Rockburst Risk Assessment of Deep Lying Tunnels Based on Combination Weight and Unascertained Measure Theory: A Case Study of Sangzhuling Tunnel on Sichuan-Tibet Traffic Corridor
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摘要: 针对复杂山区深埋隧道岩爆危险性评价中的诸多不确定性因素问题，通过归纳分析典型高地应力条件下深埋隧道岩爆破坏特征及关键影响因子，从客观反映高地应力环境、岩石力学性能和围岩性质3个层面确定5项岩爆评价指标，利用未确知测度理论建立隧道岩爆危险性评价模型.为了充分考虑岩爆危险性评价的主观因素和客观因素，通过引入距离函数，采用熵权法和层次分析法相结合构建组合赋权法，综合确定各指标的权重系数.基于未确知测度理论及计算规则，结合岩爆危险性分级标准，构建直线型单指标测度函数，计算单指标测度评价矩阵和多指标测度向量，依照置信度准则进行岩爆危险性评价.将构建的岩爆危险性评价未确知测度模型应用于川藏交通廊道桑珠岭隧道，并与强度应力比法、Russenes判据、岩石脆性系数、岩体完整性系数、岩石弹性能指数等单指标判据评价结果及实际岩爆结果进行对比.研究结果表明：该模型评价结果的准确率达到94.4%，比单指标岩爆判据的准确率高16.7%~66.7%.Abstract: Aming at the uncertain factors in the rockburst risk assessment of deep lying tunnels in complex mountainous areas, the unascertained measurement theory was used to establish a tunnel rockburst risk evaluation model. By summarizing and analyzing the rockburst failure characteristics and key influencing factors of deep lying tunnels under typical high geostress conditions, 5 evaluation indexes were determined from three levels that objectively reflect the high geostress environment, rock mechanical properties and surrounding rock properties. In order to fully consider the subjective and objective factors of rockburst risk assessment, by introducing a distance function, using the combination of entropy weight method and analytic hierarchy process to construct a combination weighting method, and comprehensively determined the weight coefficient of each index. Based on the unascertained measurement theory and calculation rules, it constructed a linear single-index measurement function according to the rockburst risk classification standard. By calculating the single-index measurement evaluation matrix and the multi-index measurement vector, the rockburst risk assessment was carried out according to the confidence criterion. The unascertained measurement model of rockburst risk assessment was applied to the Sangzhuling tunnel of the Sichuan-Tibet traffic corridor. The evaluation accuracy was compared with that obtained from single index criteria such as strength-stress ratio method, Russenes criterion, rock brittleness coefficient, rock mass integrity coefficient, rock elastic energy index and the actual rockburst results. The research results show that the accuracy rate of the evaluation result of the model is 94.4%, which is 16.7%-66.7% higher than that of the single-index rockburst criterion.
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图 1 岩爆等级与各影响因素的关系
Fig. 1. Relationship between rockburst grade and influencing factors
图 2 岩爆的多准则评估系统
Fig. 2. Multicriteria assessment system for rockburst of the surrounding rock
图 4 川藏交通廊道桑珠岭隧道地理位置
Fig. 4. Geographical position of Sangzhuling tunnel on the Sichuan-Tibet traffic corridor
图 6 桑珠岭隧道典型岩爆现场实录
Fig. 6. Records of typical rockburst damage in the Sangzhuling tunnel
图 7 岩爆倾向性评价标准及结果分析
