Fatigue Damage Prognosis Method for Main Girders of Cable-Stayed Bridges Based on Wavelet Neural Network

At present, the research on bridge structure health monitoring mainly focuses on discovering existing structural damage and less on predicting when the damage will occur in the future. This paper proposes a fatigue damage prognosis method for the main girders of cable-stayed bridges based on wavelet neural networks (WNNs). This method integrates WNN with multi-scale finite element modeling to predict fatigue damage progression. First, the theoretical foundation and implementation algorithms of the WNN are elaborated on and applied to forecast the future load environments of cable-stayed bridges. Subsequently, multi-scale finite element models are employed to derive stress influence lines for critical fatigue-prone regions in the main girder of the cable-stayed bridge. Finally, fatigue reliability methods are utilized to predict the fatigue reliability indices, service life, and failure probabilities of critical fatigue details. The proposed prognosis method in this paper can accurately predict the future operation conditions and remaining service life of bridge structures so as to provide a more reasonable maintenance strategy for bridge structures.