Deep gated recurrent unit networks for time-domain long-term fatigue analysis of mooring lines considering wave directionality

Abstract

Mooring lines are a crucial component of offshore mooring systems, and long-term fatigue damage assessment is a challenging task in mooring line design and structural health monitoring. Traditional methods require a large number of structural time-domain simulations. Because the sea state varies over time, the variations in wave parameters (e.g., wave heights, periods, and directions) must be considered in long-term fatigue damage assessment. Wave directions have usually been ignored in previous studies, while the wave direction sensitive analysis conducted in this study showed that the wave directions could not be neglected in the long-term fatigue damage assessment of mooring lines. Recent deep neural network (DNN) methods have shown great potential in mooring line/riser response prediction for offshore structures. However, the utilization of float responses as input data in these DNNs restricts their applicability during the structure design phase. To address this issue, a gated recurrent unit (GRU) network is proposed for mooring line tension modeling. The proposed GRU network surrogate models are capable of accurately predicting the mooring line tension using wave elevation components or float motion responses. The rain-flow counting algorithm, T-N approach, and Miner's rule were employed to estimate the structural fatigue damage. The results showed that the proposed method could replace the heavy cost numerical computation, assess the long-term fatigue damage with high accuracy and alleviate the data dependency of DNN methods.