Fig. 7. Test results and evaluation criteria of rockburst proneness
表 1 部分典型隧道岩爆调查实录
Table 1. Investigation cases of rockburst in some typical tunnels
隧道名称 里程 地层岩性 岩石强度σc(MPa) 最大主应力σmax(MPa) 地质构造 弹性能指数Wet 围岩级别 地下水 岩爆等级 川藏交通廊道桑珠岭隧道 DK178+544~DK179+072 英云闪长岩 141 25.1 无 4.3 Ⅱ 干燥 中等 DK179+667~DK179+727 英云闪长岩 141 26.3 无 4.3 Ⅲ 干燥 中等 DK180+062~DK182+743 闪长岩 147 36.9 无 4.6 Ⅱ 干燥 强烈 DK188+280~DK188+896 闪长岩 147 24.6 无 4.6 Ⅱ 干燥 中等 DK189+430~DK189+450 花岗岩 143 22.3 无 4.0 Ⅲ 干燥 中等 DK189+450~DK189+610 花岗岩 143 21.6 无 4.0 Ⅱ 干燥 轻微 川藏公路二郎山隧道 主洞K260+080~K260+240 砂岩、泥岩等 60 24.0 无 2.0 Ⅱ~Ⅲ 干燥 轻微 主洞K260+380~K260+440 砂岩等 65 25.0 无 2.2 Ⅲ 干燥 轻微 主洞K260+791~K260+815 砂岩、泥岩等 60 20.0 无 2.0 Ⅱ 干燥 轻微 平导K260+100~K260+250 砂质泥岩 55 25.0 无 2.2 Ⅱ 干燥 轻微 平导K261+820~K261+940 灰岩等 80 35.0 无 2.6 Ⅲ 干燥 中等 平导K261+940~K262+295 砂质泥岩 55 20.0 无 2.0 Ⅱ~Ⅲ 干燥 轻微 都汶公路福堂隧道 ZK19+526~ZK19+533 花岗岩 75 12.0 无 2.8 Ⅲ 干燥 轻微 ZK19+608~ZK19+612 花岗岩 75 12.0 无 2.8 Ⅲ 渗滴水 轻微 ZK20+400~ZK20+408 花岗岩 75 16.0 无 2.8 Ⅲ 干燥 中等 ZK20+422~ZK20+428 花岗岩 85 18.0 无 3.3 Ⅱ 干燥 中等 ZK20+453~ZK20+456 花岗岩 85 18.0 无 3.3 Ⅱ 干燥 中等 ZK20+518~ZK20+520 花岗岩夹辉绿岩 75 18.0 无 2.8 Ⅲ 干燥 轻微-中等 锦屏二级水电站引水隧道 K0+622~k0+637 灰白色大理岩 138 25.0 无 2.8 Ⅱ 干燥 轻微 K1+149~K1+300 灰黑色大理岩 124 35.0 向斜核部 3.3 Ⅱ 渗滴水 轻微 K1+555~K1+569 灰黑色大理岩 124 40.0 无 3.3 Ⅱ 干燥 轻微-中等 K1+786~K1+792 灰白色大理岩 138 36.0 无 2.8 Ⅰ 干燥 轻微 K1+801~K1+804 条带状大理岩 110 40.0 无 1.8 Ⅱ 干燥 轻微 K2+060~K2+283 条带状大理岩 110 42.0 背斜核部 1.8 Ⅱ 渗水 轻微 
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表 2 岩爆危险性等级与各评价指标的关系
Table 2. Relation between rating and evaluation indexes of rockburst
岩爆等级 σc/σmax σθ/σc σc/σt Kv Wet 无岩爆 ≥7 < 0.20 ≥40.0 < 0.55 < 2.0 轻微岩爆 [4, 7) [0.20, 0.30) [26.7, 40.0) [0.55, 0.65) [2.0, 3.5) 中等岩爆 [2, 4) [0.30, 0.55) [14.5, 26.7) [0.65, 0.75) [3.5, 5.0) 强烈岩爆 < 2 ≥0.55 < 14.5 ≥0.75 ≥5.0
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表 3 岩石力学基本参数
Table 3. Basic parameters of rock mechanics
岩性 密度ρ(g/cm3) 纵波速度vp(m/s) 抗压强度σc(MPa) 抗拉强度σt(MPa) 弹性模量E(GPa) 泊松比v 花岗岩 2.64 5 233.84 138.35 6.51 28.23 0.22 2.68 5 169.03 161.98 6.85 31.57 0.24 2.63 5 128.19 143.46 6.47 31.37 0.22 平均值 2.65 5 177.02 147.93 6.61 30.39 0.23 闪长岩 2.71 5 694.83 142.36 6.72 33.53 0.22 2.72 5 450.18 151.45 7.37 33.78 0.20 2.67 5 135.65 137.52 7.03 32.72 0.21 平均值 2.70 5 426.89 143.78 7.04 33.34 0.21
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表 4 DK-SZLSD-2钻孔地应力测量结果
Table 4. In-situ geostress test results of DK-SZLSD-2 borehole
序号 埋深(m) 主应力(MPa) SH方位 SH Sh Sv 1 205.65~206.15 9.41 5.61 5.34 N9°W 2 297.45~298.05 10.58 7.70 7.72 - 3 391.85~392.45 11.36 8.61 10.18 N6°W 4 476.95~477.55 12.58 9.70 12.39 - 5 582.65~583.15 17.72 13.10 15.13 N7°E
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表 5 岩体力学参数
Table 5. Mechanical parameters of rock masses
岩体类型 弹性模量E(GPa) 泊松比v 密度ρ
（g/cm3）糜棱岩带 20.0 0.35 2.45 东缘断裂 6.0 0.27 2.35 花岗闪长岩 33.0 0.21 2.70 英云闪长岩 34.0 0.21 2.70 闪长岩 33.3 0.21 2.70 花岗岩 30.4 0.23 2.65 巴玉断层 8.0 0.26 2.40
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表 6 桑珠岭隧道部分里程的应力计算结果
Table 6. Stress calculation results of some mileage of Sangzhuling tunnel
