Fatigue at welded tubular joints often limits the service life of offshore steel jacket structures. However, detailed fracture mechanics analyses of mixed-mode fatigue crack propagation remain computationally demanding for routine design and maintenance. This study develops a multi-fidelity finite element (FE) model of a steel jacket and validates crack propagation modeling in Abaqus and Franc3D against a full-scale fatigue test on a K-joint. The corresponding fatigue life was within 8% of the experimental results, establishing a validated numerical environment for computing mixed-mode stress intensity factors (SIFs) K I and K II . In this environment, mixed-mode SIFs along non-planar crack fronts were computed for a range of crack configurations and loading conditions and used to train eight machine learning (ML) surrogates. Among these, a deep neural network (DNN) achieved the highest accuracy, with a mean absolute error of 3.2 MPa·mm 1/2 . After identifying the DNN as the best-performing model, it was applied in an incremental crack growth procedure to two cases, where it predicted crack propagation and fatigue life with less than 7% deviation from simulation results. This combined multi-fidelity and ML-based approach reduces computational time from 4 h to 3 s per analysis and supports efficient and scalable fatigue assessment of offshore structures. • An ML-based framework predicts mixed-mode fatigue crack growth in steel jackets. • Full-scale K-joint tests validate the FE modelling with fatigue life error within 8%. • DNN surrogate outperforms tree ensembles, SVR, and kNN in SIFs prediction accuracy. • DNN predicts mixed-mode SIFs with MAE of 3.2 MPa mm 1 ᐟ 2 and fatigue life within 6%. • DNN reduces fatigue evaluation time from 4 h to a few seconds per case.
Al-Hagri et al. (Thu,) studied this question.