Forecast Model of Phreatic Surface on Tailings Dam Based on GM-GRNN Theory

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Due to the extremely complicated seepage boundary conditions of tailing dam, the calculation results adopting two-dimensional simplified theory may greatly different from the measured results. It is urgent need of an accurate calculation method to forecast phreatic surface. In-depth analysis of factors affecting tailings dam phreatic surface, phreatic surface prediction model based on GRNN and GM (1,1) was established. A tailing dam engineering is tested using this model. It shows that the model uses the advantages of "accumulative generation" of a Gray prediction method, which weakens the original sequence of random disturbance factors, and increases the regularity of data. It also makes full advantage of the GRNN approximation performance, which has a fast solving speed, describes the nonlinear relationship easily, and avoids the defects of Gray theory.

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3403-3407

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December 2010

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] Mingjian Hu, Aiguo Guo, Shouyi Chen: Rock and Soil Mechanics Vol. 25(2004), pp.769-773.

Google Scholar

[2] Guangzhi Yin, Guo Yu, Dongming Zhang: Journal of Chongqing University (Natural Science Edition) Vol. 28(2005), p.81-83.

Google Scholar

[3] Meili Lu, Li Cui: Rock and Soil Mechanics Vol. 27(2006) , pp.1176-1180.

Google Scholar

[4] Baoyu Su, Jian Zhao, Zhutian Zhang, et al. Application of Positive Position Model in the Electronic Simulation Test of the Spacial Infiltration Flow Field of Tailings Dams. Metal Mine, 1994, (3): 27-29.

Google Scholar

[5] S.G. Vick.: Planning, Design and Analysis of Tailings Dams(BiTechPiblishers. Ltd, 1990).

Google Scholar

[6] Sarsby, R.: Environmental Geotechnics. ISBN- 0 7277 27524 (2000).

Google Scholar

[7] K. J. Witt., M. Schönhardt., (Eds., 2004): Tailings Management Facilities – Risks and Reliability. Report of the European RTD project.

Google Scholar

[8] Longjun Dong, Feiyue Wang. The Chinese Journal of Geological Hazard and Control,Vol. 18(2007), p.74–78.

Google Scholar

[9] Julong Deng: Wuhan: Huazhong University of Science and Technology Press,(2002).

Google Scholar

[10] D F Specht: A General Regression Neural Network. IEEE Transactions on Neural Networks, 1991, 2(6): 568~576.

DOI: 10.1109/72.97934

Google Scholar