Rock Burst Classification Prediction Method Based on Weight Inverse Analysis Cloud Model
Junchao Yu, Shangge Liu, Ling Gao · IOP Conference Series Earth and Environmental Science · 2019
Abstract A cloud model comprehensive evaluation method based on weight inverse analysis is proposed and applied to the prediction of rock burst grade. At first, the objective function with weight as independent variable is derived and established. The objective weight of each evaluation factor is obtained by genetic algorithm. Then the concrete steps of the coupling of weight inverse analysis and cloud model are given. After that, σθ/σc σc/σt and Wet are selected as the evaluation factors. σθ/σc is the ratio of the maximum tangential stress in the cavern to the compressive strength of rock. σc/σt is the ratio of tensile strength to the compressive strength of rock. Wet is the elastic energy index. According to the rock burst engineering example, the inverse analysis and calculation of the factor weights are carried out. Finally, this method is applied to the rock burst grade prediction of Jiangbian Hydropower Station and Maluping Mine. Compared with the cloud model prediction results of other weighting methods, its feasibility and effectiveness has been verified. The research shows that the cloud model for rock burst based on weighted inverse analysis is less subjective in the weighting process and the prediction effect is better.