Research on Prediction of Rating of Rockburst Based on BP Neural Network

With the development of economic construction, underground space development continues to move towards the deep. "More, long, big, deep," will be the general trend of the development of underground engineering in the 21st century. Rock burst is a kind of sudden geological disasters with a higher frequency in deep tunnel construction. Rock burst prediction has very important significance for the construction of underground engineering in highland stress area. This paper described the mechanism of rockburst. The researchers systematically analyzed relevant factors of rockburst. In this paper, the principle and application of Back-Propagation (BP) neural network were introduced, and to improve the algorithm of neural network, the NNT prediction model was set up. The author have taken the seven parameters including (as input values): Index of brittleness, Ratio of Strength stress, Ratio of maximum stress to minimum stress, Depth of engineering, Completeness of rockmass, Structural strength, Depth of pit for rock burst. The results of rockburst also proved the prediction model has high accuracy and stability, indicating that the model has a good prospect in the rock burst forecasting.