal distribution. Furthermore, we have employed the SHapley Additive exPlanations (SHAP) method to interpret the probabilistic model. It was revealed that the column dimensions exert the most significant influence on the axial compressive capacity of CFST.

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Probabilistic Machine Learning Prediction for Axial Compressive Capacity of CFST: Preliminary Results

  • 会议论文

  • 发表日期：

    2025年

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  • Dade Lai,
  • Cristoforo Demartino,
  • Junqing Xue,
  • Alessandro Contento,
  • Bruno Briseghella

摘要

Concrete Filled Steel Tube (CFST) is favorable to utilize in the construction of arch bridges. Since CFST are mostly intended to carry compression loads, the axial compressive capacity is of primary importance in the of CFST columns. Current standards limit their applicability to conventional material strength and geometric dimensions of CFST, sometimes failing to meet the requirements of modern arch bridges. This study has developed a probabilistic Machine Learning (ML) model based on the NGBoost algorithm. The results demonstrate that the NGBoost model with a LogNormal distribution provides both accurate and probabilistic predictions, surpassing the performance of the XGBoost model and the NGBoost model with a Normal distribution. Furthermore, we have employed the SHapley Additive exPlanations (SHAP) method to interpret the probabilistic model. It was revealed that the column dimensions exert the most significant influence on the axial compressive capacity of CFST.