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Optimization Design of RC Ribbed Floor System Using Eagle Strategy with Particle Swarm Optimization

Jiejiang Zhu1, *, Bolun Zhou1

1 Department of Civil Engineering, Shanghai University, Shanghai, 200444, China.

* Corresponding Author: Jiejiang Zhu. Email: email.

Computers, Materials & Continua 2020, 62(1), 365-383. https://doi.org/10.32604/cmc.2020.06655

Abstract

The eagle strategy algorithm is combined with particle swarm optimization in this paper. The new algorithm, denoted as the ES-PSO, is implemented by interfacing Etabs structural analysis codes. ES-PSO is used to optimize the RC ribbed floor system, including floor and underground garage roof. By considering the effects of reinforcement, the principle of virtual work is applied to calculate the deflections of components. Construction cost is taken as the objective function and the constraint conditions are required to satisfy. Accordingly, the optimal layout, the optimal sections of the beams and slabs and the corresponding reinforcements are obtained for different column grids. In this investigation, the RC ribbed floor system is optimized according to the Chinese standard, whose column grids are 8.4 m and 8.4 m. The performance of the ES-PSO algorithm is good enough, which can be applied to practical engineering. The paper can also provide a basis for subsequent optimization design of monolithic structures.

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Cite This Article

APA Style
Zhu, J., Zhou, B. (2020). Optimization design of RC ribbed floor system using eagle strategy with particle swarm optimization. Computers, Materials & Continua, 62(1), 365-383. https://doi.org/10.32604/cmc.2020.06655
Vancouver Style
Zhu J, Zhou B. Optimization design of RC ribbed floor system using eagle strategy with particle swarm optimization. Comput Mater Contin. 2020;62(1):365-383 https://doi.org/10.32604/cmc.2020.06655
IEEE Style
J. Zhu and B. Zhou, “Optimization Design of RC Ribbed Floor System Using Eagle Strategy with Particle Swarm Optimization,” Comput. Mater. Contin., vol. 62, no. 1, pp. 365-383, 2020. https://doi.org/10.32604/cmc.2020.06655

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cc Copyright © 2020 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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