Journal of Zhejiang University-SCIENCE A

, Volume 12, Issue 6, pp 415–427 | Cite as

Modified particle swarm optimization for optimum design of spread footing and retaining wall

  • Mohammad Khajehzadeh
  • Mohd Raihan Taha
  • Ahmed El-Shafie
  • Mahdiyeh Eslami
Article

Abstract

This paper deals with the economically optimized design and sensitivity of two of the most widely used systems in geotechnical engineering: spread footing and retaining wall. Several recent advanced optimization methods have been developed, but very few of these methods have been applied to geotechnical problems. The current research develops a modified particle swarm optimization (MPSO) approach to obtain the optimum design of spread footing and retaining wall. The algorithm handles the problem-specific constraints using a penalty function approach. The optimization procedure controls all geotechnical and structural design constraints while reducing the overall cost of the structures. To verify the effectiveness and robustness of the proposed algorithm, three case studies of spread footing and retaining wall are illustrated. Comparison of the results of the present method, standard PSO, and other selected methods employed in previous studies shows the reliability and accuracy of the algorithm. Moreover, the parametric performance is investigated in order to examine the effect of relevant variables on the optimum design of the footing and the retaining structure utilizing the proposed method.

Key words

Particle swarm optimization (PSO) Spread footing Retaining wall Sensitivity analysis 

