Use of Hybrid PSOGSA Search Algorithm for Optimum Design of RC Beam

Authors

  • Sonia Chutani  Ph.D Scholar, IKG Punjab Technical University, Kapurthala, Punjab, India
  • Jagbir Singh  Department of Civil Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India

Keywords:

Optimum Design, Particle Swarm Optimization, Gravitational Search Algorithm, Indian Design Standards.

Abstract

A more realistic and optimum design of reinforced concrete (RC) beam using a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA) is presented in this paper. Optimal size and reinforcement of the beam element have been found by employing the technique in computer aided environment, whereby the whole process of analysis, design and optimization has been coded in C++. The analysis and design procedure follows specifications of Indian codes. Use of gravitational search along with standard particle swarm optimization technique has been found to possess a better capability to escape from local optimums with faster convergence than the standard PSO and GSA. In this approach, different variables of beam element have been considered as continuous functions and rounded off appropriately to imbibe the practical relevance of the present study. Few beam design examples have been considered to emphasis the validity of this optimum design procedure.

References

  1. A. Kaveh and O. Sabji. 2011. “Optimum design of reinforced frames using a heuristic Particle Swarm –Ant Colony Optimization.” Proceedings of second International Conference on Soft Computing Technology, Civil-comp Press. Stirbngshire, Scotland.
  2. O.K. Erol and I. Eksin. “A new optimization method: Big Bang-Big Crunch.” Advances in Engineering Software, 37(2), 106–111.
  3. B. Saini, V.K. Sehgal and M. L. Gambhir. 2006. “Genetically optimized artificial neural networks based optimum design of singly and doubly reinforced concrete beams.” Asian Journal of Civil Engineering (Building and Housing), 7(6), 603-619.
  4. C.V. Camp and F. Huq. 2013. “CO2 and cost optimization of reinforced concrete frames using a big bang-big crunch algorithm.” Engineering Structures, 48, 363–372.
  5. C.V. Camp. 2007. “Design of space trusses using Big Bang-Big Crunch optimization.”Journal of Structural Engineering ASCE, 133(7), 999–1008.
  6.  A. Kaveh and S. Talatahari. 2009. “Optimal design of Schwedler and ribbed domes via hybrid Big Bang-Big Crunch algorithm.”Journal of Constructional Steel Research, 66(3), 412–419.
  7. A. Kaveh and S. Talatahari. 2010. “A discrete Big Bang-Big Crunch algorithm for optimal design of skeletal structure.” Asian Journal of Civil Engineering, 11(1), 103–22.
  8. M.G. Sahab, A.F. Ashour and V.V. Toporov. 2005. “Cost optimization of reinforced concrete flat slab buildings”, Engineering Structures, 27, 313–322.
  9. P.S. Shelokar, P. Siarry, V.K. Jayaraman and B.D. Kulkarni, B.D. 2007. “Particle swarm and ant colony algorithms hybridized for improved continuous optimization.”Applied Mathematic Computation, 188, 129-42.
  10. A. Kaveh and S. Talatahari.2008. “A Hybrid Particle Swarm And Ant Colony Optimization For Design Of Truss Structures.” Asian Journal of Civil Engineering (Building and Housing), 9(4), 329-348.
  11. M. Alqedra, M. Arafa and M. Ismail. 2011. “Optimum Cost of Prestressed    and Reinforced Concrete Beams using Genetic Algorithms.”Journal of Artificial Intelligence, 4(1), 76-88.
  12. K. Alreshaid, I.M. Mahdi and E. Soliman. 2004. “Cost Optimization of Reinforced Concrete Elements.”Asian Journal of Civil Engineering (Building and Housing), 5(3-4), 161-174. 
  13. V. Govindaraj and J.V. Ramasamy. 2005. “Optimum design of reinforced continuous beams by genetic algorithms.” Computers and Structures, 84, 34-48.
  14. C.A. Coello, A.D. Christiansen and F. Santos. 1997. “A simple genetic algorithm for the design of reinforced concrete beams.” Engineering with Computers, 13(4), 185-196.
  15. E. Rashedi, H. Nezamabadi-pour and S. Saryazdi. 2009. “GSA: A gravitational search algorithm,” Information Sciences, 179, 2232-2248.
  16. J. Kennedy and R.C. Eberhart. 1995. "Particle swarm optimization,” in Proceedings of IEEE international conference on neural networks, 4, 1942–1948.
  17. I.C. Trelea. 2003. “The particle swarm optimization algorithm: Convergence analysis and parameter selection.” Information Processing Letters, 85, 317–325.
  18. Y.D. Valle, G.K. Venayagamoorthy, S. Mohagheghi, J.C. Hernandez andR.G.Harley. 2008. “Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems.” IEEE Transactions on Evolutionary Computation, 12(2).
  19. S. Mirjalili and S.M. Hashim. 2010. “A new hybrid PSOGSA algorithm for function optimization “in International conference on computer and information application (ICCIA), 374-377.
  20. IS: 456:2000, “Plain and Reinforced Concrete Code of Practice.” Bureau of Indian Standards, Manak Bhavan, New Delhi.
  21. C. Lee and J. Ahn. 2003. “Flexural design of reinforced concrete frames by genetic algorithm.”Journal of Structural Engineering ASCE, 129(6), 762-774.
  22. C.V. Camp, S. Pezeshk and H. Hansson. 2003. “Flexural design of Reinforced Concrete Frames Using a Genetic Algorithms.”Journal of Structural Engineering ASCE, 129(1), 105-115.
  23. S. Rajeev and C.S. Krisnamoorthy. 1998. “Genetic algorithm-based methodology for design optimization of reinforced concrete frames.”Computer-Aided Civil Infrastructure Engineering, 13, 63–74.

Downloads

Published

2017-08-31

Issue

Section

Research Articles

How to Cite

[1]
Sonia Chutani, Jagbir Singh, " Use of Hybrid PSOGSA Search Algorithm for Optimum Design of RC Beam, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 5, pp.480-487, July-August-2017.