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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.35 No.3
- 발행연도
- 2020.1
- 수록면
- 415 - 437 (23page)
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초록· 키워드
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Circular concrete filled steel tube (CFST) columns have an advantage over all other sections when they are used in compression members. This paper proposes a new approach for deriving a new empirical equation to predict the axial compressive capacity of circular CFST columns using the Artificial Neural Network (ANN). The developed ANN model uses 5 input parameters that include the diameter of circular steel tube, the length of the column, the thickness of steel tube, the steel yield strength and the compressive strength of concrete. The only output parameter is the axial compressive capacity. Training and testing the developed ANN model was carried out using 219 available sets of data collected from the experimental results in the literature. An empirical equation is then proposed as an important result of this study, which is practically used to predict the axial compressive capacity of a circular CFST column. To evaluate the performance of the developed ANN model and the proposed equation, the predicted results are compared with those of the empirical equations stated in the current design codes and other models. It is shown that the proposed equation can predict the axial compressive capacity of circular CFST columns more accurately than other methods. This is confirmed by the high accuracy of a large number of existing test results. Finally, the parametric study result is analyzed for the proposed ANN equation to consider the effect of the input parameters on axial compressive strength.
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참고문헌 (52)
참고문헌 신청
Farid Abed / 2013 / Experimental and numerical investigations of the compressive behavior of concrete filled steel tubes (CFSTs) / Journal of Constructional Steel Research 80:429~439
AIJ / 1997 / Recommendations for design and construction of concrete filled steel tubular structures / Architectural Institute of Japan
Altun, F. / 2008 / Predicting the compressive strength of steel fiber added lightweight concrete using neural network / Comput. Mater. Sci 42(2):259~265
American concrete institute / 1962 / ACI 318. Building code requirements for structural concrete and commentary (Vol. 552)
Farhad Aslani / 2016 / Statistical calibration of safety factors for flexural stiffness of composite columns / Steel and Composite Structures, An International Journal 20(1):127~145
AIJ / 1997 / Recommendations for design and construction of concrete filled steel tubular structures / Architectural Institute of Japan
Altun, F. / 2008 / Predicting the compressive strength of steel fiber added lightweight concrete using neural network / Comput. Mater. Sci 42(2):259~265
American concrete institute / 1962 / ACI 318. Building code requirements for structural concrete and commentary (Vol. 552)
Farhad Aslani / 2016 / Statistical calibration of safety factors for flexural stiffness of composite columns / Steel and Composite Structures, An International Journal 20(1):127~145
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