Optimum design of unbraced steel frames to LRFD-AISC using particle swarm optimization

Date

2012-04-01

Author

Dogan, E.
Saka, M. P.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Particle Swarm method based optimum design algorithm for unbraced steel frames is presented. The Particle Swarm method is a numerical optimization technique that simulates the social behavior of birds, fishes and bugs. In nature fish school, birds flock and bugs swarm not only for reproduction but for other reasons such as finding food and escaping predators. Similar to birds seek to find food, the optimum design process seeks to find the optimum solution. In the particle swarm optimization each particle in the swarm represents a candidate solution of the optimum design problem. In the optimum design algorithm presented the design constraints are imposed in accordance with LRFD-AISC (Load and Resistance Factor Design, American Institute of Steel Construction). In the design of beam-column members the combined strength constraints are considered that take into account the lateral torsional buckling of the member. The algorithm developed selects optimum W sections for beams and columns of unbraced frame from the list of 272 W-sections list. This selection is carried out such that design constraints imposed by the LRFD are satisfied and the minimum frame weight is obtained. The efficiency of the algorithm is demonstrated considering a number of design examples. (C) 2011 Civil-Comp Ltd and Elsevier Ltd. All rights reserved.

Subject Keywords

Optimum design, Minimum weight, Steel frame, Combinatorial optimization, Swarm intelligence, Particle swarm optimizer

URI

https://hdl.handle.net/11511/64602

Journal

ADVANCES IN ENGINEERING SOFTWARE

DOI

https://doi.org/10.1016/j.advengsoft.2011.05.008

Collections

Department of Engineering Sciences, Article

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Citation Formats

E. Dogan and M. P. Saka, “Optimum design of unbraced steel frames to LRFD-AISC using particle swarm optimization,” ADVANCES IN ENGINEERING SOFTWARE, pp. 27–34, 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64602.