Rockburst laboratory tests database — Application of data mining techniques

• A comprehensive presentation of the rockburst phenomenon and occurrences
• A database composed by data from 139 rockburst tests was gathered.
• Predictive models for the rockburst maximum stress (σRB) and rockburst risk index (IRB) were developed.
• An overview of the influence of the input parameters in the rockburst indexes is provided.

Rockburst is characterized by a violent explosion of a block causing a sudden rupture in the rock and is quite common in deep tunnels. It is critical to understand the phenomenon of rockburst, focusing on the patterns of occurrence so these events can be avoided and/or managed saving costs and possibly lives. The failure mechanism of rockburst needs to be better understood. Laboratory experiments are undergoing at the Laboratory for Geomechanics and Deep Underground Engineering (SKLGDUE) of Beijing and the system is described. A large number of rockburst tests were performed and their information collected, stored in a database and analyzed. Data Mining (DM) techniques were applied to the database in order to develop predictive models for the rockburst maximum stress (σRB) and rockburst risk index (IRB) that need the results of such tests to be determined. With the developed models it is possible to predict these parameters with high accuracy levels using data from the rock mass and specific project.