Model-Based Analysis for Ultimate Axial Load of Circular CFST Columns Using Artificial Neural Network

مهندسی عمران فردوسی

Article 3, Volume 32, Issue 3 - Serial Number 27, August 2019, Pages 33-48   

Document Type: پژوهشی

DOI: 10.22067/civil.v32i3.73399

Authors

M.H Yaqoubi¹; Behrooz Keshtegar^{* 1}; H.A Rahdar²

¹Univrsity of Zabol

²Uonivercity of Zabol

Abstract

Concrete-filled steel tube (CFST) columns are increasingly adopted in many modern structures due to the advantage composite action between steel tube and concrete core. The almost empirical models and design code relations can not provide the accurate predictions for the axial ultimate strength of these composite steel-concrete sections, especially high (ultra-high) strength concrete. In this paper, a novel predicted model of the ultimate axial strength was introduced based on Artificial Neural Network (ANN) using a large experimental data set more 1168 sample data on CFST. The predicted results of the developed ANN model were compared with eight empirical newest models and EC4 code relations using several error statistics. The ANN predictions are more accurate than the existing (code design) models and covers a wider range of material strengths and the ratio of diameter to thickness and height to diameter.
 

Keywords

Concrete-filled steel tube; Axial ultimate strength; Artificial neural network; Modeling

Supplementary Files

3 Keshtegar.pdf

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