# Development of Rockburst Research: A Comprehensive Review
## 3 Technologies and Methods for Rockburst Research
### Numerical Simulation Techniques  
The theoretical basis of the numerical simulation of rockburst mainly includes: the finite element method (FEM) [37], the extended finite element method (XFEM) [38], the boundary element method (BEM) [39], the finite difference method (FDM) [40], the discrete element method (DEM) [41], general particle dynamics (GPD) [42], discontinuous deformation analysis (DDA) [43], the numerical manifold method (NMM) [44] and the particle manifold method (PMM) [45]. NMM is a coupling method of FEM and DDA, which is used to solve the problems of continuous deformation and discontinuous deformation. PMM  
Figure 3: Rockburst types of classification: (a) rockburst damage mechanism, damage severity and required support functions (from [30]); (b) six geomechanical types of rockburst adapted from [28].  
is another coupling method proposed by introducing the concept of particles into NMM. For instance, a realistic full 3D finite element model was used to evaluate the distribution and accumulation of strain energy in kimberlite to predict potential rockburst induced by mining [46]. The mechanical behavior of rock samples was simulated by DEM to observe the basic mechanism of rock deformation and failure [47]. The Johnson-Holmquist-Beissel (JHB) model was introduced into particle-based numerical manifold method (PNMM) to capture more mechanical responses on the micro scale [48]. Since DEM can obviously reflect a continuous nonlinear stress-strain relationship, yield strength and process of post peak strain softening or hardening, it is strongly applicable to studying mechanical properties of heterogeneous materials of rock mass.  
Numerical simulation can also be used to dynamically evaluate rockburst risk. The focus is to establish a quantitative mapping relationship with rockburst characteristic parameters with evaluation indicators. The commonly used evaluation indices can be divided into numerical indicators based on the strength theory or the energy theory (See Table 3).  
\begin{table}
\begin{tabular}{c c c} \hline
**Type** & **Evaluating Indicator** & **Characteristics of Indicator** \\ \hline Indicators based on the strength theory & Excess shear stress (ESS) & • • Possibility of rockburst controlled by rock mass structural stress can be obtained [20]. \\ \cline{3-3}  & Failure approach index (FAI) & • Failure depth, section distribution and position of rock mass can be obtained [49]. \\ \cline{3-3}  & Energy release rate (ERR) & • Energy released from rock mass under different excavation sequences and sizes can be obtained [31]. \\ \cline{3-3}  & Energy storage rate (ESR) & • Energy evolution process before and after rock failure can be characterized [50]. \\ \cline{3-3}  & Burst potential index (BPI) & • Possibility of rockburst when energy storage rate reaches the maximum energy storage limit can be presented [51]. \\ \cline{3-3}  & Local energy release density (LERD) & • Effective kinetic energy released from surrounding rock system before and after pillar failure can be characterized [52]. \\ \cline{3-3}  & Modelled Ground Work (MGW) & • Energy change of rock mass before and after failure can be characterized [53]. \\ \cline{3-3}  & Local energy release rate (LERR) & • Energy release value and evolution law on the section before and after rock mass excavation can be obtained [54]. \\ \cline{3-3}  & Relative energy release index (RERI) & • Energy evolution of the instability process can be tracked, and influence of deformation characteristics on failure is implicitly considered [55]. \\ \hline \end{tabular}
\end{table}
Table 3: Common numerical calculation indicators.  
Numerical simulation can reflect actual situation to a certain extent, which is a good auxiliary research method. However, it is not simple to establish a realistic model and set reasonable initial conditions and parameters to make simulation results more realistic.