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<p>The advantages of concrete-filled steel tubular (CFST) have made it a popular choice for structural applications. For structural design and safety, the precise estimation of the axial capacity of the CFST is a crucial element. However, design codes currently cover only conventional CFST parts, such as square, circular, and rectangular sections, and aren’t applicable to polygonal sections, such as hexagonal, octagonal, etc. In this study, four data-driven models were developed to predict the axial capacity of polygonal CFST columns, including an optimized multilayer perceptron (MLP) neural network, Support Vector Regressor (SVR), Random Forest Regressor (RFR), and Gradient Boosting Regressor (GBR). This study uses a dataset containing 835 experimental and numerical test samples from the literature. All samples will be analysed using machine learning models, and the best predictors will be determined. For further evaluation, ML model predictions will be compared to existing design codes (such as Eurocode 4, ANSI/AISC 360, Chinese Code GB50936, etc.) and proposed equations from the literature. The results showed that DNN was the best predictor and outstanding (which can be applied to all polygonal CFST) when compared to design codes and proposed equations from the literature. Finally, a GUI-based polygonal CFST application predictor is offered to integrate engineering applications.</p>

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Prediction of the axial capacity of various polygon-type section concrete-filled steel (CFST) columns utilising deep neural network and machine learning regressor

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月15日

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  • Yue Chen,
  • Haytham F. Isleem,
  • Bagas R. Subchan,
  • Mohammad Khishe

摘要

The advantages of concrete-filled steel tubular (CFST) have made it a popular choice for structural applications. For structural design and safety, the precise estimation of the axial capacity of the CFST is a crucial element. However, design codes currently cover only conventional CFST parts, such as square, circular, and rectangular sections, and aren’t applicable to polygonal sections, such as hexagonal, octagonal, etc. In this study, four data-driven models were developed to predict the axial capacity of polygonal CFST columns, including an optimized multilayer perceptron (MLP) neural network, Support Vector Regressor (SVR), Random Forest Regressor (RFR), and Gradient Boosting Regressor (GBR). This study uses a dataset containing 835 experimental and numerical test samples from the literature. All samples will be analysed using machine learning models, and the best predictors will be determined. For further evaluation, ML model predictions will be compared to existing design codes (such as Eurocode 4, ANSI/AISC 360, Chinese Code GB50936, etc.) and proposed equations from the literature. The results showed that DNN was the best predictor and outstanding (which can be applied to all polygonal CFST) when compared to design codes and proposed equations from the literature. Finally, a GUI-based polygonal CFST application predictor is offered to integrate engineering applications.