Development of regression models for estimating main particulars of RoPax vessels in the conceptual design stage

•Analysis of a modern database of RoPax vessels.•Implementation of simple and advanced regression models.•Comparison between literature and new regressions.•Testing of multiple regression models for main particulars estimation. The design of new ships is a process that requires knowledge of several...

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Vydáno v:Ocean engineering Ročník 333; s. 121407
Hlavní autoři: Mauro, Francesco, Salem, Ahmed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 30.07.2025
Témata:
Forest tree algorithms
Multiple linear regression
Regression models
RoPax vessels
Ship design
Ship design
RoPax vessels
Regression models
Multiple linear regression
Forest tree algorithms
ISSN:0029-8018
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Shrnutí:•Analysis of a modern database of RoPax vessels.•Implementation of simple and advanced regression models.•Comparison between literature and new regressions.•Testing of multiple regression models for main particulars estimation. The design of new ships is a process that requires knowledge of several aspects of naval architecture and marine engineering. During the early design stage, one of the first issues that designers should face is the preliminary estimation of the vessel’s main dimensions, respecting the desiderata of the ship owner. Therefore, it is relevant to provide designers with suitable tools that may help estimate the principal dimensions, consider conventional methods and investigate the applicability of modern techniques based on machine learning. The present work focuses on applying different regression techniques to a database of RoPax vessels, finding mathematical instruments to evaluate the ship’s main dimensions. Conventional regression techniques are first investigated here to compare with the existing formulae provided by other databases. The study is then extended by applying multiple linear regression and forest tree algorithms, seeking an improvement of conventional formulations available in the literature. The results highlight how the most modern regression techniques allow for better coverage of the design space, allowing the use of more than one input to obtain the final dimensions.
ISSN:0029-8018
DOI:10.1016/j.oceaneng.2025.121407