Parameter Determination of Discrete Element Model of Conditioned Soil by Genetic Neural Network

Li Wu; Tian Min Guan; Fu Zheng Qu; Shou Ju Li

doi:10.4028/www.scientific.net/AMR.150-151.27

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 150-151Parameter Determination of Discrete Element Model...

Parameter Determination of Discrete Element Model of Conditioned Soil by Genetic Neural Network

Abstract:

The inversion method combining the genetic neural network and the discrete element simulation of triaxial tests is firstly described for determining the discrete element model parameters of the conditioned soil. The purpose is to make the error of the simulation curves and the laboratory curves of the triaxial test minimum. The solve approach is the parameters identification based on the genetic neural network. The network training sample is provided by the discrete element simulation. The input sample is the simulation curves of triaxial test, and the output sample is the model parameters. The laboratory triaxial test curves of the conditioned soil are used to determine its model parameters. The simulation curves calculated with the inversed parameters match the laboratory curves well, which illustrate that the discrete element model can accurately predict the deformation characteristics and flow patterns of conditioned soils.

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Periodical:

Advanced Materials Research (Volumes 150-151)

Pages:

27-31

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.150-151.27

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Online since:

October 2010

Authors:

Li Wu, Tian Min Guan, Fu Zheng Qu, Shou Ju Li

Keywords:

Conditioned Soil, Discrete Element, Genetic Neural Network, Parameters Inversion, Triaxial Test

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References

[1] C. J. Coetzee and D. N. J. Els: Computers and Eelectronics in Agriculture vol. 65 No. 2 (2009), pp.198-212.

Google Scholar

[2] Y. C. Liang, D. P. Feng and G. R. Liu: Computers and Structures vol. 81 No. 24-25 (2003), pp.2373-2382.

Google Scholar

[3] C. Wang, G. W. MA and J. Zhao: International Journal of Rock Mechanics and Mining Sciences vol. 41 No. 3 (2004), pp.453-457.

Google Scholar

[4] C. J. Cloetzee, D. N. J. Els and G. F. Dymond: Journal of Terramechanics vol. 47 No. 1 (2010), pp.33-44.

Google Scholar

[5] J. M. M. Manuel and E. M. R. Luis: Geotechnical and Geoenvironmental Engineering vol. 131 No. 10 (2005), pp.1234-1242.

Google Scholar

[6] Z. Asaf, D. Rubinstein and I. Shmulevich: Soil and Tillage Research vol. 92 No. 1-2 (2007), pp.227-242.

DOI: 10.1016/j.still.2006.03.006

Google Scholar

[7] A. Nardin, B. Schrefler and M. Lefik: Lecture Notes in Computer Science vol. 2718 (2003), pp.545-554.

Google Scholar

[8] S. Quebaud, M. Sibai and J. P. Henry: Tunnelling and Underground Space Technology vol. 13 No. 2 (1998), pp.173-180.

DOI: 10.1016/s0886-7798(98)00045-5

Google Scholar