# Research on Replacing Numerical Simulation of Mooring System with Machine Learning Methods
## 1 Introduction  
A mooring system is usually used for an offshore floating structure to maintain its upright position, which is a traditional approach compared to the dynamic positioner. However, mooring system incidents have occurred at an increasing rate [1]. To ensure the operational safety of the mooring system, a lot of research focusing on its design and failure detection, using machine learning methods, has been conducted in recent years. The design of the mooring system has been hindered by the low efficiency of time-domain numerical calculation for a long time. Compared with the static or frequency-domain method, time-domain numerical simulation is regarded as the more accurate method for mooring analysis despite being more computationally demanding. With the advancement of computing technology, time-domain numerical simulation has become feasible in mooring design. Nevertheless, efficiency is still a significant limitation when it comes to the optimization design, which requires a lot of calculation cases as the basis data. As a result, static mooring analysis has been adopted and combined with the genetic algorithm to perform mooring optimization design [2]. Later, the machine learning method was introduced to make quick and accurate predictions, replacing the time-domain simulation and lifting the efficiency limitation on optimization problems. For a floating offshore platform anchored by 18 mooring lines distributed in four clusters, a hybrid method combining finite element analysis and artificial neural networks (ANN) for fatigue analysis has been proposed, to balance the accuracy and efficiency of simulating multiple sea states [3]. They also used ANN to reduce computational efforts in truncated model tests [4; 5] performed a floating wind turbine(FOWTs) aero-hydro-servo-elastic modeling to assess the effects of mooring line tension via deep learning, and the major contributing factors were identified by ANN [6; 7] assessed mooring system design optimization using the multi-objective genetic algorithm assisted by a random forest-based surrogate model. Recently, a new artificial intelligence-based method named SADA was proposed for the prediction of dynamic responses [8]. For a 4 point tanker-buoy mooring system, the simulation results of mooring tensions and tanker motion displacements by Ocraflex have been employed to train ANN structure, and an algorithm was created to obtain quick predictions [9; 10], which achieved time-domain simulation by a BP neural network using the calculation results from Moses as training samples for pipe-laying vessels. Pipe movement was significantly reduced at different wave direction angles using the genetic optimization algorithm. For a vessel-shaped offshore fishing farm, ref. [11] applied the Kriging metamodels as surrogates for calculating the time-domain responses. The optimal solutions were found by exploring the design space using a gradient-based search algorithm. During the mooring system building process, structural health monitoring could be achieved by machine learning methods to make real-time predictions based on previous field data or numerical simulation results [12; 13]. For a semisubmersible with mooring lines, an ANN model was used for the prediction of mooring line tensions and fatigue assessment [14]. The model was trained using time histories of vessel motions and the corresponding mooring line tensions for a range of sea states generated by numerical simulations. The monitored motion of the platform is used to predict the mooring line damage based on the results of the ANN model trained using the results from numerical analyses. This method was also adapted for spread-moored FSPO [15]. For the spread-moored FSPO, neural networks trained by measured motion were also used to detect mooring line failure in near real-time [16; 17]. [PERSON] et al., 2021; [PERSON] et al., 2019, on the other hand, trained the neural networks using FPSO response to a range of Metocean conditions by numerical simulations.