Abstract：In order to predict the rockburst tendency level more accurately，shear stress of surrounding rock，ratio of shear stress of surrounding rock and compressive strength，brittleness index and elastic deformation energy index are selected as the prediction indexes of rockburst tendency level according to the cause and characteristics of rockburst，and the improved CRITIC algorithm is used to weight the indicator samples. A new machine learning algorithm named as XGBoost is introduced to perform computational training on the samples，and a CRITIC-XGB model of rockburst tendency level prediction is established. The model is used to predict the rockburst tendency level of the collected rockburst instances，and the prediction results are compared with those by XGBoost，random forest(RF) and support vector machine(SVM) algorithms. The research results show that the convergence performance of the CRITIC-XGB prediction model is significantly improved compared to the single XGBoost model and that the CRITIC-XGB prediction model has higher prediction accuracy than the single XGBoost，RF and SVM algorithms. The developed model provides a new and reliable method for the prediction of rockburst tendency grades.
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2020, Vol. 39 
