Application of ANN in predicting ACC of SCFST column

By Viet-Linh Tran, Duc‐Kien Thai, Seung-Eock Kim. Composite structures, 2019

133 citations recorded by the source corpus.

Abstract The main objective of this paper is to derive a new empirical formula for predicting the axial compression capacity (ACC) of square concrete-filled steel tubular (SCFST) columns using the artificial neural network (ANN). A total of 300 experimental data of SCFST columns extracted from the literature were used for training, testing, and validating the ANN models. The trial and error method was used to determine the best ANN model, which had the highest correlation coefficient (R) and the lowest mean square error (MSE). In addition, several existing and design code formulae were adopted to evaluate the performance of the current study. The comparative results revealed that the ANN model was more stable and accurate than any other existing formula. Using the validated ANN, a number of master curves were generated to establish a new formula to predict the ACC of the SCFST column. The comparisons with the existing formulae showed a higher accuracy of the proposed empirical formula.

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/c7029372b12d68a2f3326db7e629174430fbf2ef