隧道里程 σmax(MPa) 夹角(°) σθ(MPa) DK175+950~DK176+875 22.1 25.3 40.5 DK176+875~DK177+733 21.9 26.7 41.9 DK178+544~DK179+092 25.1 42.1 48.9 DK179+667~DK179+727 26.3 46.3 47.8 DK180+062~DK182+743 36.9 56.9 73.7 DK184+371~DK184+404 29.7 54.5 61.3 DK184+680~DK184+713 27.6 38.9 61.1 DK184+800~DK185+806 18.3 42.3 31.9 DK185+848~DK185+850 16.2 38.9 32.7 DK185+949~DK186+072 14.8 38.9 20.7 DK188+280~DK188+896 24.6 28.3 58.4 DK188+896~DK188+946 23.1 28.3 54.4 DK188+946~DK189+167 22.9 27.6 54.0 DK189+167~DK189+217 22.7 25.1 54.8 DK189+217~DK189+390 22.1 26.3 41.9 DK189+430~DK189+450 22.3 24.9 30.9 DK189+450~DK189+610 21.6 23.1 27.2 DK189+660~DK190+065 21.8 25.2 32.3
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表 7 桑珠岭隧道评价指标值
Table 7. Evaluation index value of Sangzhuling tunnel
样本编号 隧道里程 岩性 围岩级别 岩爆评价指标 σc/σmax σθ/σc σc/σt Kv Wet 1 DK175+950~DK176+875 英云闪长岩 Ⅲ 6.47 0.28 19.53 0.52 4.30 2 DK176+875~DK177+733 英云闪长岩 Ⅱ 6.53 0.29 21.40 0.62 4.30 3 DK178+544~DK179+092 英云闪长岩 Ⅱ 5.70 0.34 21.40 0.71 4.30 4 DK179+667~DK179+727 英云闪长岩 Ⅲ 5.44 0.33 19.53 0.71 4.30 5 DK180+062~DK182+743 闪长岩 Ⅱ 3.88 0.52 22.54 0.81 4.60 6 DK184+371~DK184+404 闪长岩 Ⅱ 4.81 0.43 21.40 0.71 4.60 7 DK184+680~DK184+713 闪长岩 Ⅲ 5.18 0.43 19.53 0.62 4.60 8 DK184+800~DK185+806 闪长岩 Ⅱ 7.81 0.22 21.40 0.62 4.60 9 DK185+848~DK185+850 闪长岩 Ⅲ 8.82 0.23 19.53 0.62 4.60 10 DK185+949~DK186+072 闪长岩 Ⅱ 9.66 0.14 21.40 0.62 4.60 11 DK188+280~DK188+896 闪长岩 Ⅱ 5.81 0.41 21.40 0.71 4.60 12 DK188+896~DK188+946 闪长岩 Ⅲ 6.19 0.38 19.53 0.62 4.60 13 DK188+946~DK189+167 闪长岩 Ⅱ 6.24 0.38 21.40 0.71 4.60 14 DK189+167~DK189+217 闪长岩 Ⅲ 6.30 0.38 19.53 0.62 4.60 15 DK189+217~DK189+390 花岗岩 Ⅱ 6.70 0.28 22.38 0.62 4.00 16 DK189+430~DK189+450 花岗岩 Ⅲ 6.63 0.21 21.38 0.62 4.00 17 DK189+450~DK189+610 花岗岩 Ⅱ 6.85 0.18 22.38 0.62 4.00 18 DK189+660~DK190+065 花岗岩 Ⅱ 6.79 0.22 22.38 0.62 4.00
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表 8 岩爆各评价指标权重
Table 8. Weight of each evaluation index of rockburst
评价指标 σc/σmax σθ/σc σc/σt Kv Wet 主观权重wj(AHP) 0.205 0.205 0.065 0.328 0.197 客观权重wi(EW) 0.117 0.267 0.040 0.364 0.212 组合权重w 0.164 0.233 0.054 0.345 0.204
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表 9 桑珠岭隧道岩爆危险性评价结果
Table 9. Rockburst risk evaluation results of Sangzhuling tunnel
样本编号 隧道里程 综合未确知测度 实际岩爆等级 C1 C2 C3 C4 评价结果 1 DK175+950~DK176+875 0.451 0.251 0.275 0.023 轻微 轻微 2 DK176+875~DK177+733 0.113 0.511 0.363 0.014 轻微 轻微 3 DK178+544~DK179+092 0.022 0.259 0.637 0.083 中等 中等 4 DK179+667~DK179+727 0.000 0.281 0.627 0.092 中等 中等 5 DK180+062~DK182+743 0.000 0.066 0.317 0.617 强烈 强烈 6 DK184+371~DK184+404 0.000 0.122 0.707 0.171 中等 中等 7 DK184+680~DK184+713 0.000 0.419 0.470 0.111 中等 中等 8 DK184+800~DK185+806 0.397 0.279 0.228 0.095 轻微 轻微 9 DK185+848~DK185+850 0.397 0.276 0.222 0.105 轻微 轻微 10 DK185+949~DK186+072 0.397 0.279 0.228 0.095 轻微 轻微 11 DK188+280~DK188+896 0.034 0.153 0.648 0.164 中等 中等 12 DK188+896~DK188+946 0.075 0.424 0.395 0.105 中等 中等 13 DK188+946~DK189+167 0.081 0.146 0.609 0.164 中等 中等 14 DK189+167~DK189+217 0.087 0.412 0.395 0.105 中等 中等 15 DK189+217~DK189+390 0.131 0.543 0.325 0.000 轻微 轻微 16 DK189+430~DK189+450 0.310 0.400 0.290 0.000 轻微 中等 17 DK189+450~DK189+610 0.381 0.334 0.285 0.000 轻微 轻微 18 DK189+660~DK190+065 0.281 0.434 0.285 0.000 轻微 轻微