CLC number

TU470 TU17 

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References

  1. ACI 318-05, 2005. Building Code Requirements for Structural Concrete and Commentary. American Concrete Institute, Farmington Hills, MI, USA.Google Scholar
  2. Ahmadi-Nedushan, B., Varaee, H., 2009. Optimal Design of Reinforced Concrete Retaining Walls Using a Swarm Intelligence Technique. Proceedings of the First International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering, Funchal, Madeira, Portugal. Civil-Comp Press, Stirlingshire, UK, p.26. [doi:10.4203/ccp.92.26]Google Scholar
  3. Basudhar, P.K., Vashistha, A., Deb, K., Dey, A., 2008. Cost optimization of reinforced earth walls. Geotechnical and Geological Engineering, 26(1):1–12. [doi:10.1007/s10706-007-9143-6]CrossRefGoogle Scholar
  4. Bowles, J., 1982. Foundation Analysis and Design. McGraw-Hill, New York, USA.Google Scholar
  5. Budhu, M., 2006. Soil Mechanics and Foundations. John Wiley & Sons, New York, USA.Google Scholar
  6. Cheng, Y.M., Li, L., Chi, S.C., 2007. Performance studies on six heuristic global optimization methods in the location of critical slip surface. Computers and Geotechnics, 34(6):462–484. [doi:10.1016/j.compgeo.2007.01.004]CrossRefGoogle Scholar
  7. He, Q.Y., Han, C.J., 2006. An improved particle swarm optimization algorithm with disturbance term. Computational Intelligence and Bioinformatics, 4115: 100–108. [doi:10.1007/11816102_11]CrossRefGoogle Scholar
  8. He, S., Wu, Q.H., Wen, J.Y., Saunders, J.R., Paton, R.C., 2004. A particle swarm optimizer with passive congregation. Biosystems, 78(1–3):135–147. [doi:10.1016/j.biosystems. 2004.08.003]CrossRefGoogle Scholar
  9. Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization. IEEE International Conference on Neural Networks, Perth, Australia. IEEE Service Center, Piscataway, p.1942–1948.Google Scholar
  10. Kvam, P.H., Vidakovic, B., 2007. Nonparametric Statistics with Applications to Science and Engineering. John Wiley & Sons, New York, USA. [doi:10.1002/9780470 168707]CrossRefMATHGoogle Scholar
  11. Lee, K.S., Geem, Z., 2004. A new structural optimization method based on the harmony search algorithm. Computers & Structures, 82(9–10):781–798. [doi:10.1016/j. compstruc.2004.01.002]CrossRefGoogle Scholar
  12. Mendes, R., Kennedy, J., Neves, J., 2004. The fully informed particle swarm: simpler, maybe better. IEEE Transactions on Evolutionary Computation, 8(3):204–210. [doi:10. 1109/TEVC.2004.826074]CrossRefGoogle Scholar
  13. Parsopoulos, K.E., Vrahatis, M.N., 2002. Particle Swarm Optimization Method for Constrained Optimization Problems. Proceedings of the Euro-International Symposium on Computational Intelligence, Košice, Slovakia.Google Scholar
  14. Paya-Zaforteza, I., Yepes, V., Hospitaler, A., González-Vidosa, F., 2009. CO2-optimization of reinforced concrete frames by simulated annealing. Engineering Structures, 31(7): 1501–1508. [doi:10.1016/j.engstruct.2009.02.034]CrossRefMATHGoogle Scholar
  15. Perea, C., Alcalá, J., Yepes, V., González-Vidosa, F., Hospitaler, A., 2008. Design of reinforced concrete bridge frames by heuristic optimization. Advances in Engineering Software, 39(8):676–688. [doi:10.1016/j.advengsoft. 2007.07.007]CrossRefGoogle Scholar
  16. Saribas, A., Erbatur, F., 1996. Optimization and sensitivity of retaining structures. Journal of Geotechnical Engineering, 122(8):649–656. [doi:10.1061/(ASCE)0733-9410(1996)122:8(649)]CrossRefGoogle Scholar
  17. Shi, Y., Eberhart, R., 1998. A Modified Particle Swarm Optimizer. IEEE World Congress on Computational Intelligence, Anchorage, AK, USA. IEEE, Piscataway, USA, p.69–73. [doi:10.1109/ICEC.1998.699146]Google Scholar
  18. van den Bergh, F., Engelbrecht, A.P., 2002. A New Locally Convergent Particle Swarm Optimizer. IEEE International Conference on Systems, Man and Cybernetics, Hammamet, Tunisia, p.96–101.Google Scholar
  19. Wang, Y., 2009. Reliability-based economic design optimization of spread foundations. Journal of Geotechnical and Geoenvironmental Engineering, 135(7):954–959. [doi:10.1061/(ASCE)GT.1943-5606.0000 013]CrossRefGoogle Scholar
  20. Wang, Y., Kulhawy, F.H., 2008. Economic design optimization of foundations. Journal of Geotechnical and Geoenvironmental Engineering, 134(8):1097–1105. [doi:10.1061/(ASCE)1090-0241(2008)134:8(1097)]CrossRefGoogle Scholar
  21. Xie, X.F., Zhang, W.J., Yang, Z.L., 2002. Adaptive Particle Swarm Optimization on Individual Level. 6th International Conference on Signal Processing, Beijing, China, p.1215–1218.Google Scholar
  22. Yepes, V., Alcala, J., Perea, C., González-Vidosa, F., 2008. A parametric study of optimum earth-retaining walls by simulated annealing. Engineering Structures, 30(3):821–830. [doi:10.1016/j.engstruct.2007.05.023]CrossRefGoogle Scholar
  23. Zhong, W.M., Li, S.J., Qian, F., 2008. θ-PSO: a new strategy of particle swarm optimization. Journal of Zhejiang University-SCIENCE A, 9(6):786–790. [doi:10.1631/jzus. A071278]CrossRefMATHGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mohammad Khajehzadeh
    • 1
  • Mohd Raihan Taha
    • 2
  • Ahmed El-Shafie
    • 2
  • Mahdiyeh Eslami
    • 3
  1. 1.Department of Civil Engineering, Anar BranchIslamic Azad UniversityAnarIran
  2. 2.Department of Civil and Structural EngineeringUniversity Kebangsaan MalaysiaSelangorMalaysia
  3. 3.Department of Electrical Engineering, Anar BranchIslamic Azad UniversityAnarIran

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