Using neural networks to predict the design load of cold-formed steel compression members

2002 ◽  
Vol 33 (7-10) ◽  
pp. 713-719 ◽  
Author(s):  
E.M.A El-Kassas ◽  
R.I Mackie ◽  
A.I El-Sheikh
2018 ◽  
Vol 22 (3) ◽  
pp. 613-625 ◽  
Author(s):  
M Anbarasu ◽  
M Venkatesan

This work reports numerical results concerning the cold-formed steel built-up I-section columns composed of four U-profiles under axial compression. A finite element model is developed by using the software program ABAQUS. The developed model includes geometric, material nonlinearities and geometric imperfections. The finite element model was verified against the experimental results reported in the cold-formed steel built-up open section columns. In the parametric study, the sections are analysed with several cross-sectional dimension ratios and lengths, in order to assess their influence on the buckling behaviour and ultimate strength of cold-formed steel built-up I-section columns. After presenting and discussing the numerical parametric results, the article shows that the current direct strength method in the North American Specification for cold-formed steel compression members design curve fails to predict adequately the ultimate strength of some of the columns analysed and addresses the modification proposed on current direct strength method curves, providing improved predictions of all the numerical ultimate strength available. The proposed method is also assessed by reliability analysis.


2007 ◽  
Vol 45 (3) ◽  
pp. 330-338 ◽  
Author(s):  
Ben Young ◽  
Ehab Ellobody

2006 ◽  
Vol 62 (10) ◽  
pp. 962-973 ◽  
Author(s):  
Ibrahim H. Guzelbey ◽  
Abdulkadir Cevik ◽  
Ahmet Erklig

2015 ◽  
Vol 125 ◽  
pp. 850-856 ◽  
Author(s):  
Ali Awaludin ◽  
Kundari Rachmawati ◽  
Made Aryati ◽  
Anindha Dyah Danastri

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