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Forecast Model of Phreatic Surface on Tailings Dam Based on
GM-GRNN Theory
Feiyue WANG
1,a
, Zhisheng Xu
1
，
b
and Longjun DONG
2,c
1.Institute of Disaster Prevention Science & Safety Technology, Central South University, Hunan
Changsha, 410004,China
2.College of Resources & Safety Engineering，Central South University，Hunan Changsha,
410083,China
awfyhn@163.com, bxuzhshe82@163.com, ccsudlj@163.com
Keywords: tailings dam; phreatic surface; GM-GRNN coupling model; forecasts
Abstract. 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.
1.Introduction
Various uncertainties effected dam safety, applicable and durable in design, construction, and
operational process of tailings dam , which are generally believed that these have four main aspects
including random, fuzzy, unascertained and the imperfection of information. The research methods
were probability and statistics, fuzzy mathematics, unascertained mathematics and gray system
theory[1-7].
Unascertained mathematics research in probability and statistics at an embryonic state, which
overcomes the weakness of probability and statistics, through the generation and development of
the data sequence of chaotic, finite, discrete to extract valuable information, establish the gray
system model, and then use it to analysis, forecasting, decision-making and planning, achieve the
goal of making correct description and effective control of the operating rules of the system[8]. As a
mathematical method to solve incomplete information systems, since the early eighties of last
century the gray system theory was founded by China Professor Deng Julong [9], which has been
more extensively and deeply application to social, economic, meteorological, medical, legal,
military and other fields.
In recent years, with the continuous development of science and technology the application of
neural network theory in engineering technology lets to a boom and made some achievements and
provides an effective method to solve practicalities engineering technology. The neural network is
composed by extensive number of interconnected neurons. The neural network can simulate the real
world of biological neural systems and made interacting reaction between the objects. Artificial
neural network to process information through the information samples training the neural network,
Applied Mechanics and Materials Online: 2010-12-06
ISSN: 1662-7482, Vols. 44-47, pp 3403-3407
doi:10.4028/www.scientific.net/AMM.44-47.3403
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