개선된 유전적 프로그래밍 방법을 이용한 부유식 해양 구조물의 중량 추정 모델 연구
A Study on Weight Estimation Model of Floating Offshore Structures using Enhanced Genetic Programming Method
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초록
The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in thepreliminary design, is one of direct measures of both construction cost and basic performance. Through both literature investigation andinternet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimationmodel with the genetic programming was suggested for FPSO. The weight estimation model using genetic programming was establishedby fixing the independent variables based on this data. In addition, the correlation analysis was performed to make up for the weakpoints of genetic programming; it is apt to induce over-fitting when the number of data is relatively smaller than that of independentvariables. That is, by reducing the number of variables through the analysis of the correlation between the independent variables, theincreasing effect in the number of weight data can be expected. The reliability of the developed weight estimation model was within 2%of error rate.
Abstract
The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in thepreliminary design, is one of direct measures of both construction cost and basic performance. Through both literature investigation andinternet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimationmodel with the genetic programming was suggested for FPSO. The weight estimation model using genetic programming was establishedby fixing the independent variables based on this data. In addition, the correlation analysis was performed to make up for the weakpoints of genetic programming; it is apt to induce over-fitting when the number of data is relatively smaller than that of independentvariables. That is, by reducing the number of variables through the analysis of the correlation between the independent variables, theincreasing effect in the number of weight data can be expected. The reliability of the developed weight estimation model was within 2%of error rate.
참고문헌 (14)
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