DOI QR코드

DOI QR Code

Efficient gravitational search algorithm for optimum design of retaining walls

  • Khajehzadeh, Mohammad (Department of Civil and Structural Engineering, National University of Malaysia) ;
  • Taha, Mohd Raihan (Department of Civil and Structural Engineering, National University of Malaysia) ;
  • Eslami, Mahdiyeh (Department of Electrical Engineering, Science and Research Branch, Islamic Azad University)
  • 투고 : 2012.08.17
  • 심사 : 2012.12.01
  • 발행 : 2013.01.10

초록

In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and $CO_2$ emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.

키워드

참고문헌

  1. ACI (2005), "318-05, Building Code Requirements for Structural Concrete and Commentary", American Concrete Institute International.
  2. Aguilar Madeira, J., Rodrigues, H. and Pina, H. (2005), "Multi-objective optimization of structures topology by genetic algorithms", Adv. Eng. Softw., 36(1), 21-28. https://doi.org/10.1016/j.advengsoft.2003.07.001
  3. Aydogdu, I. and Saka, M. (2012), "Ant colony optimization of irregular steel frames including elemental warping effect", Adv. Eng. Softw., 44(1), 150-169. https://doi.org/10.1016/j.advengsoft.2011.05.029
  4. Bowles, J. (1982), Foundation analysis and design, McGraw-Hill, New York.
  5. Camp, C., Pezeshk, S. and Cao, G. (1998), "Optimized design of two-dimensional structures using a genetic algorithm", J. Struct. Eng., 124(5), 551-559. https://doi.org/10.1061/(ASCE)0733-9445(1998)124:5(551)
  6. Camp, C.V. and Akin, A. (2012), "Design of Retaining Walls Using Big Bang‐Big Crunch Optimization", J. Struct. Eng., 138(3), 438-448. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000461
  7. Camp, C.V., Bichon, B.J. and Stovall, S.P. (2005), "Design of steel frames using ant colony optimization", J. Struct. Eng., 131(3), 369-379. https://doi.org/10.1061/(ASCE)0733-9445(2005)131:3(369)
  8. Degertekin, S. (2011), "Improved harmony search algorithms for sizing optimization of truss structures", Comput. Struct., (92-93), 229-241. https://doi.org/10.1109/SNPD.2011.15
  9. Degertekin, S. (2012), "Optimum design of geometrically non-linear steel frames using artificial bee colony algorithm", Steel Compos. Struct, 12(6), 505-522. https://doi.org/10.12989/scs.2012.12.6.505
  10. Dogan, E. and Saka, M. (2012), "Optimum design of unbraced steel frames to LRFD-AISC using particle swarm optimization", Adv. Eng. Softw., 46(1), 27-34. https://doi.org/10.1016/j.advengsoft.2011.05.008
  11. Hasançebi, O., Carbas, S. and Saka, M.P. (2010), "Improving the performance of simulated annealing in structural optimization", Struct. Multidiscip. Optim., 41(2), 189-203. https://doi.org/10.1007/s00158-009-0418-9
  12. Hasançebi, O. and Erbatur, F. (2002), "Layout optimisation of trusses using simulated annealing", Adv. Eng. Softw., 33(7), 681-696. https://doi.org/10.1016/S0965-9978(02)00049-2
  13. Khajehzadeh, M., Taha, M.R., El-Shafie, A. and Eslami, M. (2011), "Modified particle swarm optimization for optimum design of spread footing and retaining wall", J. Zhejiang Univ. Sci A, 12(6), 415-427. https://doi.org/10.1631/jzus.A1000252
  14. Lee, K.S. and Geem, Z.W. (2004), "A new structural optimization method based on the harmony search algorithm", Comput. Struct., 82(9), 781-798. https://doi.org/10.1016/j.compstruc.2004.01.002
  15. Perez, R. and Behdinan, K. (2007), "Particle swarm approach for structural design optimization", Comput. Struct., 85(19-20), 1579-1588. https://doi.org/10.1016/j.compstruc.2006.10.013
  16. Rahnamayan, S., Tizhoosh, H.R. and Salama, M. (2008), "Opposition versus randomness in soft computing techniques", Appl. Soft Comput., 8(2), 906-918. https://doi.org/10.1016/j.asoc.2007.07.010
  17. Rashedi, E., Nezamabadi-pour, H. and Saryazdi, S. (2009), "GSA: a gravitational search algorithm", Inform. Sci., 179(13), 2232-2248. https://doi.org/10.1016/j.ins.2009.03.004
  18. Salajegheh, E. and Gholizadeh, S. (2005), "Optimum design of structures by an improved genetic algorithm using neural networks", Adv. Eng. Softw., 36(11), 757-767. https://doi.org/10.1016/j.advengsoft.2005.03.022
  19. Saribas, A. and Erbatur, F. (1996), "Optimization and sensitivity of retaining structures", J. Geotech. Eng., 122(8), 649-656. https://doi.org/10.1061/(ASCE)0733-9410(1996)122:8(649)
  20. Sonmez, M. (2011), "Discrete optimum design of truss structures using artificial bee colony algorithm", Struct. Multidiscip. Optim., 43(1), 85-97. https://doi.org/10.1007/s00158-010-0551-5
  21. Technology, C.I.o.C. (2009), BEDEC PR/PCT ITEC materials database, Barcelona, Spain.
  22. Tizhoosh, H.R. (2005). "Opposition-based learning: A new scheme for machine intelligence", International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2005, Vienna, Austria.
  23. Togan, V., Daloglu, A.T. and Karadeniz, H. (2011), "Optimization of trusses under uncertainties with harmony search", Struct. Eng. Mech., 37(5), 543-560. https://doi.org/10.12989/sem.2011.37.5.543
  24. Wang, W., Guo, S., Chang, N., Zhao, F. and Yang, W. (2010), "A modified ant colony algorithm for the stacking sequence optimisation of a rectangular laminate", Struct. Multidiscip. Optim., 41(5), 711-720. https://doi.org/10.1007/s00158-009-0447-4
  25. Yepes, V., Alcala, J., Perea, C. and González-Vidosa, F. (2008), "A parametric study of optimum earthretaining walls by simulated annealing", Eng. Struct., 30(3), 821-830. https://doi.org/10.1016/j.engstruct.2007.05.023

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