Applications of machine learning in motion prediction of marine structures: review and outlook

Abstract:

To study the development status and the key problems that need to be solved urgently of machine learning in motion prediction of marine structures, this paper comprehensively discusses the research on motion prediction of marine structures in the field of marine engineering in the past ten years. With the increasing demand for motion prediction of marine structures, the traditional prediction methods based on fluid mechanics theory cannot meet the practical application requirements in terms of both prediction accuracy and real-time performance. The emergence of machine learning methods makes it possible to accurately predict the future motion response and realize advanced control of structures according to the response. Based on the modeling principles of forecasting methods, they are classified into four categories: statistical regression methods, general neural network methods, intelligent neural network methods and hybrid forecasting methods, and the four categories of methods are comprehensively reviewed, analyzed and synthesized. Finally, the existing shortcomings and problems are analyzed, and the future development directions are given from the aspects of prediction method, framework and data set, which can provide reference for the development of motion prediction of marine structures such as ships and offshore platforms. The research shows that the research of machine learning in the field of marine structure motion prediction is still in the initial stage, and there are still many technical problems to be solved. However, with the vigorous development of AI large model and the deepening of machine learning research by researchers in this field, it can provide a solid foundation for the development of characteristic prediction methods in this field.