Abstract

In the operation of ships, assessing seakeeping performance is crucial. Historically, this has been done through experimentation in towing tank basins or numerical computations. However, with the rise of Artificial Intelligence (AI) and increased computational resources, there are many opportunities to use AI in predicting seakeeping performance. This research will utilize a pre-trained Artificial Neural Network (ANN) to evaluate the behaviour of fishing vessels in various operational scenarios. One of the key advantages of using these algorithms is the ability to predict a large number of scenarios quickly, compared to traditional methods. By analysing millions of variations in the principal dimensions of a fishing ship and different sea states, the study aims to identify the optimal seakeeping performance in challenging conditions, ultimately improving ship safety by examining principal form coefficients and dimensions. The research will also determine significant conclusions.