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A new empirical formula for prediction of the axial compression capacity of CCFT columns

  • Tran, Viet-Linh (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 : 2018.12.27
  • Accepted : 2019.09.30
  • Published : 2019.10.25

Abstract

This paper presents an efficient approach to generate a new empirical formula to predict the axial compression capacity (ACC) of circular concrete-filled tube (CCFT) columns using the artificial neural network (ANN). A total of 258 test results extracted from the literature were used to develop the ANN models. The ANN model having the highest correlation coefficient (R) and the lowest mean square error (MSE) was determined as the best model. Stability analysis, sensitivity analysis, and a parametric study were carried out to estimate the stability of the ANN model and to investigate the main contributing factors on the ACC of CCFT columns. Stability analysis revealed that the ANN model was more stable than several existing formulae. Whereas, the sensitivity analysis and parametric study showed that the outer diameter of the steel tube was the most sensitive parameter. Additionally, using the validated ANN model, a new empirical formula was derived for predicting the ACC of CCFT columns. Obviously, a higher accuracy of the proposed empirical formula was achieved compared to the existing formulae.

Keywords

artificial neural network;axial compression capacity;circular concrete-filled tube;empirical formula

Acknowledgement

Supported by : National Research Foundation of Korea (NRF)