MATEC Web Conf.
Volume 313, 2020Dynamics of Civil Engineering and Transport Structures and Wind Engineering – DYN-WIND’2020
|Number of page(s)||9|
|Published online||16 April 2020|
A statistical method for predicting the eccentric load capacity of rectangular concrete filled steel tubular columns
Ukrainian State University of Railway Transport, Structural Mechanics and Hydraulics Department, Feuerbach sq., 7, Kharkiv, 61050, Ukraine
* Corresponding author: firstname.lastname@example.org
The article deals with the integrated approach to the study of the behaviour of rectangular CFST columns under eccentric compression. Such an approach includes the development of methods for assessing the magnitude of the carrying capacity, assessing the degree of reliability and credibility of the obtained results, as well as studying the nature of the development of columns deformations at various stages of loading. The authors developed a mathematical model for calculation of columns carrying capacity under eccentric compression based on statistical methods. Substantial amount of experimental data collected by the world leading laboratories enabled obtaining a regression dependence of the columns carrying capacity that takes into account the impact of the physical and geometric characteristics of such structures. High degree of model confidence is confirmed by a comparative analysis with experimental results that are not involved in the development of the model, as well as with calculations performed according to Eurocode, Japanese and Chinese regulatory documents. The article presents experimental studies of the nature of deformations development on the surface of the steel shell and inside the concrete core of various lengths rectangular columns. As a result of the experimental tests, it was established that the longitudinal strains of the compressed area of the shell have the most significant impact on the bearing capacity of eccentrically compressed steel concrete samples.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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