This paper proposes the use of artificial neural network (ANN) algorithms to estimate the ultimate compressive strength of slender circular concrete-filled steel tubular (CCFST) columns. A dataset of 1051 samples was applied to generate an appropriate ANN prognostic model. Empirical equations were also developed from the best neural network, and their results were compared with those obtained by Eurocode 4 (EC4) design code. Analyses show that the proposed ANN model has a better agreement with experimental results than those created with provisions of the EC4 design code.
TY - CONF
AU - Đorđević, Filip
AU - Kostić, Svetlana M.
PY - 2022
UR - https://grafar.grf.bg.ac.rs/handle/123456789/2764
AB - This paper proposes the use of artificial neural network (ANN) algorithms to estimate the ultimate compressive strength of slender circular concrete-filled steel tubular (CCFST) columns. A dataset of 1051 samples was applied to generate an appropriate ANN prognostic model. Empirical equations were also developed from the best neural network, and their results were compared with those obtained by Eurocode 4 (EC4) design code. Analyses show that the proposed ANN model has a better agreement with experimental results than those created with provisions of the EC4 design code.
C3 - 16th Congress of Association of Structural Engineers of Serbia
T1 - Estimation of ultimate strength of slender ccfst columns using artificial neural networks
UR - https://hdl.handle.net/21.15107/rcub_grafar_2764
ER -
@conference{
author = "Đorđević, Filip and Kostić, Svetlana M.",
year = "2022",
abstract = "This paper proposes the use of artificial neural network (ANN) algorithms to estimate the ultimate compressive strength of slender circular concrete-filled steel tubular (CCFST) columns. A dataset of 1051 samples was applied to generate an appropriate ANN prognostic model. Empirical equations were also developed from the best neural network, and their results were compared with those obtained by Eurocode 4 (EC4) design code. Analyses show that the proposed ANN model has a better agreement with experimental results than those created with provisions of the EC4 design code.",
journal = "16th Congress of Association of Structural Engineers of Serbia",
title = "Estimation of ultimate strength of slender ccfst columns using artificial neural networks",
url = "https://hdl.handle.net/21.15107/rcub_grafar_2764"
}
Đorđević, F.,& Kostić, S. M.. (2022). Estimation of ultimate strength of slender ccfst columns using artificial neural networks. in 16th Congress of Association of Structural Engineers of Serbia.
https://hdl.handle.net/21.15107/rcub_grafar_2764
Đorđević F, Kostić SM. Estimation of ultimate strength of slender ccfst columns using artificial neural networks. in 16th Congress of Association of Structural Engineers of Serbia. 2022;.
https://hdl.handle.net/21.15107/rcub_grafar_2764 .
Đorđević, Filip, Kostić, Svetlana M., "Estimation of ultimate strength of slender ccfst columns using artificial neural networks" in 16th Congress of Association of Structural Engineers of Serbia (2022),
https://hdl.handle.net/21.15107/rcub_grafar_2764 .
