Groutability analysis and prediction model for grouting of rock mass based on SVM

Abstract

There are many factors that affect the groutability of rock mass, especially the accuracy of judging the groutability of rock mass with the empirical formula is low for the hidden rock mass fracture. Aiming at the above problems, the support vector machine method is used to establish the prediction model of rock mass groutability analysis in order to realize the fast and accurate analysis of rock mass groutability. Based on the analysis of the influencing factors and the small sample characteristics of the grouting data set, the regression prediction SVR model and classification prediction SVM model of rock mass groutability were established respectively. Further, by improving the gray Wolf optimization algorithm and enhancing the whale optimization algorithm, the penalty factor C and kernel function parameter g of the prediction model based on support vector machine are optimized. The result show that compared with other prediction models, the classification prediction accuracy of the groutability prediction model proposed in this paper is improved by about 6.5%, and it has the obvious advantage of fast convergence, which verifies the accuracy and effectiveness of the prediction model of rock mass groutability analysis based on support vector machine.

Keywords

groutability prediction / support vector machine / cement take prediction / swarm intelligent algorithm / rock grouting