Rock burst prediction based on SOM neural network clustering and grayscale TOPSIS

With the continuous reduction of resources, the depth of mining continues to increase, and the probability of rock burst increases.In order to accurately predict the level of rock burst, a clustering-correlation TOPSIS model for rock burst prediction is proposed.On the basis of comprehensive analysis of rock burst generation conditions, the samples are classified from three indexes:stress, lithology and energy, and the grey correlation method and TOPSIS evaluation method are combined.The method can accurately classify the samples through the self-organizing feature mapping network, and at the same time quantify the importance of different indicators through the gray correlation degree.Finally, the blasting level is judged by the TOPSIS evaluation method.This method makes the prediction result more objective and accurate and operable through multi-information fusion method.Comparing with engineering examples, it is found that the simulation calculation of SOM neural network clustering-correlation TOPSIS rock burst prediction method is basically the same as that of engineering examples.