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Structural optimization of closed built-up cold-formed steel columns
Abstract Cold-formed steel (CFS) members are commonly found in single sections, whereas built-up CFS members are usually composed of existing single sections. There is a lack of designed shapes for single sections targeting high structural performance built-up members, i.e., maximizing their resistance-to-weight ratio. Furthermore, traditionally, design optimization methods have a solid mathematical background (gradient-based methods for instance) and use design guidelines for the search of the optimal solution. The construction industry is responsible for a large share of the worldwide consumption of natural resources, and structural optimization plays an important role in improving the sustainability of the sector and reducing the impact of climate change. Non-traditional search optimization methods such as evolutionary algorithms are growing popularity in engineering optimization problems due to i) their nature-inspired technique, ii) their easy way (for the user) of solving complex real-world optimization problems, and iii) the current advanced computing machines, which provide sufficient computational speed to generate solutions to difficult problems in reasonable time. Therefore, optimized structural solutions for closed built-up CFS columns under compression are proposed by using the particle swarm optimization (PSO) algorithm and the finite element method (FEM). Design predictions for the optimum columns based on European Code and North American Specification are also given and compared with the numerical ones. Finally, the findings of this research work show how some parameters (including steel thickness, cross-section height and column length) can affect the optimum solution of the studied objective function (resistance-to-weight ratio), especially the steel thickness.
Highlights Structural optimization of closed built-up CFS columns. Combined application of swarm intelligence (PSO) and finite element method (FEM). Optimized closed built-up sections in terms of resistance-to-weight ratio under compression were proposed. The influence of some parameters (steel thickness, cross-section height and column length) on the optimum solution was shown. Design predictions for the optimum columns based on European Code and North American Specification were also given.
Structural optimization of closed built-up cold-formed steel columns
Abstract Cold-formed steel (CFS) members are commonly found in single sections, whereas built-up CFS members are usually composed of existing single sections. There is a lack of designed shapes for single sections targeting high structural performance built-up members, i.e., maximizing their resistance-to-weight ratio. Furthermore, traditionally, design optimization methods have a solid mathematical background (gradient-based methods for instance) and use design guidelines for the search of the optimal solution. The construction industry is responsible for a large share of the worldwide consumption of natural resources, and structural optimization plays an important role in improving the sustainability of the sector and reducing the impact of climate change. Non-traditional search optimization methods such as evolutionary algorithms are growing popularity in engineering optimization problems due to i) their nature-inspired technique, ii) their easy way (for the user) of solving complex real-world optimization problems, and iii) the current advanced computing machines, which provide sufficient computational speed to generate solutions to difficult problems in reasonable time. Therefore, optimized structural solutions for closed built-up CFS columns under compression are proposed by using the particle swarm optimization (PSO) algorithm and the finite element method (FEM). Design predictions for the optimum columns based on European Code and North American Specification are also given and compared with the numerical ones. Finally, the findings of this research work show how some parameters (including steel thickness, cross-section height and column length) can affect the optimum solution of the studied objective function (resistance-to-weight ratio), especially the steel thickness.
Highlights Structural optimization of closed built-up CFS columns. Combined application of swarm intelligence (PSO) and finite element method (FEM). Optimized closed built-up sections in terms of resistance-to-weight ratio under compression were proposed. The influence of some parameters (steel thickness, cross-section height and column length) on the optimum solution was shown. Design predictions for the optimum columns based on European Code and North American Specification were also given.
Structural optimization of closed built-up cold-formed steel columns
Laím, Luís (Autor:in) / Mendes, Jérôme (Autor:in) / Craveiro, Hélder D. (Autor:in) / Santiago, Aldina (Autor:in) / Melo, Carlos (Autor:in)
31.03.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Cold-formed steel , Columns , Closed built-up sections , Buckling , Finite element analysis , Particle swarm optimization , Resistance-to-weight ratio , Computational intelligence , ACO , ant colony optimization , AE , artificial strain energy , CFS , cold-formed steel , DA , dynamic analysis , DE , dissipated energy fraction , DSM , direct strength method , EAs , evolutionary algorithms , EC3 , European Standards [1] , EWM , effective width method , FEA , finite element analysis , FEM , finite element method, finite element model , GA , genetic algorithm , IE , internal energy , KE , kinetic energy , OF , objective function , PSO , particle swarm optimization , SA , static analysis , SD , viscous damping energy , SE , elastic strain energy , SDS , stochastic diffusion search
Closed built‐up cold‐formed steel columns under compression
| Wiley | 2023
Closed built‐up cold‐formed steel columns under compression
| Wiley | 2023