Study on Fatigue Crack Growth Prediction and Machine Learning Correction for Deepwater Risers

Under long-term marine environmental loading, deep-water risers are highly susceptible to fatigue damage, and the accumulation of local damage may lead to global structural failure. In this study, the fatigue damage mechanism and crack growth behavior of a girth-welded riser are systematically investigated. Full-scale radial fatigue test results of risers are referenced, and the experimental process is reproduced through numerical simulation. A finite element model of a girth-welded riser is established. The fatigue crack growth process is subsequently simulated, yielding the crack propagation path and crack growth rate curves. By comparison with experimental results, the characteristics of the crack growth process are analyzed, and the feasibility and accuracy of numerical simulations in predicting fatigue crack growth in riser girth welds are verified. A relatively accurate prediction model for fatigue crack growth in risers is proposed. To further improve the accuracy of crack growth prediction, a machine learning-based correction model is developed. On the basis of available in-service inspection data, a correction strategy is proposed in which the predicted crack growth process is dynamically updated with measured crack growth data. The proposed approach establishes a theoretical foundation for accurate and forward prediction of fatigue fracture damage in riser structures.