Preliminary parametric estimation of steel weight for new ships

Abstract

This paper presents an agile method for accurately estimating the steel weight of a new ship in the preliminary design stage. A container ship database within a three-dimensional computer-aided design system was used to facilitate the division of a ship into several estimated structural sections, including the forward, cargo, engine room, aft, etc. This procedure is an improvement over the traditional parametric estimation process, which only estimates a rough weight for an entire ship based on specific empirical formulas and then assigns a weight to each part of the ship manually. To estimate the steel weight of a new ship, the parameters of the individual sections are referenced from a similar vessel automatically selected using the principal dimension. Next, principal component analysis is applied to define the principal parameters, i.e., the principal dimension, the feature of each section and the fitting coefficient of the principal parameters. The general equations with the highest correlative predictor variables for the individual sections are then obtained by regression. Comparing the estimation variability, the section estimation provides a more accurate steel weight and constitutes a new means of efficiently obtaining the weight distribution to support the various aspects of preliminary design, i.e., the determination of the center of gravity, the design of the ship lines and ship performance evaluation.

Using Artificial Neural Networks for Predicting Ship Fuel Consumption

“…Considering this fact, the use of derivative-based techniques, such as the Gauss-Newton algorithm and the Levenberg-Marquardt algorithm, is insufficient for providing estimations of all common parameters together. To overcome this obstacle, researchers have come up with sequential parameter estimation procedures at the cost of global optimality [124].…”

Determination of Regression Formulas for Key Design Characteristics of Container Ships at Preliminary Design Stage

“…Ekincia et al (2011) through the use of various computational intelligence techniques to determine general ship principal parameter, also including main engine power. Lin and Shaw (2016) developed a parametric method to accurately estimate the steel weight of a new ship at the preliminary design stage. Hou et al proposed an (2011) artificial neural network model for the principal dimension estimation of naval vessels.…”