An Optimized GWO-BPNN Model for Predicting Corrosion Fatigue Performance of Stay Cables in Coastal Environments

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Corrosion and fatigue damage of high-strength steel wires in cable-stayed bridges in coastal environments can seriously affect the reliability of bridges. Previous studies have focused on isolated factors such as corrosion rates or stress ratios, failing to capture the complex interactions between multiple variables. In response to the critical need for accurate fatigue life prediction of high-strength steel wires under corrosive conditions, this study proposes an innovative prediction model that combines Grey Wolf Optimization (GWO) with a Backpropagation Neural Network (BPNN). The optimized GWO-BPNN model significantly enhances prediction accuracy, stability, generalization, and convergence speed. By leveraging GWO for efficient hyperparameter optimization, the model effectively reduces overfitting and strengthens robustness under varying conditions. The test results demonstrate the model’s high performance, achieving an R2 value of 0.95 and an RMSE of 140.45 on the test set, underscoring its predictive reliability and practical applicability. The GWO-BPNN model excels in capturing complex, non-linear dependencies within fatigue data, outperforming conventional prediction methods. Sensitivity analysis identifies stress range, average stress, and mass loss as primary determinants of fatigue life, highlighting the dominant influence of corrosion and stress factors on structural degradation. These results confirm the model’s interpretability and practical utility in pinpointing key factors that impact fatigue life. Overall, this study establishes the GWO-BPNN model as a highly accurate and adaptable tool, offering substantial support for advancing predictive maintenance strategies and enhancing material resilience in corrosive environments.