Modified particle swarm optimization for optimum design of spread footing and retaining wall
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 wordsParticle swarm optimization (PSO) Spread footing Retaining wall Sensitivity analysis
CLC numberTU470 TU17
Unable to display preview. Download preview PDF.
- ACI 318-05, 2005. Building Code Requirements for Structural Concrete and Commentary. American Concrete Institute, Farmington Hills, MI, USA.Google Scholar
- 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
- Bowles, J., 1982. Foundation Analysis and Design. McGraw-Hill, New York, USA.Google Scholar
- Budhu, M., 2006. Soil Mechanics and Foundations. John Wiley & Sons, New York, USA.Google Scholar
- 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
- 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
- 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
- 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
- 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