A structural weight estimation model of FPSO topsides using an improved genetic programming method. (2nd January 2017)

• Record Type: Journal Article
• Authors: Ha, Sol; Um, Tae-Sub; Roh, Myung-Il; Shin, Hyun-Kyoung
• Is Part Of: Ships and offshore structures. Volume 12:Number 1(2017)
• Page Start: 43
• Page End: 55
• Publication Date: 2017-01-02
• Subjects: weight estimation -- topsides weight -- FPSO plant -- genetic programming -- correlation analysis -- optimisation -- statistics; Ships -- Periodicals; Offshore structures -- Periodicals; Marine engineering -- Periodicals; Marine engineering -- Technological innovations -- Periodicals; Ocean engineering -- Periodicals; Ocean engineering -- Technological innovations -- Periodicals; 623.8
• DOI: 10.1080/17445302.2015.1099246
• Languages: English
• ISSNs: 1744-5302

Abstract

Abstract : The weight information of an FPSO (floating production, storage, and offloading) plant is one of the important data needed to estimate the amount of production material (e.g., plates) needed and to determine the suitable production method for its construction. In addition, the weight information is a key factor that affects the building cost and the production period of the FPSO plant. Although the importance of the weight has long been recognised, the weight, especially of the topside, has been roughly estimated using the existing similar data as well as the designer's experience. To improve this task, a weight estimation model for FPSO plant topsides was developed in this study using the improved genetic programming (GP) method. For this reason, various past records on the estimation of the weight of the FPSO plant were collected through a literature survey, and then the weight estimation model using GP was established by fixing the independent variables based on these data. In addition, correlation analysis was performed to make up for the weak points of genetic programing, which is apt for inducing overfitting when the number of data is relatively smaller than that of the independent variables. That is, by reducing the number of variables through the analysis of the correlation between the independent variables, an increase in the number of weight data can be expected. Finally, to evaluate the applicability of the suggested model, it was applied to an example of the weight estimation of the FPSO plant topside. Compared with the results of the multiple nonlinear regression analysis that was conducted in the previous study, the results showed that the suggested model can be applied to the weight estimation process of the FPSO plant at the early design stage.