Research Article
Prediction and Evaluation of Rockburst Based on Depth Neural Network
Table 2
Rockburst sample data of typical hard rock tunnel.
| | Project name | Maximum principal stress (MPa) | Uniaxial compressive strength of rock (MPa) | Uniaxial tensile strength of rock (MPa) | Elastic energy index | Rockburst grade |
| | Diversion tunnel of Yuzixi hydropower station | 90.0 | 170.0 | 11.3 | 9.0 | Medium rockburst | | Vietas hydropower station in Sweden | 80.0 | 180.0 | 6.7 | 5.5 | Slight rockburst | | Guanyue tunnel, Japan | 89.0 | 236.0 | 8.3 | 5.0 | Medium rockburst | | Qinling tunnel | 60.7 | 111.5 | 7.9 | 6.2 | Strong rockburst | | Kuocangshan tunnel | 13.9 | 124.0 | 4.2 | 2.0 | No rockburst | | Jinping II hydropower station | 30.6 | 160.8 | 11.1 | 3.6 | Strong rockburst | | Erlangshan highway tunnel | 53.7 | 62.29 | 6.7 | 5.9 | Medium rockburst | | Qinling railway tunnel | 40 | 98 | 7.6 | 4.6 | Slight rockburst |
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