Probabilistic classification prediction of rockburst intensity in a deep buried high geo-stress rock tunnel during engineering investigation

Rockburst is a dynamic process of a sudden and rapid release of elastic strain energy stored in hard rock mass during underground excavation. The occurrence of rockburst disaster during tunnel construction will cause serious consequences such as casualties, equipment damage and construction delay. With a large number of deep-buried long tunnels to be constructed in southwestern mountainous areas of China, the prediction of rockburst disaster is of great importance. In this paper, to fulfil the requirement of tunnel alignment and design during engineering investigation stage, on the premise of the availability of rockburst prediction indexes in this stage, the Bayesian network is used to reflect the relationship between rockburst intensity and various influencing factors. Based on 473 groups of rockburst cases, the naive Bayesian probability classification model is constructed to predict the rockburst intensity by using four prediction indexes—geo-stress, geological structure, surrounding rock grade and surrounding rock strength. The prediction accuracy of the model is found to be 84.47% using the 10-fold cross validation method. At the same time, this model is applied to the rockburst section of Paomashan No. 1 Tunnel of Ya’an—Yecheng Expressway. The results show that the prediction accuracy is 85.71% in the 28 tunnel section applications, and the established Bayesian network model has a good prediction performance. The proposed method can provide a good support to the rockburst prediction during the investigation of deep-buried long tunnels located in Southwest China.