City Research Online

Abstract

In this paper a model for the prediction of the ultimate axial compressive capacity of square and rectangular Concrete Filled Steel Tubes, based on an Artificial Neural Network modeling procedure is presented. The model is trained and tested using an experimental database, compiled for this reason from the literature that amounts to 1193 specimens, including long, thin-walled and high-strength ones. The proposed model was selected as the optimum from a plethora of alternatives, employing different activation functions in the context of Artificial Neural Network technique. The performance of the developed model was compared against existing methodologies from design codes and from proposals in the literature, employing several performance indices. It was found that the proposed model achieves remarkably improved predictions of the ultimate axial load.

Publication Type:	Article
Additional Information:	Copyright © 2022 Techno-Press. This is the author accepted manuscript of an article published in Steel and Composite Structures: an international journal.
Publisher Keywords:	artificial neural network; CFST column; soft computing; ultimate axial load
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Departments:	School of Science & Technology > Engineering
SWORD Depositor:	Symplectic Administrator

Export

Downloads