Techno Press
You logged in as Techno Press

Steel and Composite Structures
  Volume 35, Number 3, May10 2020, pages 415-437
DOI: http://dx.doi.org/10.12989/scs.2020.35.3.415
 


Predicting the axial compressive capacity of circular concrete filled steel tube columns using an artificial neural network
Mai-Suong. Nguyen, Duc-Kien Thai and Seung-Eock Kim

 
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.
 
Key Words
    axial compressive capacity; concrete filled steel tube; empirical equation; artificial neural network; parametric study
 
Address
Duc-Kien Thai and Seung-Eock Kim: Department. of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 05006, South Korea
Mai-Suong. Nguyen: Department. of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 05006, South Korea;
Thuyloi University, 175 Tay Son, Hanoi, Vietnam
 

Techno-Press: Publishers of international journals and conference proceedings.       Copyright © 2021 Techno Press
P.O. Box 33, Yuseong, Daejeon 305-600 Korea, Tel: +82-42-828-7996, Fax : +82-42-828-7997, Email: info@techno-press.com