Rock burst prediction probability model based on case analysis

Abstract

Most of grading results obtained by the traditional rock burst prediction model are qualitatively expressed, the corresponding relationship between the rock burst prediction grade and occurrence probability has not been studied, and it is difficult to obtain the quantitative occurrence possibility of rock burst. For the above problems, a novel tunnel rock burst prediction probability model is proposed based on actual rock burst cases analysis. Firstly, six quantitatively characteristic parameters, namely the maximum tangential stress, the uniaxial compressive strength, the uniaxial tensile strength, the stress coefficient, the rock brittleness coefficient and the elastic energy index were extracted from rock burst cases, and the probability distribution function and correlation of those parameters were determined. Secondly, the multi-dimensional joint probability distribution function of six characteristic parameters was constructed under the framework of Copula theory, and then the Least Squares Support Vector Machine (LSSVM) which was optimized by particle swarm optimization algorithm served as the intelligent response surface model to reflect the nonlinear mapping relationship between six parameters and the tunnel rock burst prediction level. Subsequently, the Copula-LSSVM rock burst prediction probability model was established, and the Weibull distribution function of rock burst prediction grade was obtained with the application of the Monte Carlo simulation method. Lastly, six rock burst cases of the Jinping II Hydropower Station are used to demonstrate the effectiveness of the proposed method, and analysis was done on the influence of parameter uncertainty and model uncertainty on the prediction results. It is evidenced that the prediction probability obtained can be used for rock burst quantitative risk assessment of hard rock tunnels.