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Optimal Building Frame Column Design Based on the Genetic Algorithm

Tao Shen1,*, Yukari Nagai1, Chan Gao2

Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi City, Ishikawa 923-1292, Japan.
Architecture Department, Huzhou University, Huzhou, Zhejiang, 313000, China.

* Corresponding Author: Tao Shen. Email: email.

Computers, Materials & Continua 2019, 58(3), 641-651. https://doi.org/10.32604/cmc.2019.04885

Abstract

Building structure is like the skeleton of the building, it bears the effects of various forces and forms a supporting system, which is the material basis on which the building depends. Hence building structure design is a vital part in architecture design, architects often explore novel applications of their technologies for building structure innovation. However, such searches relied on experiences, expertise or gut feeling. In this paper, a new design method for the optimal building frame column design based on the genetic algorithm is proposed. First of all, in order to construct the optimal model of the building frame column, building units are divided into three categories in general: building bottom, main building and building roof. Secondly, the genetic algorithm is introduced to optimize the building frame column. In the meantime, a PGA-Skeleton based concurrent genetic algorithm design plan is proposed to improve the optimization efficiency of the genetic algorithm. Finally, effectiveness of the mentioned algorithm is verified through the simulation experiment.

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APA Style
Shen, T., Nagai, Y., Gao, C. (2019). Optimal building frame column design based on the genetic algorithm. Computers, Materials & Continua, 58(3), 641-651. https://doi.org/10.32604/cmc.2019.04885
Vancouver Style
Shen T, Nagai Y, Gao C. Optimal building frame column design based on the genetic algorithm. Comput Mater Contin. 2019;58(3):641-651 https://doi.org/10.32604/cmc.2019.04885
IEEE Style
T. Shen, Y. Nagai, and C. Gao "Optimal Building Frame Column Design Based on the Genetic Algorithm," Comput. Mater. Contin., vol. 58, no. 3, pp. 641-651. 2019. https://doi.org/10.32604/cmc.2019.04885



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