Real-time prediction of wave-induced hull girder loads for a large container ship based on the recurrent neural network model and error correction strategy

Abstract

Real-time acquisition of wave-induced hull girder loads of a sailing ship will help the captain make reasonable decisions, which is of great significance for improving the safety of the ship's navigation. This paper investigates the real-time prediction method of hull girder loads based on the Recurrent Neural Network (RNN) model and error correction strategy. Firstly, taking the vertical bending moment, horizontal bending moment, and torsional moment at the mid-ship position of a large container ship as examples, corresponding neural network prediction models are established through parameter influence analysis. Secondly, various sea state conditions are used to verify the feasibility of established network prediction models to predict the hull girder loads in real-time. The VBM prediction model performs better than the TM prediction model and HBM prediction model, and the errors of the TM prediction model and HBM prediction model are slightly larger in some cases. Lastly, an improved prediction model based on an error correction strategy is proposed to improve the prediction accuracy of the neural network prediction model, and the adequate performance of the error correction strategy is discussed.