A Kind of Prediction Based on SOM Neural Clustering and Combination Weighting Evaluation Method

Rockburst disasters seriously threaten the safety and production progress of construction workers. In order to improve the accuracy of rockburst tendency prediction and ensure the rationality of index weights and classification and identification, a SOM clustering-combined weighting VIKOR model is proposed to predict rockburst. Based on the comprehensive analysis of the conditions of rockburst, the samples are classified from three indicators: rock brittleness index, tangential stress index and elastic strain energy index. This method accurately classifies samples through a self-organizing feature mapping network, and calculates the weights of different indicators through a combination weighting method, and finally sorts the rockburst grades through a multi-criteria compromise solution sorting method. This method makes the multi-information fusion of rockburst prediction more objective and operability. Comparing the engineering examples, it is found that the simulation calculation of the VIKOR rockburst prediction method of SOM neural network clustering and combination weighting is basically consistent with the engineering examples.