Application of New Kernel Function of SVM Prediction Model for Rock Slope Stability
The influencing factors leading to rock mass failure are complex and diverse,which are geometric factors and physical factors.At present,there are many studies on the physical parameter choice of homogeneous rock slope,but the analysis on the influencing factors of the improved binary rock slope model is still in its infancy.It is necessary to find an analysis method that can solve the nonlinear relationship among the factors.Based on the second line of slope calculation model,the support vector machine(SVM)method is introduced to solve the high-dimensional nonlinear problem.A new synthetic kernel function is proposed by sensitivity analysis of important parameters to prove the feasibility of this method in the predictive analysis on the stability of rock slope.By analyzing the sensitivity of each factor,it can be known that the prediction model established by RBF kernel function for the geometric factors of slope has higher accuracy,but the applicability of Sigmoid kernel function is poor.And for the physical mechanical factors of slope,the prediction model established by Linear kernel function has higher accuracy,but the applicability of Polynomial kernel function and Sigmoid kernel function is poor.A new synthetic kernel function is obtained by the combination of kernel function matrix,and the prediction effect is compared with 4 conventional kernel functions.The result shows that prediction accuracy of the rock slope stability given by the new synthetic kernel function is the highest,the absolute error does not exceed 0.010 3,and the relative error is less than 2.83％.The research conclusion can provide a new idea for the stability analysis of rock slope.
rock slopesafety factorprediction modelkernel functionsensitivity analysis
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