Experimental and numerical investigation of the behavior of perforated cold-formed square hollow sections under axial and eccentric compression loading
2024
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Advisor: Dr. Öğr. Üyesi Süleyman İstemihan Coşğun
Abstract (EN)
This study mainly investigated the behavior of eccentrically perforated square hollow sections under eccentric compression loading. Cold-formed steel square hollow-section stub columns with nominal dimensions of 100 mm × 100 mm and a length of 410 mm were experimentally tested. The test results of perforated square hollow-section stub columns with nominal wall thicknesses ranging from 2.00 to 5.00 mm were compared with those of nonperforated specimens. Furthermore, the formation and progression of all the test specimens with local buckling were observed using the digital image correlation method. In addition to axial loading, a numerical parametric study was conducted considering different eccentricity values ranging from 25 to 100 mm in 25 mm increments. The results showed that when eccentric flat oval-shaped perforations were formed and a column was subjected to eccentric compression loading, the load-bearing capacity was substantially reduced by up to 75%–78%. Numerical analysis was comprehensively used as a predictive tool to effectively determine both load-bearing capacities and deformation modes of the square hollow-section stub columns. The strong agreement between the numerical model predictions and experimental findings demonstrated the reliability of the method for obtaining the behavior of perforated and nonperforated elements under both axial and eccentric compressive loads in terms of the load-bearing capacity and damage mode. Additionally, the effectiveness of a ring-type hole stiffener was numerically investigated by considering different stiffener thicknesses.
Author
Dr. Mehmet Emin Akcan
Institution
How to Cite
Mehmet Emin Akcan (Master Thesis). Experimental and numerical investigation of the behavior of perforated cold-formed square hollow sections under axial and eccentric compression loading, 2024, Erzincan Binali Yıldırım University.
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