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<p>Stiffened steel plate girders are very important for the safety and proper operation of modern structures and bridges. When people try to Figure out how much shear these girders can bear, they frequently make assumptions that don't always take into account how geometry, materials, and stiffeners are related in a complicated way. This research was indicated that machine learning (ML) prediction framework may make better guesses about how much shear strengthened steel plate girders can handle. We put together and changed published experimental results from 173 variables by using numerical methods from the field. As part of feature engineering, geometric and material ratios that are critical to aerodynamics were added. We trained Random Forest, XGBoost, CatBoost, AdaBoost, Decision Tree, and Multilayer Perceptron very carefully and changing their hyperparameters to see how well they worked. Both CatBoost and Random Forest did the best job of predicting, with R<sup>2</sup> scores over 0.97. SHAP analysis was used to find out that model works and what design features are most important for better&#xa0;shear strength. Engineers can utilize the framework to perform&#xa0; designs that are both safer and cheaper with peer&#xa0;because traditional code-based methods. To help with real-time prediction for design purposes, a simple graphical interface was &#xa0;built.</p>

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Prediction of the shear capacity of stiffened steel plate girders using machine learning algorithms

  • 期刊论文

  • 发表日期：

    2025年8月8日

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  • Baljeet Yadav,
  • S. S. Mishra

摘要

Stiffened steel plate girders are very important for the safety and proper operation of modern structures and bridges. When people try to Figure out how much shear these girders can bear, they frequently make assumptions that don't always take into account how geometry, materials, and stiffeners are related in a complicated way. This research was indicated that machine learning (ML) prediction framework may make better guesses about how much shear strengthened steel plate girders can handle. We put together and changed published experimental results from 173 variables by using numerical methods from the field. As part of feature engineering, geometric and material ratios that are critical to aerodynamics were added. We trained Random Forest, XGBoost, CatBoost, AdaBoost, Decision Tree, and Multilayer Perceptron very carefully and changing their hyperparameters to see how well they worked. Both CatBoost and Random Forest did the best job of predicting, with R2 scores over 0.97. SHAP analysis was used to find out that model works and what design features are most important for better shear strength. Engineers can utilize the framework to perform  designs that are both safer and cheaper with peer because traditional code-based methods. To help with real-time prediction for design purposes, a simple graphical interface was  built.