Abstract
The formation mechanism of rockburst is complex, and its prediction has always been a difficult problem in engineering. According to the tunnel engineering data, a three-dimensional discrete element numerical model is established to analyze the initial stress characteristics of the tunnel. A neural network model for rockburst prediction is established. Uniaxial compressive strength, uniaxial tensile strength, maximum principal stress, and rock elastic energy are selected as input parameters for rockburst prediction. Training through existing data. The neural network model shows that the rockburst risk is closely related to the maximum principal stress. Based on the division of rockburst risk areas, according to different rockburst levels, the corresponding treatment methods are put forward to avoid the occurrence of rockburst disaster. Based on the field measured data and test data, combined with the existing rockburst situation, numerical simulation and neural network method are used to predict the rock burst classification, which is of great significance for the early and late construction safety of the tunnel.
Highlights
IntroductionDeep tunnel rockburst has great harm, and the mechanism and law of rockburst formation have not been fully revealed, which leads to the difficulty of comprehensive, accurate, and complete rockburst prediction and prevention methods. e academic theories related to its formation mainly include strength theory, impact tendency theory, stiffness theory, energy theory, and three-criterion instability theory
Deep tunnel rockburst has great harm, and the mechanism and law of rockburst formation have not been fully revealed, which leads to the difficulty of comprehensive, accurate, and complete rockburst prediction and prevention methods. e academic theories related to its formation mainly include strength theory, impact tendency theory, stiffness theory, energy theory, and three-criterion instability theory.For example, in Feng et al.’s work [1], in the field of coal mining, based on the elastic thin plate theoretical model, the roof deformation deflection equation and surrounding rock deformation energy equation are derived
In the above formula, σk is the measured in situ stress value of the measuring point, and k is the mark of the measuring point. b0 is the free term and bi is the multiple regression coefficient. n is the number of working conditions
Summary
Deep tunnel rockburst has great harm, and the mechanism and law of rockburst formation have not been fully revealed, which leads to the difficulty of comprehensive, accurate, and complete rockburst prediction and prevention methods. e academic theories related to its formation mainly include strength theory, impact tendency theory, stiffness theory, energy theory, and three-criterion instability theory. In Feng et al.’s work [1], in the field of coal mining, based on the elastic thin plate theoretical model, the roof deformation deflection equation and surrounding rock deformation energy equation are derived. Li et al [2] established an elastic-plastic brittle catastrophe rockburst model of rock with structural plane and studied the relationship between energy accumulation and dissipation in the process of dynamic fracture of coal and rock. In terms of rockburst prediction method and rock mechanics research method, Ji et al [12] studied the Advances in Civil Engineering contribution of multiple MS data (including MS original wave data and MS energy data) to rockburst by using the method of combining support vector machine (SVM) and genetic algorithm (GA). Through the neural network training model, the characteristics of the target rockburst are analyzed, and the preventive measures are put forward. e research can provide an important basis for the division of rockburst area and subsection control
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