Research On Landslide Prediction And Controlling Based On Artificial Neural Networks

With rapid economic development and urbanization process, green area has been reducedyear after year. Especially some human activities such as slope cut and underground mining inhigh incidence area of slope geological disasters make slope rock mass less stable to a largeextent, so the possibility of slippage of slope rock mass along connecting shear failure surfacewill be larger. Finally the failure of slope will happen. Besides natural attribute, Slope also hassocial and economic attributes. It directly threatens people’s safety of life and wealth, andhurts development of national and regional economy. In order to implement disasteremergency management and reduce the loss caused by landslides, prediction and controlshould be in real time and accurate. Based on summarizing overseas and domestic researchstatus and development tendency of landslide prevention and control, evaluation of slopestability is discussed, then prediction and control methods of slope cumulative displacementare proposed in this dissertation.The first step is evaluation of slope stability. As over-simplification of parametersselection in traditional stability evaluation methods leads to subjective computational processof stability coefficient and reduces credibility of results, the evaluation method based onsupport vector machine (SVM for short) is proposed, considering the characteristics of smallsample, nonlinearity model and high-dimensional data in actual projects.6major factorsinfluencing slope stability are considered as discriminant factors of SVM. Radial basisfunction is selected as kernel function. Then cross validation method is used to determineoptimal penalty coefficient and variance. Compared with conventional methods, SVMapproach increases accuracy and speed of evaluation.It’s necessary to predict imminent deformation of unstable slope rock masses.Considering nonlinearity characteristic (determinacy and stochasticity coexist) presented inmechanical behavior and deformation tendency of slope, the prediction model based on BPneural network is proposed to describe non-linear relationship between input and output ofslope system. Modeling complicated mechanical system can be avoided by use of BP neuralnetwork. Genetic algorithm (GA) and simulated annealing (SA) algorithmare combined,which is genetic-simulated annealing (GSA) algorithm, to optimize network weights, so thatthe shortcomings of BP neural network such as slow rate of convergence and local minimumtrouble in later stage can be overcome. As an external factor, rainfall capacity has an effect on slope displacement. According todata in Chinese geological disaster database, the occurrences of most landslides are caused byrainfall. Currently the research on relationship between rainfall capacity and slopedisplacement is mainly based on static neural network or other static models. These staticmodels have trouble in reflecting dynamic characteristics of this relationship. The predictionof slope displacement can be treated as identification process of a nonlinear dynamic system.Elman recurrent neural network is used to build systematic prediction model, and initialnetwork weight values are optimized by genetic algorithm. This method is adaptive andflexible, it has high rate of convergence and accurate result.Finally the opinions of cybernetics are introduced to discuss control strategy of slope.Due to the complexity of slope system, it’s hard to obtain accurate mathematical model. Theusage of predictive control methods for slope system can avoid this trouble. Landslide thrustforce is selected as systems control variable according to principle of landslide dynamics, thenGA-Elman neural network is used to identify slope system. By rolling optimization andfeedback compensation, we can find time series of optimal control variables.This dissertation systematically discusses3main problems of slope system which arestability analysis, displacement prediction and displacement control. It provides theoreticaldirection for further slope system research.