Optimum geometrical pattern and design of real-size diagrid structures using accelerated fuzzy-genetic algorithm with bilinear membership function

被引:15
作者
Ashtari, Payam [1 ]
Karami, Roghaye [1 ]
Farahmand-Tabar, Salar [1 ]
机构
[1] Univ Zanjan, Fac Engn, Dept Civil Engn Engn, Zanjan, Iran
关键词
Structural optimization; Tall buildings; Diagrid system; Optimum geometrical patterns; Genetic algorithm; Fuzzy concept; SEISMIC PERFORMANCE-FACTORS; TALL BUILDINGS; STEEL STRUCTURES; OPTIMIZATION; SYSTEM; LOGIC;
D O I
10.1016/j.asoc.2021.107646
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Diagrids are the efficient systems of tube structures for tall buildings. One of the design considerations for these structures is the geometrical pattern of the system. In this paper, a new method of fuzzy genetic algorithm based on bilinear membership functions is proposed with an improved crossover operator and penalty function. The method is applied on tall buildings with a diagrid system to find the optimum geometrical patterns and the overall structural weight. Various three-dimensional diagrid structures with 24, 36, 42, 56, and 60 stories and different slenderness ratios are analyzed under gravity and wind load. Then the effects of variation in the number of bays (4, 6, and 8) are investigated and compared with each other. The results show that by increasing the dimension of the structure, the structural weight is reduced up to 33% in some cases. However, the obtained angle of the diagrid members (range of 63 to 79 degrees) is increased by increasing the number of stories and the height of the structure. The optimum weight and geometrical pattern of the models is obtained and a formulation is extracted from the results regarding the optimum angle of a diagrid system. Considering GA, results show the merit of the accelerated fuzzy-genetic algorithm regarding the convergence and the avoidance of being trapped in local minimum. (C) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页数:14
相关论文
共 61 条
  • [1] Fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problems
    Alavidoost, M. H.
    Tarimoradi, Mosahar
    Zarandi, M. H. Fazel
    [J]. APPLIED SOFT COMPUTING, 2015, 34 : 655 - 677
  • [2] Diagrid structural systems for tall buildings: Changing pattern configuration through topological assessments
    Angelucci, Giulia
    Mollaioli, Fabrizio
    [J]. STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS, 2017, 26 (18)
  • [3] Seismic performance factors for low- to mid-rise steel diagrid structural systems
    Asadi, Esmaeel
    Adeli, Hojjat
    [J]. STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS, 2018, 27 (15)
  • [4] Seismic Performance Assessment and Loss Estimation of Steel Diagrid Structures
    Asadi, Esmaeel
    Li, Yue
    Heo, YeongAe
    [J]. JOURNAL OF STRUCTURAL ENGINEERING, 2018, 144 (10)
  • [5] Diagrid: An innovative, sustainable, and efficient structural system
    Asadi, Esmaeel
    Adeli, Hojjat
    [J]. STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS, 2017, 26 (08)
  • [6] Accelerating fuzzy genetic algorithm for the optimization of steel structures
    Ashtari, Payam
    Barzegar, Farshid
    [J]. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2012, 45 (02) : 275 - 285
  • [7] hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems
    Aydogan, Emel Kizilkaya
    Karaoglan, Ismail
    Pardalos, Panos M.
    [J]. APPLIED SOFT COMPUTING, 2012, 12 (02) : 800 - 806
  • [8] Integrated fuzzy logic and genetic algorithmic approach for simultaneous localization and mapping of mobile robots
    Begum, Momotaz
    Mann, George K. I.
    Gosine, Raymond G.
    [J]. APPLIED SOFT COMPUTING, 2008, 8 (01) : 150 - 165
  • [9] An approximation to solve regression problems with a genetic fuzzy rule ordinal algorithm
    Carlos Gamez, Juan
    Garcia, David
    Gonzalez, Antonio
    Perez, Raul
    [J]. APPLIED SOFT COMPUTING, 2019, 78 : 13 - 28
  • [10] Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction
    Chou, Chih-Hsun
    Hsieh, Su-Chen
    Qiu, Chui-Jie
    [J]. APPLIED SOFT COMPUTING, 2017, 56 : 298 - 316