CLC number: TU470; TU17
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2011-05-24
Cited: 25
Clicked: 7646
Mohammad Khajehzadeh, Mohd Raihan Taha, Ahmed El-Shafie, Mahdiyeh Eslami. Modified particle swarm optimization for optimum design of spread footing and retaining wall[J]. Journal of Zhejiang University Science A, 2011, 12(6): 415-427.
@article{title="Modified particle swarm optimization for optimum design of spread footing and retaining wall",
author="Mohammad Khajehzadeh, Mohd Raihan Taha, Ahmed El-Shafie, Mahdiyeh Eslami",
journal="Journal of Zhejiang University Science A",
volume="12",
number="6",
pages="415-427",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1000252"
}
%0 Journal Article
%T Modified particle swarm optimization for optimum design of spread footing and retaining wall
%A Mohammad Khajehzadeh
%A Mohd Raihan Taha
%A Ahmed El-Shafie
%A Mahdiyeh Eslami
%J Journal of Zhejiang University SCIENCE A
%V 12
%N 6
%P 415-427
%@ 1673-565X
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1000252
TY - JOUR
T1 - Modified particle swarm optimization for optimum design of spread footing and retaining wall
A1 - Mohammad Khajehzadeh
A1 - Mohd Raihan Taha
A1 - Ahmed El-Shafie
A1 - Mahdiyeh Eslami
J0 - Journal of Zhejiang University Science A
VL - 12
IS - 6
SP - 415
EP - 427
%@ 1673-565X
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1000252
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.
[1]ACI 318-05, 2005. Building Code Requirements for Structural Concrete and Commentary. American Concrete Institute, Farmington Hills, MI, USA.
[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.
[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.
[4]Bowles, J., 1982. Foundation Analysis and Design. McGraw-Hill, New York, USA.
[5]Budhu, M., 2006. Soil Mechanics and Foundations. John Wiley & Sons, New York, USA.
[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.
[7]He, Q.Y., Han, C.J., 2006. An improved particle swarm optimization algorithm with disturbance term. Computational Intelligence and Bioinformatics, 4115:100-108.
[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.
[9]Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization. IEEE International Conference on Neural Networks, Perth, Australia. IEEE Service Center, Piscataway, p.1942-1948.
[10]Kvam, P.H., Vidakovic, B., 2007. Nonparametric Statistics with Applications to Science and Engineering. John Wiley & Sons, New York, USA.
[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.
[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.
[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.
[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.
[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.
[16]Saribas, A., Erbatur, F., 1996. Optimization and sensitivity of retaining structures. Journal of Geotechnical Engineering, 122(8):649-656.
[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.
[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.
[19]Wang, Y., 2009. Reliability-based economic design optimization of spread foundations. Journal of Geotechnical and Geoenvironmental Engineering, 135(7):954-959.
[20]Wang, Y., Kulhawy, F.H., 2008. Economic design optimization of foundations. Journal of Geotechnical and Geoenvironmental Engineering, 134(8):1097-1105.
[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.
[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.
[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.
Open peer comments: Debate/Discuss/Question/Opinion
<1>