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[1] Cai, M. F., 2016. Prediction and Prevention of Rockburst in Metal Mines: A Case Study of Sanshandao Gold Mine. Journal of Rock Mechanics and Geotechnical Engineering, 8(2): 204-211. https://doi.org/10.1016/j.jrmge.2015.11.002 [2] Chen, H. J., Li, N. H., Nie, D. X., et al., 2002. A Model for Prediction of Rockburst by Artificial Neural Network. Chinese Journal of Geotechnical Engineering, 24(2): 229-232(in Chinese with English abstract). [3] Chen, S. K., Li, H. R., Zhou, H., et al., 2021. Route Selection of Deep-Lying and Hard Rock Tunnel in the Sichuan-Tibet Railway Based on Rock Burst Risk Assessment. Hydrogeology and Engineering Geology, 48(5): 81-90(in Chinese with English abstract). [4] Cheng, Q. S., 1997. Attribute Recognition Theoretical Model with Application. Acta Scicentiarum Naturalum Universitis Pekinesis, 33(1): 12-20(in Chinese with English abstract). [5] Dong, L. J., Li, X. B., Peng, G. K., 2013. Prediction of Rockburst Classification Using Random Forest. Transactions of Nonferrous Metals Society of China, 23(2): 472-477. https://doi.org/10.1016/s1003-6326(13)62487-5 [6] Dong, L. J., Peng, G. J., Fu, Y. H., et al., 2008. Unascertained Measurement Classifying Model of Goaf Collapse Prediction. Journal of Coal Science and Engineering (China), 14(2): 221-224. https://doi.org/10.1007/s12404-008-0046-9 [7] Feng, X. T., Xiao, Y. X., Feng, G. L., et al., 2019. Study on the Development Process of Rockbursts. Chinese Journal of Rock Mechanics and Engineering, 38(4): 649-673(in Chinese with English abstract). [8] Gong, F. Q., Li, X. B., 2007. A Distance Discriminant Analysis Method for Prediction of Possibility and Classification of Rockburst and Its Application. Chinese Journal of Rock Mechanics and Engineering, 26(5): 1012-1018(in Chinese with English abstract). [9] Guo, J. L., Zhang, H. F., Xu, W. C., et al., 2019. The Bulk Crustal Composition of the Southeastern Lhasa Terrane and Its Origin. Earth Science, 44(6): 1809-1821(in Chinese with English abstract). [10] He, Y. F., Li, T. B., Cao, H. Y., 2020. Attribute Recognition Model of Fatalness Assessment of Rockburst in Tunnel Construction and Its Application. Hydrogeology & Engineering Geology, 47(2): 102-111(in Chinese with English abstract). [11] Jia, Q. J., Wu, L., Li, B., 2019. The Comprehensive Prediction Model of Rockburst Tendency in Tunnel Based on Optimized Unascertained Measure Theory. Geotechnical and Geology Engineering, 37: 3399-3411. https://doi.org/10.1007/s10706-019-00854-9 [12] Jia, Y. P., Lü, Q., Shang, Y. Q., et al., 2014. Rockburst Prediction Based on Rough Set and Ideal Point Method. Journal of