A new approach to predict dynamic mooring tension using LSTM neural network based on responses of floating structure.

The prediction of dynamic mooring line tension using the responses of floating structures is of great significance for the safety monitoring of the station-keeping. Due to the mooring line tension usually has the typical low-frequency and wave-frequency characteristics, this paper proposes a method of Low-frequency adds wave-frequency responses (LAWR) to predict the mooring line tension. Firstly, the optimal Long-short term memory (LSTM) neural network structure is determined through conducting the parameter sensitivity analysis on the performance of the LSTM model. Secondly, the low-frequency and wave-frequency tension are predicted using the corresponding low-frequency and wave-frequency responses as the input data, respectively. Then, the total mooring line tension is predicted by superposing the low-frequency tension and wave-frequency tension. Finally, the feasibility of the LAWR method is verified by considering the different data relevance between the training sets and validating sets, including the sea state condition contains different current direction, wave height and spectral peak frequency. The method proposed in this paper could further improve the prediction accuracy of mooring line tension more than 30%, which has great engineering significance. • A method of Low-frequency adds wave-frequency responses (LAWR) to predict the mooring line tension. • The proposed LAWR method could improve about 30% prediction accuracy. • The influence factors are analyzed to obtain the optimal neural network structure. [ABSTRACT FROM AUTHOR]