# A Decision Tree for Rockburst Conditions Prediction
## 1 Introduction  
Underground activities, such as mining, railway, and road constructions, are complex geotechnical works. These could be attributed to the limited understanding of the subsurface, which makes it difficult for underground constructions, due to the variability of geology. Even though more effort has been made to perfect underground constructions, where underground operations at 2000 m below the surface have been common, several uncertainties do pop up occasionally, which can lead to the wastage of resources (time, money, and properties) and, sometimes, loss of life. One such uncertainty is the instability of rock mass, as stated by [PERSON] et al. and [PERSON] et al. [1, 2]. [PERSON] et al. [3] and [PERSON] et al. [4] explained that the instability of a rock mass in deep excavations depends on the inherent properties of the rock mass, such as the type of rock, its strength and brittleness, and external conditions, such as the magnitude of in situ stresses, geological structures, dynamic perturbations, and excavation sequences during underground operations. Consequently, based on understanding the above factors and how they influence rock mass instability, the magnitude of in situ stresses and the rock mass quality have been of more impact [1, 5]. Accordingly, rockburst, which is a dynamic phenomenon, is considered a type of rock mass failure around deep excavations of hard and brittle rocks and in a high-stress environment [1, 3, 6, 7]. Rockburst occurs as a result of overstressing the rock mass or intact brittle rock, when the stresses exceed the compressive strength of the material [1, 8, 9, 10, 11, 12]. Rockburst can also be defined as sudden and intense movement, accompanied by rock failure, in underground spaces under high-stress conditions [1, 13, 14, 15, 16]. Given the fact thatrockburst is an unexpected mechanism, it poses more problems in terms of vulnerability. These are the loss of lives of mining personnel, destruction of mining equipment, and, in some cases, financial loss and time loss. Therefore, it is a must to understand this mechanism and predict how it is triggered to prevent these vulnerabilities from occurring. Owing to its complexity, rockburst can be classified into various types, based on its intensity (rockburst condition), seismicity, the shape of the ejected rock, and others. Rockburst classification is divided into three groups: first, the type of rockburst (features of failure plane observed in underground excavation), rockburst intensity, and the mechanism of the rockburst and evaluation of seismic events [1; 8; 17]. The present paper uses a rockburst intensity, or rockburst conditions (RBC), grading system, with four predefined classes based on rock displacement, damage, and failure characteristics, as shown in Table 1[18; 19; 20; 21]. Rockburst mechanisms and their predictions have been under serious research over the years, and have achieved thoughtful and profound results, as shown by the works produced by several authors. Among the rockburst forecasting methods, including laboratory, numerical, analytical, and empirical, the empirical approach is the most commonly used, due to its low cost, fast procedure, and simplicity [22]. Recent studies focusing on Machine Leaning (ML) algorithms are reviewed in Table 2, and results suggest that different methods have varying performances, and some methods have shown better accuracy in predicting rockburst occurrence than others [23]. Therefore, improving the accuracy of rockburst prediction is essential for mitigating risks and enhancing mining safety during the preliminary design phase [24]. These models have successfully operated using sets of input and output data from historical rockburst cases, affirming their capability in this context [24].