# Frontiers of Structural and Civil Engineering

 Front. Struct. Civ. Eng.    2019, Vol. 13 Issue (4) : 863-889     https://doi.org/10.1007/s11709-019-0523-9
 RESEARCH ARTICLE
Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology
Tugrul TALASLIOGLU()
Department of Civil Engineering, Osmaniye Korkut Ata University, Osmaniye 80000, Turkey
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 Abstract This study concerns with the design optimization of steel skeletal structures thereby utilizing both a real-life specification provisions and ready steel profiles named hot-rolled I sections. For this purpose, the enhanced genetic algorithm methodology named EGAwMP is utilized as an optimization tool. The evolutionary search mechanism of EGAwMP is constituted on the basis of generational genetic algorithm (GGA). The exploration capacity of EGAwMP is improved in a way of dividing an entire population into sub-populations and using of a radial basis neural network for dynamically adjustment of EGAwMP’s genetic operator parameters. In order to improve the exploitation capability of EGAwMP, the proposed neural network implementation is also utilized for prediction of more accurate design variables associating with a new design strategy, design codes of which are based on the provisions of LRFD_AISC V3 specification. EGAwMP is applied to determine the real-life ready steel profiles for the optimal design of skeletal structures with 105, 200, 444, and 942 members. EGAwMP accomplishes to increase the quality degrees of optimum designations Furthermore, the importance of using the real-life steel profiles and design codes is also demonstrated. Consequently, EGAwMP is suggested as a design optimization tool for the real-life steel skeletal structures. Corresponding Authors: Tugrul TALASLIOGLU Just Accepted Date: 07 March 2019   Online First Date: 23 April 2019    Issue Date: 10 July 2019
 Cite this article: Tugrul TALASLIOGLU. Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology[J]. Front. Struct. Civ. Eng., 2019, 13(4): 863-889. URL: http://journal.hep.com.cn/fsce/EN/10.1007/s11709-019-0523-9 http://journal.hep.com.cn/fsce/EN/Y2019/V13/I4/863
 Fig.1  A pseudo code for EGAwMP[29] Tab.1  Genetic operators and their related parameters for binary-coded design variables (see Fig. 1 for ParAll) Fig.2  (a) Front view and (b)schematic view of w-shaped profiles corresponding to optimum designation of planar frame with 105-bar Fig.3  Trend steps (a) for migration (b)for competition and (c) trend line for feasible designs stored in best result (planar frame with 105-bar) Fig.4  Fig. 4 The convergence history for average of best result and 10 runs (planar frame with 105-bar) Fig.5  (a)Maximum displacements and (b)unities values corresponding to optimum design (a-b) (planar frame with 105-bar) Tab.2  Optimum design comparison for planar frame with 105-bar Fig.6  (a) Front view and (b) schematic view of w-shaped profiles corresponding to optimum designation of planar structure with 200-bar (Design case I) and (c) (Design case II) Fig.7  Fig. 7 (a) Trend steps for migration; (b) for competition; (c) trend line for feasible designs stored in best result (planar structure with 200-bar and Design case I) Fig.8  The convergence history for average of best result and 10 runs (planar structure with 200-bar and Design case I) Fig.9  Maximum displacements and unity values corresponding to feasible design with higher quality obtained when ParOGN=7 (a-b) and optimum design (c-d) (planar structure with 200-bar and Design case I) Tab.3  Optimum design comparison for planar structure with 200-bar (Design case I) Fig.10  Trend Steps for Migration (a), for Competition (b) and Trend Line for Feasible Designs Stored in Best Result (c) (Planar Structure with 200-bar and Design Case II) Fig.11  The Convergence History for Average of Best Result and 10 Runs (200-bar Planar Structure, Design Case II) Fig.12  Maximum displacements and unity values corresponding to optimum design (a-b) (planar structure with 200-bar and Design case II) Tab.4  Optimum design comparison for planar structure with 200-bar (Design case II) Fig.13  (a) Plan and (b)perspective view along with (c) schematic view of w-shaped profiles corresponding to optimum designation of spatial structure with 444-bar Fig.14  Trend steps for migration, (b)for competition (c) trend line for feasible designs stored in best result (spatial structure with 444-bar) Fig.15  The convergence history for average of best result and 10 runs (spatial structure with 444-bar) Fig.16  Maximum displacements and unity values corresponding to feasible design with higher quality obtained when ParOGN=5 (a-b) and optimum design (c-d) (spatial structure with 444-bar) Tab.5  Optimum design comparison for spatial truss structure with 444-bar Fig.17  (a) Plan and (b)perspective view along with schematic view of w-shaped profiles (c) corresponding to optimum designation of spatial structure with 942-bar Tab.6  Joint load values and their distribution in x, y and z directions according to node numbers Fig.18  Trend steps for migration (a), for competition (b) and trend line for feasible designs stored in best result (c) (spatial structure with 942-bar) Fig.19  The convergence history for average of best result and 10 runs (spatial structure with 942-bar) Fig.20  Maximum displacements and unity values corresponding to feasible design with higher quality obtained when ParOGN=7 (a-b) and optimum design (c-d) (spatial structure with 942-bar) Tab.7  Optimum design comparison for spatial structure with 942-bar
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