Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network

Author:

Fang ZhiyuanORCID,Roy KrishanuORCID,Mares Jiri,Sham Chiu-WingORCID,Chen Boshan,Lim James B.P.

Publisher

Elsevier BV

Subject

Safety, Risk, Reliability and Quality,Building and Construction,Architecture,Civil and Structural Engineering

Reference45 articles.

1. Howick Ltd. Portal Frame Buildings, [Online]. Available: https://www.howickltd.com.

2. AS/NZS (Australian/New Zealand Standard) 2018. Cold-formed steel structures. AS/NZS 4600:2018. Sydney, Australia: Standards Australia.

3. AISI (American Iron and Steel Institute). 2016. North American specification for the design of cold-formed steel structural members. AISI S100-16. Washington, DC: AISI.

4. Schafer BW, Peköz T, Direct strength prediction of cold-formed steel members using numerical elastic buckling solutions, Fourteenth International Specialty Conference on Cold-Formed Steel Structures. St. Louis, Missouri 1998, pp. 69–76.

5. Review: The direct strength method of cold-formed steel member design;Schafer;J Constr Steel Res,2008

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