Forecasting evolution of tunnel surrounding rock displacement by epsilon-SVR

Because the prediction accuracy of gray theory, GA and ANN algorithm is insufficiency for tunnel rock surrounding stability, the method of epsilon-support vector machines was applied to researching of evolution law for tunnel rock surrounding displacement; and in order to enhance the learning efficiency of epsilon-support vector machines and the capability of forecasting, the accelerated hybrid genetic algorithm (GA) is used for optimizing parameters of epsilon-support vector machines. Comparison the forecasting results of gray theory, GA and ANN and monitoring results for tunnel rock surrounding displacement, the results show the learning efficiency and prediction accuracy of epsilon-support vector machines is superior to gray theory, GA and ANN obviously.