关键词:  岩石力学, 岩爆, 烈度分级, 预测, 贝叶斯模型

Abstract: In view of many uncertain elements in classification and prediction of rockburst intensity，the evaluation model for classification and prediction of rockburst intensity is established by Bayes discriminant analysis.According to the characteristic and cause of rockburst，three factors of the stress coefficient of rock，the brittleness coefficient of rock   and the elastic energy index of rock   are selected as the discriminant factors.And the intensity of rockburst is divided into 4 grades，denoted as none，inferior，medium and intense，which are taken as the four normal populations in Bayes discriminant analysis.The classification discrimination is carried out based on the initial data for rockburst intensity analysis of 14 rock projects around the world.Bayes linear discriminant function is obtained through the training samples，which is then used to calculate the Bayes function value of the evaluating samples.Meanwhile，the population corresponding to the maximum function value is used to judge which population the evaluating sample belongs to.In order to verify the effectiveness and feasibility of the model，the cases of Qinling Tunnel and Fankou Lead-Zinc Mine are analyzed using the proposed method，and the obtained results are also compared with the results of neural network model，fuzzy probability model and practical conditions.The results show that the results predicted by Bayes model are both in good agreement with the practical conditions and the results obtained from the neural network model and fuzzy probability model.

Key words: Rock mechanics, Rockburst, Classification of intensity, Prediction, Bayes model