Zhejiang University (Engineering Science), 48(3): 498-503(in Chinese with English abstract). [13] Kidybiński, A., 1981. Bursting Liability Indices of Coal. International Journal of Rock Mechanics and Mining Sciences, 18(4): 295-304. https://doi.org/10.1016/0148-9062(81)91194-3 [14] Li, P. X., Chen, B. R., Zhou, Y. Y., et al., 2019. Research Progress of Rockburst Prediction and Early Warning in Hard Rock Underground Engineering. Journal of China Coal Society, 44(Suppl. 2): 447-465(in Chinese with English abstract). [15] Li, S. C., Zhou, Z. Q., Li, L. P., et al., 2013. Risk Evaluation Theory and Method of Water Inrush in Karst Tunnels and Its Applications. Chinese Journal of Rock Mechanics and Engineering, 32(9): 1858-1867(in Chinese with English abstract). [16] Li, T. B., Ma, C. C., Zhu, M. L., et al., 2017. Geomechanical Types and Mechanical Analyses of Rockbursts. Engineering Geology, 222: 72-83. https://doi.org/10.1016/j.enggeo.2017.03.011 [17] Li, T. B., Meng, L. B, Wang, L. S., et al., 2016. High Stress Tunnel Stability and Large Deformation Disaster Prevention. Science Press, Beijing, 361-391(in Chinese). [18] Ministry of Housing and Urban-Rural Development of the People's Republic of China, General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, 2014. GB50218-2014. Standard for Engineering Classification of Rock Mass. China Planning Press, Beijing(in Chinese). [19] National Railway Administration, 2016. TB10003-2016. Code for Design of Railway Tunnel. China Railway Publishing House, Beijing(in Chinese). [20] Pan, G. T., Ren, F., Yin, F. G., et al., 2020. Key Zones of Oceanic Plate Geology and Sichuan-Tibet Railway Project. Earth Science, 45(7): 2293-2304(in Chinese with English abstract). [21] Russenes, B. F., 1974. Analysis of Rock Spalling for Tunnels in Steep Vally Site. Norwegian Institute of Technology, Depatment of Geology, Norway. [22] Saaty, T. L., 1997. Applications of Analytical Hierarchies. Mathematics and Computers in Simulation, 21(1): 1-20. https://doi.org/10.1016/0378-4754(79)90101-0 [23] Shirani Faradonbeh, R., Taheri, A., 2019. Long-Term Prediction of Rockburst Hazard in Deep Underground Openings Using Three Robust Data Mining Techniques. Engineering with Computers, 35(2): 659-675. https://doi.org/10.1007/s00366-018-0624-4 [24] Song, B. W., Zhang, K. X., Xu, Y. D., et al., 2020. Paleogene Tectonic-Stratigraphic Realms and Sedimentary Sequence in China. Earth Science, 45(12): 4352-4369(in Chinese with English abstract). [25] Wang, D., Li, T., B., Jiang, L. W., et al. 2017. Analysis of the Stress Characteristics and Rock Burst of Ultra Deep Buried Tunnel in Sichuan-Tibet Railway. Journal of Railway Engineering Society, 34(4): 46-50(in Chinese with English abstract). [26] Wang, G. Y., 1990. Uncertainty Information and Its Mathematical Treatment. Journal of Harbin Architecture and Engineering Institute, 23(4): 1-9(in Chinese with English abstract). [27] Wang, M. W., Li, L., Jin, J. L., 2008. An Improved Set Pair Analysis Model for the Prediction of Rockburst. Rock and Soil Mechanics, 29(Suppl. 