Shear performance prediction for corrugated steel web girders based on machine-learning algorithms

Abstract

This study aimed to predict the shear strength of corrugated steel web girders (CSWGs) by developing a new method based on four machine-learning (ML) algorithms, namely the support vector machine, artificial neural network, random forest, and XGBoost. Based on the acquired experimental and numerical data, a database containing 552 samples was constructed to train and test the ML models. A five-fold cross-validation approach was adopted during training to prevent model overfitting. A RandomizedSearchCV was used to optimize the hyperparameters of each model. The performance of the trained models was evaluated using four performance metrics, and the results revealed that the coefficients of determination (R²) of all ML models exceeded 0.97 when used on both training and validation sets, demonstrating the excellent performance of the ML models in predicting the shear strength of CSWGs. Additionally, the implemented ML models outperformed existing design codes and empirical formulae. The XGBoost model yielded the best prediction results with an R² of 0.999, mean absolute error of 44.98 kN, root-mean-square error of 66.67 kN, and mean absolute percentage error of 2.1 %. By using the Shapley additive explanation to derive a visual, quantitative explanation of the XGBoost model, the yield strength, web thickness, and web height were identified as the most critical factors affecting the shear strength of CSWGs, and their average absolute Shapley values accounted for approximately 91.45 % of the total value. The ML models implemented in this study provide a promising new approach for pre-designing and verifying the stability of CSWGs.