Application of a data-driven approach for maximum fatigue damage prediction of an unbonded flexible riser

The fatigue damage prediction is a challenging issue in the design and maintenance phases of unbonded flexible pipes. This arises from the necessity of conducting numerous numerical analyses, encompassing both global performance and local structural assessments, to determine the long-term fatigue damage. To handle the computational-demanding problem for fatigue damage estimation, this work applies an efficient machine learning approach known as the AK-MDAmax approach for maximum fatigue damage estimation of unbonded flexible risers. Kriging surrogate models are employed to predict the short-term distribution of fatigue damage over the sea states. The maximum long-term fatigue damage of the flexible risers is then estimated using the AK-MDAmax approach. The verification process of the AK-MDAmax model unfolds incrementally, testing its feasibility and stability across four different scenarios with varying numbers of considered locations, ranging from 4 to 512. Comparative analyses against numerical results reveal that the AK-MDAmax approach efficiently and accurately estimates maximum long-term fatigue damage across different locations. Remarkably, a 25-fold increase in efficiency is achieved with an error margin of less than 1%. This study demonstrates the substantial potential of the proposed machine learning methodology, offering designers an efficient tool for optimizing flexible risers and mitigating computational costs and time constraints.