Fatigue failure constitutes an issue that cannot be ignored when designing welded steel structures due to the initiation of cracks at weld toes and defects under cyclic loading conditions. Traditional methods, such as the notch stress approach, estimate fatigue life by modeling local stress distributions using idealized weld geometries. While these methods are widely accepted in design codes, they can be limited by complexity and reduced accuracy in real-world applications. This study explores the use of artificial neural networks (ANNs) to enhance fatigue life prediction through data-driven modeling. The proposed method involves training an ANN using synthetic data generated through finite element simulations of S355 steel weldments under various loading histories, rates, and frequencies. The objective is to capture the influence of local geometric and stress features without relying solely on assumptions used in conventional approaches. The FEM-based training data incorporate both classical experimental findings and validated modeling practices. While performance evaluation of the ANN model is reserved for future work, this study lays the groundwork for replacing or supplementing the notch stress approach with a more adaptable and efficient predictive tool. The integration of machine learning into fatigue assessment has the potential to improve reliability, reduce computational burden, and support more informed maintenance and design decisions.
Bachhav et al. (Tue,) studied this question.