Heave Motion Prediction of Rectangular Floating Barge Using Artificial Neural Network

Abstract

Motion response prediction at the design stage of a vessel can ameliorate the performance of any floating structure. Many naval operations and offshore activities such as oil and gas exploration, aircraft landing, mooring, berthing, etc. are motion-sensitive. Hence, it is apparent, that motion response plays a vital role in these cases and, to keep it to a minimum while designing a vessel, motion prediction is essential. Traditional ways of predicting motion response require a wide range of parameters, which may not be available at the early stage of the design. Besides a significant amount of computation time and human efforts are also necessary. Artificial Intelligence can be beneficial to overcome the aforesaid issues. In this research, the architecture of the neural network model has been explored. A hybrid model is developed using Artificial Neural Network and Lewis Form method along with the numerical solution. The principal particulars of vessels and heave motion responses have been fed to the model to learn the behavior of the vessels with respect to time in presence of excitation force. Based on 15 to 30 seconds of simulation, the trained model can predict the heave motion of a vessel efficiently.