A two-stage deep learning model for fracture location and fatigue life prediction of corroded cable steel wires

Artikel i vetenskaplig tidskrift, 2026

Corrosion will accelerate the degradation of materials in cables and thereby compromise the long-term serviceability of bridges. Most existing studies consider the deepest corrosion pit as both the crack initiation point and the fracture location, and employ a single corrosion parameter, such as maximum/average corrosion depth or total corrosion area, for fatigue life prediction. However, these methods exhibit inherent limitations in capturing the complex interactions between the corrosion distribution, fatigue load, and material properties. Therefore, this study proposes a two-stage deep learning model for comprehensive fatigue performance evaluation of corroded steel wires. In the first stage, the 3D scanning technique is used to obtain surface morphological images, and the training dataset is constructed by combining the finite element simulation data. The Fracture Prediction Pix2Pix (FP-Pix2Pix) model is then developed to predict fracture locations. In the second stage, the physics-informed neural network (PINN) is adopted by integrating prior information, including corrosion, fatigue load, material properties, and fracture locations predicted in the first stage, to predict the residual fatigue life (RFL). Experimental results show that the proposed two-stage model outperforms existing models in both fracture location prediction and RFL prediction with errors less than 5 %. The proposed method provides a robust and efficient approach for evaluating the fatigue performance of corroded steel wires in bridge cables.