Grey Model Based Particle Swarm Optimization Algorithm for Fatigue Strength Prognosis of Concrete

Qin Ming Liu; Ming Dong

doi:10.4028/www.scientific.net/AMR.148-149.420

Paper Titles

Simulation and Experimental Research of Adaptive Control with Constant Pressure on Large-Scale Titanium Alloy Composite Plates
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Fabrication of Cu-Al₂O₃ Composites by Simplified Internal Oxidation Process
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Grey Model Based Particle Swarm Optimization Algorithm for Fatigue Strength Prognosis of Concrete
p.420

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The Superplastic Deformation and Microstructural Analysis of Fine-Grained 1420 Al-Li Alloy
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 148-149Grey Model Based Particle Swarm Optimization...

Grey Model Based Particle Swarm Optimization Algorithm for Fatigue Strength Prognosis of Concrete

Abstract:

This paper explores the grey model based PSO (particle swarm optimization) algorithm for fatigue strength prognosis of concrete. First, depending on concrete’s testing status, fatigue life is studied. Then, one GM(1,1) based PSO algorithm is used in fatigue strength prognosis of concrete. One important advantage of the proposed algorithm is that only fewer data is in need for fatigue strength prognosis. Finally, a case study is given to illustrate effectiveness and efficiency of the proposed approach.

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Periodical:

Advanced Materials Research (Volumes 148-149)

Pages:

420-424

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.148-149.420

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Online since:

October 2010

Authors:

Qin Ming Liu, Ming Dong

Keywords:

Building Material, Fatigue Strength, Grey Model, Particle Swarm Optimization Algorithm (PSO)

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References

[1] K. Dai, G.Y. Qiu, S.R. Hu: Computer Engineering and Science. Vol. 21 (1999), p.10, in Chinese.

Google Scholar

[2] C.Y. Zhou, Y.L. Zhang, etc.: Pressure Vessels. Vol. 2 (2002), p.14, in Chinese.

Google Scholar

[3] J.L. Deng: The Base of Grey Theory (Press of Huazhong University of Science& Technology, Wuhan 2002), in Chinese.

Google Scholar

[4] J.L. Deng: Grey system theory Guide (Press of Central China Polytechnic University, Wuhan 1990) , in Chinese.

Google Scholar

[5] J.L. Deng: Grey Prediction and Grey Decision (Revised Edition) (Press of Huazhong University of Science &Technology, Wuhan 2002), in Chinese.

Google Scholar

[6] J. L Deng: The Journal of Grey System. Vol. 3 (2002), p.213, in Chinese.

Google Scholar

[7] D.R. Eberhart, Y.J. Kennedy: A new optimizer using particle swarm theory. Proceeding of sixth international symposium on micro machine and human science, USA: IEEE Service Center, (1995), p.39.

DOI: 10.1109/mhs.1995.494215

Google Scholar

[8] Y.J. Kennedy, D.R. Eberhart: Particle swarm optimization. Proceeding of IEEE Int'l Conference on Neural Networks, USA: IEEE Service Center, (1995), p. (1982).

Google Scholar

[9] J.S. Zhu, Y.P. Son: concrete. Vol. 6 (2002), p.10, in Chinese.

Google Scholar

[10] X.P. Shi: Journal of China Civil Engineering. Vol. 23 (1990), p.11, in Chinese.

Google Scholar

[11] T. Ray, K.W. Liew: Engineering Optimization. Vol. 342 (2002), p.141.

Google Scholar