Bayesian and Neural Networks for Preliminary Ship Design

Abstract

To ease the determination of the main particulars of a ship at the initial design stage it is convenient to have tools which, given the type of ship and a few other parameters, output estimations of the remaining dimensions. To establish such a tool, a database of the characteristics of about 87 000 ships is acquired and various methods for derivation of empirical relations are employed. A regression analysis is carried out to fit functions to the data. Further, the data are used to learn Bayesian and neural networks to encode the relations between the characteristics. On the basis of examples, the three methods are evaluated in terms of accuracy and limitations of use. For different types of ships, the methods provide information on the relations between length, breadth, height, draft, speed, displacement, block coefficient and loading capacity. Thus, useful tools are available to the designer when he chooses the preliminary main characteristics of a ship.