Regression analysis for container ships in the early design stage

Abstract

The seaway trade market has expanded in the last years and container ship dimensions are constantly increasing for higher cargo capacity. In the early design stage, main dimensions are usually determined based on an existing ship database from which regression formulas are derived. In the present paper, a database of 260 non-sister container ships built from 1979 to 2022, representing 20% of the world fleet, has been considered to derive and compare different types of regressions. Simple regressions have been developed and compared with equivalent formulations available in literature, proving better approximations of the trends. The study has been further extended by multivariable regressions and forest tree algorithms, which allow the use of more than one independent variable and provide a better fitting compared to simple regressions. Forest tree regressions return the highest values of fitting coefficients, but the technique is not of easy application due to the absence of mathematical expressions. The main contribution is the updated set of simple and multivariable regression formulas which have a higher goodness of fit than previous works and can be easily employed by designers in the early design stage and in multi-attribute design procedures.