樁基礎設計成本分析之粒子群演算法

陳柏維

doi:10.6846/TKU.2008.00916

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樁基礎設計成本分析之粒子群演算法

Cost Analysis of Pile Foundation Design with Particle Swarm Optimization Method

陳柏維 , Masters　　Advisor：張德文　　

繁體中文 DOI： 10.6846/TKU.2008.00916

粒子群演算法；樁基礎；最佳化； Particle Swarm Optimization ； Pile Foundation ； Optimization

Abstract │ Reference (37) │ Times Cited (1) │ Altmetrics

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

This study adopted Particle Swarm Optimization (PSO) in designing and evaluating all the cost from pile foundations. Its objection function included the limitation of standards and minimum cost of pile foundation. Initially, a particle would random to generate a position in given group, and then search the best fit solution in iteration method. In each iteration process, particles would renew its velocity and acceleration to estimate their next positions by tracing the individual and group best value. The cost of piles would involve excavation expense, pile cap and pile construction expense, land backfill expense. The parameters of optimization system included pile spacing, the diameter of pile, pile length, pile cap thickness, number of piles. The subjection function would involve the spacing width, pile displacement, bending moment, shear force, bearing capacity, pull-out force, negative frictional force and settlement. Based on the above procedure, the preliminary study is prior to discuss parametric relationship and sensitivity about parameters. Finally, this study would validate with the practical engineering cases to show its reliability and accuracy.
    The conclusions were drawn as follows: (1). For the low dimension problem, weight factors would lie in 0.4 to 0.6 and numbers of particle are set up about 20 to40. On the other hand, for the high dimension problem, weight factors would lie in 0.6 to 0.9 and numbers of particle are set up about 40 to60.The learning factors would not almost affect solutions and could be set to 2 (2) The speed law of Particle Swarm Optimization uses inertia weight types better than compression ones. (3) Case studies would show pile diameters and numbers of piles would govern the results of cost minimization. Generally, the program would complete total analyses effectively about 15 to 30 minute, which was prior to traditional methods. (4) The study might design failure due to construction period and small dimension piles.

Reference ( 37 ) 〈TOP〉

1. Eberhart, R.C. and Shi. Y. (1998). Comparison between genetic algorithms and particle swarm optimization. 1998 Annual Conference on Evolutionary Programming, San Diego.
連結：
3. Tony Huang (2005), “A Hybrid Boundary Condition for Robust Particle Swarm Optimization”, IEEE Antennas and Wireless propagation Letters, Vol. 4, pp. 112-117.
連結：
4. S. Ayed, A. Imtiaz, and A. M. Sabah, “Particle Swarm Optimization for Assignment Problem,” ELSEVIER Microprocessors and Microsystems, Vol.26, No.1, pp.363-371, January, 2002
連結：
6. 鐘明劍(2006)，「樁基礎最佳化設計之研究」，中央大學土木工程學系碩士論文。
連結：
23. 鄧永亟(2004)，「利用PSO演算法探討高速銑削最佳化」，大同大學機械工程研究所碩士論文。
連結：

Times Cited (1) 〈TOP〉

王興正(2012)。應用粒子群演算法於無人飛行載具結構系統之最佳化設計。淡江大學航空太空工程學系碩士班學位論文。2012。1-59。

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