Steel railway bridge fatigue damage detection using numerical models and machine learning: Mitigating influence of modeling uncertainty

Stringer-to-floor beam connections were reported as one of the most fatigue-prone details in riveted steel railway bridges. To detect stiffness degradation that results from the initiation and growth of fatigue cracks, an automated damage detection framework was proposed by the authors (Eftekhar Azam et al., 2019; Rageh et al., 2018). The proposed method relies on Proper Orthogonal Decomposition (POD) and Artificial Neural Networks (ANNs) to...