Optimal design of steel skeletal structures using the enhanced genetic algorithm methodology

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.

Keywords

design optimization / genetic algorithm / multiple populations / neural network

References

[1] Kaveh A, Talatahari S. Optimum design of skeletal structures using imperialist competitive algorithm. Computers & Structures, 2010a, 88(21–22): 1220–1229

[2] Saka M P. Optimum design of steel frames using stochastic search techniques based on natural phenomena: A review. In: BHV Topping, eds. Civil Engineering Computations: Tools and Techniques. UK: Saxe-Coburg Publications, 2007

[3] Haftka R T, Gurdal Z. Elements of Structural Optimization. 3rd ed. Netherlands: Kluwer Academic Publishers, 1992

[4] Kaveh A, Talatahari S. Optimal design of skeletal structures via the charged system search algorithm. Structural and Multidisciplinary Optimization, 2010b, 41(6): 893–911

[5] Kaveh A, Talatahari S.A discrete Big Bang- Big Crunch algorithm for optimal design of skeletal structures. Asian Journal of civil Engineering (Building and Housing), 2010c, 11(1):103–122

[6] Kaveh A, Ghazaan I M. Enhanced whale optimization algorithm for sizing optimization of skeletal structures. Mechanics Based Design of Structures and Machines, 2017, 45(3): 345–362

[7] Kaveh A, Talatahari S. Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Computers & Structures, 2009, 87(5–6): 267–283

[8] Kaveh A, Talatahari S. An improved ant colony optimization for the design of planar steel frames. Engineering Structures, 2010d, 32(3): 864–873

[9] Kaveh A, Majid I G. A new meta-heuristic algorithm: Vibrating particles system. Scientia Iranica, 2017, 24(2): 551–566

[10] Kaveh A, Majid I G. Meta-heuristic Algorithms for Optimal Design of Real-Size Structures. cham: Springer, 2018

[11] Hasançebi O, Erbatur F. Layout optimization of trusses using simulated annealing. Advances in Engineering Software, 2002a, 33(7–10): 681–696

[12] Lamberti L. An efficient simulated annealing algorithm for design optimization of truss structures. Computers & Structures, 2008, 86(19–20): 1936–1953

[13] Lee K S, Geem Z W. A new structural optimization method based on the harmony search algorithm. Computers & Structures, 2004, 82(9–10): 781–798

[14] HasançebiO, Carbas S, Dogan E, Erdal F, Saka M P. Comparison of non-deterministic search techniques in the optimum design of real size steel frames. Computers & Structures, 2010, 88(17–18): 1033–1048

[15] Camp C V. Design of space trusses using Big Bang–Big Crunch optimization. Journal of Structural Engineering, 2007, 133(7): 999–1008

[16] Ghasemi H, Park H S, Rabczuk T. A level-set based IGA formulation for topology optimization of flexoelectric materials. Computer Methods in Applied Mechanics and Engineering, 2017, 313: 239–258

[17] Ghasemi H, Park H S, Rabczuk T. A multi-material level set-based topology optimization of flexoelectric composites. Computer Methods in Applied Mechanics and Engineering, 2018, 332: 47–62

[18] Nanthakumar S S, Lahmer T, Zhuang X, Zi G, Rabczuk T. Detection of material interfaces using a regularized level set method in piezoelectric structures. Inverse Problems in Science and Engineering, 2016, 24(1): 153–176

[19] Talaslioglu T.A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search, Modelling and Simulation in Engineering. 2009

[20] Vu-Bac N, Duong T X, Lahmer T, Zhuang X, Sauer R A, Park H S, Rabczuk T A. NURBS-based inverse analysis for reconstruction of nonlinear deformations of thin shell structures. Computer Methods in Applied Mechanics and Engineering, 2018, 331: 427–455

[21] Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31

[22] Vu-Bac N, Silani M, Lahmer T, Zhuang X, Rabczuk T. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015a, 96: 520–535

[23] Vu-Bac N, Rafiee R, Zhuang X, Lahmer T, Rabczuk T. Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters. Composites Part B, Engineering, 2015b, 68: 446–464

[24] Vu-Bac N, Lahmer T, Keitel H, Zhao J, Zhuang X, Rabczuk T. Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations. Mechanics of Materials, 2014a, 68: 70–84

[25] Vu-Bac N, Lahmer T, Zhang Y, Zhuang X, Rabczuk T. Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs). Composites Part B, Engineering, 2014b, 59: 80–95

[26] Walls R, Elvin A. An algorithm for grouping members in a structure. Engineering Structures, 2010, 32(6): 1760–1768

[27] ANSYS Inc. ANSYS Version 7.0 User's Manual, 2003

[28] Hasançebi O, Erbatur F. Constraint handling in genetic algorithm integrated structural optimization. Acta Mechanica, 1999, 139: 15–28

[29] Talaslioglu T. Design optimisation of dome structures by enhanced genetic algorithm with multiple populations. Scientific Research and Essays, 2012, 7(45): 3877–3896

[30] MATLAB, The Math Works Inc. Natick, MA, 1997

[31] AISC. AISC. Shapes Database v15.0, 5017

[32] Kaveh A, Talatahari S. Charged system search for optimal design of frame structures. Applied Soft Computing, 2012, 12(1): 382–393

[33] Kaveh A, Ilchi Ghazaan M. A comparative study of CBO and ECBO for optimal design of skeletal structures. Computers & Structures, 2015, 153: 137–147

[34] Farshi B, Aliniaziazi A. Sizing optimization of truss structures by method of centers and force formulation. International Journal of Solids and Structures, 2010, 47(18–19): 2508–2524

[35] Lamberti L, Pappalettere C. Move limits definition in structural optimization with sequential linear programming. Part II: Numerical examples. Computers & Structures, 2003, 81(4): 215–238

[36] Lamberti L, Pappalettere C. Improved sequential linear programming formulation for structural weight minimization. Computer Methods in Applied Mechanics and Engineering, 2004, 193(33–35): 3493–3521

[37] Erbatur F, Hasançebi OTütüncü I, Kılıç H. Optimal design of planar and space structures with genetic algorithms. Computers & Structures, 2000, 75(2): 209–224

[38] Hasancebi O, Erbatur F. On efficient use of simulated annealing in complex structural optimization problems. Acta Mechanica, 2002, 157(1–4): 27–50

[39] Hasançebi O. Adaptive evolution strategies in structural optimization: Enhancing their computational performance with applications to large-scale structures. Computers & Structures, 2008, 86(1–2): 119–132

Acknowledgement

Author thanks to both Prof. Dr. Hasancebi O.-METU, Ankara, Turkey for providing additional information about the design examples with 444 and 942-bar and reviewers for their contributions in the improvement of this article.

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