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Coupled element and structural level optimisation framework for cold-formed steel frames

Authors :
James B.P. Lim
Jun Ye
Seyed Mohammad Mojtabaei
Iman Hajirasouliha
Duoc T. Phan
Publication Year :
2020

Abstract

Optimisation of cold-formed steel (CFS) structures can be challenging due to the complex behaviour of thin-walled CFS sections affected by different buckling modes. In this paper, a coupled framework is presented for element and structural level optimisation of CFS portal frames, under serviceability limit state (SLS) and ultimate limit state (ULS) conditions, using Genetic Algorithm. First, CFS lipped-channel beam sections are optimised with respect to their flexural capacity determined in accordance with the effective width method specified in Eurocode 3 (EC3). The relative dimensions of the cross-section are considered as the main design variables, while the EC3 plate dimensions and slenderness limits and a number of manufacturing and end-use constraints are taken into account in the optimisation process. The results show that the optimum CFS sections exhibit significantly higher (up to 84%) ultimate capacity compared to the standard lipped channel sections with the same plate width and thickness. The structural level optimisation is then carried out to obtain the optimal design solution for a long-span CFS portal frame with knee braces under SLS and ULS conditions. Compared to conventional optimisation using standard cross-sections, it is shown that the proposed coupled framework leads to more cost-effective solutions (up to 20% less structural material) by using the more efficient CFS cross-sectional shapes optimised for generic applications. The results also indicate that optimising the frame geometry and knee brace configuration can noticeably improve the structural performance and reduce the required structural weight, especially when both ULS and SLS conditions are considered.

Details

Language :
English
ISSN :
0143974X
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....224a022e6aef482f13470b09487c00cb