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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Steel and Composite Structures, An International Journal Steel and Composite Structures, An International Journal Vol.40 No.3
- 발행연도
- 2021.1
- 수록면
- 461 - 479 (19page)
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초록· 키워드
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This study aims to propose a new intelligence technique of predicting the ultimate capacity of axially loaded circular concrete-filled steel tube (CCFST) columns. A hybrid system based on one of the evolution algorithm – Genetic Algorithm (GA), fused with a well-known data-driven model of multivariate adaptive regression splines (MARS), namely G-MARS, was proposed and applied. To construct the MARS model, a database of 504 experimental cases was collected from the available literature. The GA was utilized to determine an optimal set of MARS’ hyperparameters, to improve the prediction accuracy. The compiled database covered five input variables, including the diameter of the circular cross section-section (D), the wall thickness of the steel tube (t), the length of the column (L), the compressive strength of the concrete (fc'), and the yield strength of the steel tube (fy). A new explicit formulation was derived from MARS in further analysis, and its estimation accuracy was validated against a benchmark model, G-ANN, an artificial neural network (ANN) optimized using the same metaheuristic algorithm. The simulation results in terms of error range and statistical indices indicated that the derived formula had a superior capability in predicting the ultimate capacity of CCFST columns, relative to the G-ANN model and the other existing empirical methods.
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