Mitigation of Ice-Induced Vibration of Offshore Platform Based on Gated Recurrent Neural Network

Abstract

Ice-induced vibration is one of the major risks that face the offshore platform located in cold regions. In this paper, the gated recurrent neural network (GRNN) is utilized to predict and suppress the response of offshore platforms subjected to ice load. First, a simplified model of the offshore platform is derived and validated based on the finite element model (FEM). The time history of the floating ice load is generated using the harmonic superposition method. Gated Recurrent Unit Network (GRU) and the Long-Short-Term Memory Network (LSTM) are composed in MATLAB to predict the behavior of the off-shore platform. Afterward, the linear quadratic regulator (LQR) control algorithm is used to calculate the controlling force for the training of the GRU/LSTM-based prediction controller. Numerical results show that the ice-induced vibration response prediction method based on GRU network design can predict the structural response with satisfying accuracy, and the ice-induced vibration response control method based on the LSTM network and GRU network design can learn the LQR method well and achieve good control effect. Time lag and other problems that the vibration control programs often encountered were solved well.