A data-driven model for predicting fatigue performance of high-strength steel wires based on optimized XGBOOST

Abstract

Fatigue damage to high-strength steel wires in cable-stayed bridges can significantly compromise bridge reliability. Previous studies have primarily focused on isolated factors such as corrosion rate or stress ratio, failing to capture the complex interactions among multiple variables. Consequently, a few researchers have developed data-driven models using machine learning methods. However, these unoptimized models exhibit limited prediction performance and convergence speed, and lack in-depth analysis of feature effects on the models. To address these shortcomings, this study combines the Gray Wolf Optimization (GWO) algorithm with the XGBoost model to create a data-driven approach for predicting the fatigue performance of high-strength steel wires under multiple influencing factors. The model is validated using both journal and experimental datasets. Results demonstrate that the proposed GWO-XGBoost model achieves high prediction accuracy (R2 = 0.94) and strong generalization ability, and exhibits rapid convergence in optimizing hyperparameters, outperforming BP neural networks and ridge regression models. Additionally, the study highlights the primary effects of wire mass loss, stress range, and average stress on fatigue life prediction, emphasizing the importance of preventing corrosion and managing stress levels to enhance wire fatigue performance. Analysis of model parameters reveals that alpha and colsample_bytree are critical for maintaining model stability and minimizing error rates, while maximum depth and learning rate significantly impact model complexity and convergence. This suggests that proper tuning of these parameters is essential for ensuring model robustness, efficiency, and generalization ability.