Prediction of rock burst classification using the technique of cloud models with attribution weight

Rock burst is one of the common failures in hard rock mining and civil construction. This study focuses on the prediction of rock burst classification with case instances using cloud models and attribution weight. First, cloud models are introduced briefly related to the rock burst classification problem. Then, the attribution weight method is presented to quantify the contribution of each rock burst indicator for classification. The approach is implemented to predict the classes of rock burst intensity for the 164 rock burst instances collected. The clustering figures are generated by cloud models for each rock burst class. The computed weight values of the indicators show that the stress ratio is the most vulnerable parameter and the elastic strain energy storage index W (et) and the brittleness factor take the second and third place, respectively, contributing to the rock burst classification. Besides, the predictive performance of the strategy introduced in this study is compared with that of some empirical methods, the regression analysis, the neural networks and support vector machines. The results turn out that cloud models perform better than the empirical methods and regression analysis and have superior generalization ability than the neural networks in modelling the rock burst cases. Hence, cloud models are feasible and applicable for prediction of rock burst classification. Finally, different models with varying indicators are investigated to validate the parameter sensitivity results obtained by cloud clustering analysis and regression analysis in context to rock burst classification.