Prediction method of rockburst intensity grade based on SOFM neural network model
YANG Xiaobin1，PEI Yanyu1，CHENG Hongming1，2，HOU Xin1，LV Jiaqi1
(1. School of Emergency Management and Safety Engineering，China University of Mining and Technology(Beijing)，Beijing 100083，China；2. School of Coal Engineering，Datong University，Datong，Shanxi 037003，China)
Abstract：In order to simplify the rockburst intensity grade prediction index system，solve the problem of fuzzy classification of prediction，and help engineers to analyze the prediction results，a rockburst intensity grade prediction model based on SOFM neural network was established. The prediction model was expanded into three models according to the different topology of the competition layer. Taking the maximum tangential stress，the uniaxial compressive strength and the uniaxial tensile strength of the rock as the model input vectors，40 sets of rockburst engineering data at home and abroad were used as data sets to input three models for training and testing. The results of the testing indicated that the prediction accuracy of the three models was up to 90%. Through comparing the clustering，testing and training effects of the three models，the prediction model with 16 neurons in the competition layer was the best. Compared the prediction results of best prediction model with that of extension theory，Russenes criteria and cloud model based on rough set of FCM algorithm，the prediction method of rockburst intensity grade based on SOFM neural network was superior to other methods. It shows that the method proposed in this paper is feasible and practical，which provides a new method and means for rockburst prediction.
2021, Vol. 40 
