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Empirical Approach for Determining Axial Strength of Circular Concrete Filled Steel Tubular Columns

Empirical Approach for Determining Axial Strength of Circular Concrete Filled Steel Tubular Columns The concrete filled steel tubular (CFST) columns are highly regarded in recent years as an interesting option in the construction field by designers and structural engineers, due to their exquisite structural performance, with enhanced load bearing capacity and energy absorption capacity. This study presents a new approach to simulate the capacity of circular CFST columns under axial loading condition, using a large database of experimental results by applying artificial neural network (ANN). A well trained network is established and is used to simulate the axial capacity of CFST columns. The validation and testing of the ANN is carried out. The current study is focused on proposing a simplified equation that can predict the ultimate strength of the axially loaded columns with high level of accuracy. The predicted results are compared with five existing analytical models which estimate the strength of the CFST column. The ANN-based equation has good prediction with experimental data, when compared with the analytical models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of The Institution of Engineers (India): Series A Springer Journals

Empirical Approach for Determining Axial Strength of Circular Concrete Filled Steel Tubular Columns

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Publisher
Springer Journals
Copyright
Copyright © 2018 by The Institution of Engineers (India)
Subject
Engineering; Civil Engineering
ISSN
2250-2149
eISSN
2250-2157
DOI
10.1007/s40030-018-0291-x
Publisher site
See Article on Publisher Site

Abstract

The concrete filled steel tubular (CFST) columns are highly regarded in recent years as an interesting option in the construction field by designers and structural engineers, due to their exquisite structural performance, with enhanced load bearing capacity and energy absorption capacity. This study presents a new approach to simulate the capacity of circular CFST columns under axial loading condition, using a large database of experimental results by applying artificial neural network (ANN). A well trained network is established and is used to simulate the axial capacity of CFST columns. The validation and testing of the ANN is carried out. The current study is focused on proposing a simplified equation that can predict the ultimate strength of the axially loaded columns with high level of accuracy. The predicted results are compared with five existing analytical models which estimate the strength of the CFST column. The ANN-based equation has good prediction with experimental data, when compared with the analytical models.

Journal

Journal of The Institution of Engineers (India): Series ASpringer Journals

Published: Mar 2, 2018

References