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Evolutionary Support Vector Machine and Its Application in Rock Body Slope

Author: LuYouZhong
Tutor: ZhengZuoShi
School: Ningxia University
Course: Solid Mechanics
Keywords: Support Vector Machine Slope Reliability Time Series Genetic Algorithms Displacement back analysis
CLC: TU457
Type: Master's thesis
Year: 2004
Downloads: 304
Quote: 2
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Abstract


Support vector machines (Support Vector Machine, SVM) based on Vapnik-Chervonenkis VC theory the creative machine learning method, the the Vapnik professor and his AT £ T Bell Labs collaborator. The basic idea is to transform the input space to a high-dimensional space through a non-linear transformation of the inner product function definition, in this high-dimensional space in search of a non-linear relationship between the input and output variables. The support vector machine has a good theoretical basis for using structural risk minimization principle, has good generalization capability; algorithm is a convex quadratic optimization problem, ensure that the solution found is a global optimal solution; solution the practical problems of the small sample, non-linear and high dimension and local minima problems, support vector machine is a hot research topic today. Rock and Soil Mechanics is a the both theoretical connotation conferred, and works very practical disciplines. After decades of research, the use of traditional scientific material mechanics, elasticity, elastic-plastic theory, proposed solving method, but due to the complexity of geotechnical media, non-linear, randomness, uncertainty and vagueness and other characteristics, the traditional method does not achieve satisfactory results. Support vector machine as a new general-purpose machine learning method has good adaptability to solve the problem is difficult to describe with mathematical models, and has broad application prospects. This article will support vector machine method is applied to the rock mass slope and displacement back analysis study, the main work is as follows: 1. For the safety assessment of geotechnical engineering problems, support vector machine is introduced into the geotechnical study to establish the reliability of estimates of the slope engineering support vector machine model; 2. For support vector machine parameters (including the kernel function and its parameters) to determine the problem, the proposed method of the evolution support vector machine, the method not only takes advantage of the genetic algorithm global optimization capability, and use of support vector machines in dealing with small samples. excellent characteristics of high dimension, nonlinear problems; 3. A the rock deformation behavior predicted evolutionary support vector machine (ESVM) method is proposed, and use the method to study the deformation behavior of the new beach landslide, to verify the feasibility of the method; 4. Rock slope nonlinear displacement time series and slide forecast by simple numerical example illustrates the feasibility of the method and its excellent performance; 5. The application of rock deformation behavior to predict the evolution of the support vector machine method proposed evolutionary support vector machine (ESVM) the displacement back the basic idea, and the displacement of the counter-analysis and forecasting. Identify the parameters to guide the construction and optimization design are of great significance.

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CLC: > Industrial Technology > Building Science > Soil mechanics,foundation engineering > Rock ( rock ) mechanics and rock testing > Rock stability analysis
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