INVESTIGATING THE EFFECT OF JOINT BEHAVIOR ON THE OPTIMUM DESIGN OF STEEL FRAMES VIA HUNTING SEARCH ALGORITHM

被引:5
作者
Dogan, Erkan [1 ]
Seker, Soner [2 ]
Saka, M. Polat [3 ]
Kozanoglu, Celalettin [1 ]
机构
[1] Celal Bayar Univ, Dept Civil Engn, TR-45140 Manisa, Turkey
[2] Usak Univ, Dept Civil Engn, TR-64200 Usak, Turkey
[3] Univ Bahrain, Dept Civil Engn, Isa Town, Bahrain
来源
ADVANCED STEEL CONSTRUCTION | 2018年 / 14卷 / 02期
关键词
Stochastic Search techniques; hunting search algorithm; optimization problems; semi-rigid steel frames; end plate connections; SEMIRIGID CONNECTIONS;
D O I
10.18057/IJASC.2018.14.2.3
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This study aims to carry out the effect of beam-to-column connections on the minimum weight design of steel plane frames In the practical analysis of steel frames, end connections are assumed to be either fully restrained or pin-connected. However, experiments reveal that the real behavior is between these extremes and should be taken into account for the realistic design of structures. Hunting search algorithm is used for the automation of optimum design process. It is a numerical optimization method inspired by group hunting of animals such as wolves and lions. It is proven that it is a reliable and efficient technique for obtaining the solution of discrete structural optimization problems. Present design algorithm developed on the basis of hunting search algorithm selects w- sections for the members of semi rigid steel frame from the complete list of w- sections given in LRFD- AISC (Load and Resistance Factor Design, American Institute of Steel Construction). The design constraints are implemented from the specifications of the same code which covers serviceability and strength limitations. The selection of w-sections is carried out such that the design limitations are satisfied and the weight of semirigid frame is the minimum. In order to demonstrate its efficiency, three different steel frames are designed by the optimum design algorithm presented. The designs obtained by use of this algorithm are also compared with the ones produced by particle swarm optimization method.
引用
收藏
页码:166 / 183
页数:18
相关论文
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