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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.