Abstract
The evaluation of the risk is the prerequisite for the implementation of countermeasures in the prevention and control of rock burst, and the research on the fast forecast at scene of the rock burst is more important for the safety production of coal mine. Aiming at the problem that dynamic disasters caused by many factors and heterogeneity of coal and rock are difficult to predict in the process of coal mining, in this paper, the general law and the risk control factors of the rock burst are studied, a mathematical model based on the BP neural network was built according to the different actual mining conditions in the mining area, and the output layer has obtained the prediction result. Then, the results of the output samples after training were fitted by using SPSS software, and the fitting function was obtained by multiple least square fitting. Finally, the fitting results are checked by the data of actual coal mine dynamic disaster parameters. The prediction results show that the simulation results of BP neural network prediction model and the fitting function of the least square method can reduce the impact of subjective judgment on the prediction results, and the application of the fitting function can obtain the prediction results in the first time to ensure the construction safety. The method of on-site hazard assessment and inspection by using fitting function is simple and feasible and has high accuracy, which provides a new idea for the field prediction of rock burst.
Highlights
Rock burst is one of the common dynamic disasters in coal mine production and becoming more and more serious with the increase of mining depth and intensity year by year [1]
The rock burst is a quite complicated dynamic phenomenon, and it has numerous influence factors; it is the result of comprehensive action of factors such as ground stress and physical and mechanical properties of coal, and the influence factors are highly fuzzy and nonlinear
As a method of nonlinear approximation ability based on black box theory, a neural network has unique advantages in the information mapping party. It can capture the relevant laws between the influence factors and the outstanding events in the dynamic disaster data and combine the qualitative and quantitative
Summary
Rock burst is one of the common dynamic disasters in coal mine production and becoming more and more serious with the increase of mining depth and intensity year by year [1]. As a method of nonlinear approximation ability based on black box theory, a neural network has unique advantages in the information mapping party It can capture the relevant laws between the influence factors and the outstanding events in the dynamic disaster data and combine the qualitative and quantitative. The analysis process of neural network operation is complicated, and it is generally required to be programmed by a computer It is difficult for the frontline workers to get the first hand information about the prediction results of rock burst, and in situ accurate and efficient prediction of rock burst needs further study. They mutually verify and effectively improve the accuracy of prediction and explore new ideas for the field prediction and prevention of rock burst
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