Steel and Composite Structures

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Predicting the axial compressive capacity of circular concrete filled steel tube columns using an artificial neural network

  • Nguyen, Mai-Suong T. (Department of Civil and Environmental Engineering, Sejong University) ;
  • Thai, Duc-Kien (Department of Civil and Environmental Engineering, Sejong University) ;
  • Kim, Seung-Eock (Department of Civil and Environmental Engineering, Sejong University)
  • Received : 2019.12.24
  • Accepted : 2020.04.10
  • Published : 2020.05.10
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Abstract

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.

Keywords

Acknowledgement

This research described in this paper was financially supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.2018R1A2A2A05018524 and No. 2019R1A4A1021702).

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