Аннотация
ABSTRACT: Rockburst is a mining-induced seismic event characterized by a sudden explosion of rock due to the release of strain energy stored in rock mass, often occurring in high geo-stress and other unfavorable geological conditions. The prediction of the rockburst damage potential (RDP) is essential in mining as this phenomenon causes damage to excavations, leading to dire consequences that include economical losses, injuries, or casualties of miners. Despite the existence of a large number of rockburst predictive models and other empirical tools, a reliable prediction of RDP still remains challenging. Hence, the aim of this paper is to revisit some existing studies and propose practical RDP charts based on basic machine learning algorithms such as artificial neural network classifier. Historical records of rockburst compiled from Australian mines were employed for this purpose. Overall, the results of this study showed good consistency with the field data and outperformed those of some existing studies. It is concluded that the proposed charts could be used for the excavation vulnerability assessment and, therefore, assist in managing ground prone to rockburst in seismically active mines. 1. INTRODUCTION Rockburst is commonly defined as a rock explosion that occurs primarily in high geo-stress conditions and other unfavorable geological conditions in underground excavations due to the sudden release of strain energy stored in rock mass (Xue et al., 2020). In underground mines, it occurs in the form of mining-induced seismic events. The consequences of this phenomenon have the potential to be extremely destructive causing significant damage to equipment and underground structures as well as injuries and fatalities among mine workers. As early as in 1738, when it was first mentioned in a tin mine in Great Britain (Zhou et al., 2012), rockburst has been associated with numerous accidents and casualties throughout the mining industry around the world. Rockburst was the main reason of the mine disaster in South Africa, where 73 employees died during underground gold extraction in 1975 (Cai, 2016). Many similar cases have been recorded in China, with an increase of cases every year as mines go deeper (Pu et al., 2019). These few examples illustrate the devastating consequences of rockburst and the urgent need to study ways of predicting or controlling its severity.