Optimization of 3D steel moment frame structures with torsional irregularity via PSO

Document Type : Research Article

Authors

1 Department of Civil Engineering faculty, Khatam Alanbia University of Technology, Behbahan, Iran

2 Department of Civil Engineering faculty, Islamic Azad University, Ramhormoz, Iran

Abstract

The aim of this study was to evaluate the optimization of three-dimensional steel moment frames structures with torsional irregularity, via particle swam algorithm under earthquake load. In this way, to produce torsional irregularity, the center of stiffness has been kept constant with symmetrical grouping of the members, and the center of mass was placed with specific distances from the center of stiffness. Besides that, two types of three-dimensional steel moment frames structures have been modeled. The first structure has box-shaped columns which is assessed with 0 Percent, 20%, 40% and 60% torsional irregularity. The second structure includes cross-shaped columns which is evaluated with the same condition as first structure. Particle Swarm Optimization (PSO) algorithm has been used to achieve the best structure in terms of: weight, maximum drift, dimensional fit of joints and strong column to weak beam. In addition, in the optimizing process of elements, the required strength of the sections according to AISC 360-16 is satisfied under the LRFD method. The AISC360-16 database is also used for sections of structures (I-shaped beams, box-shaped and cross-shaped columns). The optimization results demonstrated that the weight of the first structure (box-shaped column) with, 20%, 40% and 60% torsional irregularity was higher in comparison with the regular optimal state with 43.242 ton, to 5.5, 9.9 and 11.3%, respectively. Moreover, the weight of the second structure (cross-shaped column) in same noted condition was 0.31, 9.4 and 11.9% more than the weight of the structure in regular optimal state with 54.915 ton, respectively.

Keywords

Main Subjects

References

  1. Goldberg, D.E. and Holland, J.H. (1988) Genetic algorithms and machine learning.
  2. Eberhart, R. and Kennedy, J. (1995) A new optimizer using particle swarm theory‏. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39-43.
  3. Dorigo, M., Maniezzo, V. (1996) Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), pp. 29-41,
  4. Geem, Z.W., Kim, J.H. and Loganathan, G.V. (2001) A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76(2):60-68. doi:10.1177/003754970107600201
  5. Eslami, M., Shareef, H., Khajehzadeh, M. and Mohamed, A. (2012) A Survey of the State of the Art in Particle Swarm Optimization. Research Journal of Applied Sciences, Engineering and Technology, 4, 1181-1197.
  6. Talatahari, S., Gandomi, A., Yang, X.-S. and Deb, S. (2015) Optimum design of frame structures using the Eagle Strategy with Differential Evolution. Engineering Structures, 91, 10.1016/j.engstruct.2015.02.026.
  7. A. Kaveh and S. Talatahari, “A DISCRETE BIG BANG-BIG CRUNCHALGORITHM FOR OPTIMALDESIGN OF SKELETAL STRUCTURES‏,” 2010‏, vol. 11, no. 1, pp. 103–122, 2010, Accessed: Aug. 17, 2021. [Online]. Available: https://www.sid.ir/en/Journal/ViewPaper.aspx?ID=185452
  8. Kaveh, A. and Talatahari, S. (2010) An improved ant colony optimization for the design of planar steel frames. Engineering Structures, 32(3), 864-873.
  9. Kaveh, A. and Talatahari, S. (2010) Optimum design of skeletal structures using imperialist competitive algorithm. Computers & Structures, 88, 1220–1229, doi: 10.1016/j.compstruc.2010.06.011.
  10. Aydoğdu, I., Akın, A. and Saka, M.P. (2016) Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution. Advances in Engineering Software‏, 92, 1–14, doi: 10.1016/j.advengsoft.2015.10.013.
  11. Murren, P. and Khandelwal K. (2014) Design-driven harmony search (DDHS) in steel frame optimization‏. Engineering Structures‏, 59, 798-808.
  12. Carbas, S. (2016) Design optimization of steel frames using an enhanced firefly algorithm. Engineering Optimization, 48(12), 2007–2025, doi: 10.1080/0305215X.2016.1145217.
  13. Aydoğdu, I. and Saka, M.P. (2012) Ant colony optimization of irregular steel frames including elemental warping effect. Advances in Engineering Software‏, 44(1), 150-169.
  14. Eshaghi, M.S., Shishegaran, A. and Rabczuk, T. (2020) Evaluation of a novel Asymmetric Genetic Algorithm to optimize the structural design of 3D regular and irregular steel frames. Frontiers of Structural and Civil Engineering, 14(5), 1110–1130, doi: 10.1007/S11709-020-0643-2.
  15. Kaveh, A. and Ilchi Ghazaan, M. (2018) Optimum Seismic Design of 3D Irregular Steel Frames Using Recently Developed Metaheuristic Algorithms. Journal of Computing in Civil Engineering, 32(3), p. 04018015, doi: 10.1061/(ASCE)CP.1943-5487.0000760.
  16. BHRC Committee (2014) Regulation for Designing Structures against Earthquake (2800 standard), 4th ed., Iran, Tehran: Ministry of Housing and Urban Development.
  17. Zheng, N., Yang, Z., Shi, C. and Chang, Z. (2004) Analysis of Criterion for Torsional Irregularity of Seismic Structures. 13th World Conference on Earthquake Engineering Vancouver, B.C., Canada, August 1-6, Paper No. 1465.
  18. AISC Committee (2010) Specification for structural steel buildings (ANSI/AISC 360-10). Chicago-Illinois: American Institute of Steel Construction.
  19. Doğan, E. and Saka, M.P. (2012) Optimum design of unbraced steel frames to LRFD–AISC using particle swarm optimization‏. Advances in Engineering Software‏, 46(1), 27-34.
  20. Plevris, V., Batavanis, A.G., & Papadrakakis, M. (2011) Optimum design of steel structures with the particle swarm optimization method based on EC3. Available: http://www.vplevris.net/pdf_files/publications/C_22.pdf.
  21. Poitras, G., Lefrançois, G. and Cormier‏, G. (2011) Optimization of steel floor systems using particle swarm optimization. Journal of Constructional Steel Research‏, 67(8), 1225-1231.
Journal of Seismology and Earthquake Engineering
Volume 23, Issue 3
August 2021
Pages 53-65

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