# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 3 Technologies and Methods for Rockburst Research
### Theoretical Research
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Researchers (Year)**} & \multirow{2}{*}{**Index and/or Equations**} & \multicolumn{6}{c}{**Grade of Rockburst Proneness**} \\ \cline{3-6}  & & **None** & **Light** & **Medium** & **Heavy** & **Serious** \\ \hline [PERSON] et al. (1972) [67] & \((\sigma_{\theta}^{max}+\sigma_{L})/\sigma_{c}\) & \(\leq\)0.3 & 0.3–0.5 & 0.5–0.8 & \(>\)0.8 & – \\ [PERSON] (1974) [15] & \(\sigma_{\theta}^{max}/\sigma_{c}\) & \(\leq\)0.2 & 0.2–0.3 & 0.3–0.55 & \(>\)0.55 & – \\ [PERSON] et al. (1974) [69] & \(\sigma_{c}/\sigma_{l}\) & \(>\)10 & 10–5 & 5–2.5 & \(<\)2.5 & – \\ [PERSON] et al. (1974) & \(\sigma_{l}/\sigma_{l}\) & \(>\)0.6 & 0.66–0.33 & 0.33–0.16 & \(<\)0.16 & – \\ Hoek and Brown (1980) & \(\sigma_{\theta}^{max}/\sigma_{c}\) & & 0.34 & 0.42 & 0.56 & \(>\)0.7 \\ [PERSON] (1988) [32] & \(\sigma_{c}/\sigma_{l}\) & \(>\)14.5 & 14.5–5.5 & 5.5–2.5 & \(\leq\)2.5 & – \\ [PERSON] et al. (1999) & \(\sigma_{\theta}^{max}/\sigma_{c}\) & – & 0.3–0.5 & 0.5–0.7 & 0.7–0.9 & \(>\)0.9 \\ GB 50487-2008 (2008) [72] & \(\sigma_{c}/\sigma_{max}\) & – & \(>\)7 & 7–4 & 4–2 & \(<\)2 \\ [PERSON] et al. (2010) [73] & \((\sigma_{1}-\sigma_{3})/\sigma_{c}\) & \(<\)0.35 & 0.35–0.5 & 0.5–0.8 & 0.8–1 & \(>\)1 \\ [PERSON] et al. (2012) [19] & \((\sigma_{1}-\sigma_{3})/\sigma_{c}\) & \(<\)0.45 & 0.45–0.6 & 0.6–0.7 & \(>\)0.7 & – \\ [PERSON] et al. (2013) [74] & \(P_{\phi}=\sigma_{\theta}^{max}\kappa_{\phi}/\sigma_{l}\) & \(<\)1.7 & 1.7–3.3 & 3.3–9.7 & \(>\)9.7 & – \\ [PERSON] et al. (2015) [75] & \(I_{RB}=H/\sigma_{RB}\) & & \(<\)0.6 & 0.6–1.2 & 1.2–2.0 & \(>\)2.0 \\ [PERSON] et al. (2017) [41] & \(W_{\psi t}=E_{e}/E_{p}\) & \(<\)2.0 & 2.0–5.0 & 5.0–10.0 & \(>\)10.0 & – \\ [PERSON] et al. (2018) [70] & \(RPI=\sigma r_{m}/\sigma_{max},\sigma r_{m}=\) & & & & & \\ \(\sigma\prime_{3}+\sigma_{ci}[m_{h}(\sigma\prime_{3}/\sigma_{ci})+s]^{a}\) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Rockburst strength criteria [71]. stored and dissipated energy during disturbances. He developed an analytical approach to assess the risk of rockburst caused by tunneling. The energy model of strainburst is shown in Figure 3b, with \(\Delta\sigma\) and \(\Delta\)E illustrated as the differential uniaxial compressive strength and excess energy, respectively. The ultimate stored energy of rock under the 3D stress state is much greater than the energy required for static failure under a biaxial or uniaxial state after unloading. Thus, once unloaded, the excess energy mainly transfers to the kinetic energy of the rock mass. Rock in a 3D stress state has more stored energy than necessary for static failure after unloading. Once unloaded, the surplus energy turns into the kinetic energy of the rock mass. The energy theory is widely accepted but difficult to use in practice due to difficulties in establishing and quantifying stress and strain fields. Numerical simulations and energy indices are often used to overcome this challenge. However, these indices have limited significance in evaluating a geological formation's hazard amplification impacts [49]. The rockburst rigidity theory explains rockburst as a systematic stability problem [24; 80] and considers the relationship between the stiffness of the testing machine and the post-peak deformation of the sample. The occurrence of rockbursts in mines can be identified by comparing the stiffness of the pillar and surrounding rock to that of the testing machine and sample [24; 77; 81; 82; 83; 84]. The instability theory is more extensive than the strength and bursting liability theories, but its implementation is limited by difficulties in quantifying pillar and rock stiffness. Rockburst is a complex process that is difficult to describe with a single hypothesis, leading to a reliance on numerical simulations [85], physical model tests, and in-situ monitoring.