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<p>Accurately predicting the ultimate shear capacity of perfobond rib (PBL) connectors is of great significance for the design of steel–concrete composite structures. This study predicted the ultimate shear capacity of PBL using machine learning (ML) methods. Initially, a dataset comprising 233 sets of PBL push-out test data was established. To enhance data quality, an Isolation Forest was used to identify and eliminate outliers from the dataset. Subsequently, four ML models—XGBoost, DT, RF, and ANN—were trained on this dataset to predict the ultimate shear capacity of PBL. By comparing and analyzing the prediction results, XGBoost demonstrated the best predictive performance with an <i>R</i><sup><i>2</i></sup> value of 0.97, outperforming other models. Then, a visual analysis, including SHAP and PDP, was conducted on the XGBoost model, revealing the contribution levels of different features to the predicted values. The analysis found that the number of perforated holes (<i>n</i>) had the greatest impact. Moreover, based on the analysis of visualizations, recommended ranges of values for the input features are provided to maximize the ultimate shear capacity of the PBL connectors. In comparison with traditional formulas, the trained ML models exhibit superior accuracy. The <i>MAE</i> of XGBoost is approximately 10% of that of the traditional formulas, and its <i>RMSE</i> value is less than 20% of those.</p>

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Prediction of shear capacity for PBL connectors in concrete using visualization-based machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月25日

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  • Yuqing Hu,
  • Mengyuan Lu,
  • Jiaxing Huang,
  • Tan Wang,
  • Tingting Han,
  • Shuai Li,
  • Jingquan Wang

摘要

Accurately predicting the ultimate shear capacity of perfobond rib (PBL) connectors is of great significance for the design of steel–concrete composite structures. This study predicted the ultimate shear capacity of PBL using machine learning (ML) methods. Initially, a dataset comprising 233 sets of PBL push-out test data was established. To enhance data quality, an Isolation Forest was used to identify and eliminate outliers from the dataset. Subsequently, four ML models—XGBoost, DT, RF, and ANN—were trained on this dataset to predict the ultimate shear capacity of PBL. By comparing and analyzing the prediction results, XGBoost demonstrated the best predictive performance with an R2 value of 0.97, outperforming other models. Then, a visual analysis, including SHAP and PDP, was conducted on the XGBoost model, revealing the contribution levels of different features to the predicted values. The analysis found that the number of perforated holes (n) had the greatest impact. Moreover, based on the analysis of visualizations, recommended ranges of values for the input features are provided to maximize the ultimate shear capacity of the PBL connectors. In comparison with traditional formulas, the trained ML models exhibit superior accuracy. The MAE of XGBoost is approximately 10% of that of the traditional formulas, and its RMSE value is less than 20% of those.