1): 511-518(in Chinese with English abstract). [28] Wang, Q. W., Ju, N. P., Du, L. L., et al., 2018. Three Dimensional Inverse Analysis of Geostress Field in the Sangri-Jiacha Section of Lasa-Linzhi Railway. Rock and Soil Mechanics, 39(4): 1450-1462(in Chinese with English abstract). [29] Wang, Y. C., Jing, H. W., Ji, X. W., et al., 2014. Model for Classification and Prediction of Rock Burst Intensity in a Deep Underground Engineering with Rough Set and Efficacy Coefficient Method. Journal of Central South University (Science and Technology), 45(6): 1992-1997(in Chinese with English abstract). [30] Wang, Y. H, Li W. D., Li, Q. G., et al., 1998. Method of Fuzzy Comprehensive Evaluations for Rockburst Prediction. Chinese Journal of Rock Mechanics and Engineering, 17(5): 493-501(in Chinese with English abstract). [31] Wu, F. Y., He, C., Wang, B., et al., 2021. Rock Burst Intensity Classification of Lhasa-Linzhi Railway Based on Stress Criterion. Journal of Southwest Jiaotong University, 56(4): 792-800(in Chinese with English abstract). [32] Xu, C., Liu, X. L., Wang, E. Z., et al., 2018. Rockburst Prediction and Classification Based on the Ideal-Point Method of Information Theory. Tunnelling and Underground Space Technology, 81: 382-390. https://doi.org/10.1016/j.tust.2018.07.014 [33] Xue, Y. G., Li, Z. Q., Li, S. C., et al., 2019. Prediction of Rock Burst in Underground Caverns Based on Rough Set and Extensible Comprehensive Evaluation. Bulletin of Engineering Geology and the Environment, 78(1): 417-429. https://doi.org/10.1007/s10064-017-1117-1 [34] Yan, J., He, C., Wang, B., et al., 2019. Inoculation and Characters of Rockbursts in Extra-Long and Deep-Lying Tunnels Located on Yarlung Zangbo Suture. Chinese Journal of Rock Mechanics and Engineering, 38(4): 769-781(in Chinese with English abstract). [35] Zhang, C., Wang, Q., Chen, J. P., et al., 2011. Evaluation of Debris Flow Risk in Jinsha River Based on Combined Weight Process. Rock and Soil Mechanics, 32(3): 831-836(in Chinese with English abstract). [36] Zhang, J. J., Fu, B. J., 2008. Rockburst and Its Criteria and Control. Chinese Journal of Rock Mechanics and Engineering, 27(10): 2034-2042(in Chinese with English abstract). [37] Zhou, H., Chen, S. K., Li, H. R., et al., 2021. Rockburst Prediction for Hard Rock and Deep-Lying Long Tunnels Based on the Entropy Weight Ideal Point Method and Geostress Field Inversion: A Case Study of the Sangzhuling Tunnel. Bulletin of Engineering Geology and the Environment, 80(5): 3885-3902. https://doi.org/10.1007/s10064-021-02175-9 [38] Zhou, H., Chen, S. K., Zhang, G. Z., et al., 2020. Efficiency Coefficient Method and Ground Stress Field Inversion for Rockburst Predicition in Deep and Long Tunnel. Journal of Engineering Geology, 28(6): 1386-1396(in Chinese with English abstract). [39] Zhou, J., Li, X. B., Mitri, H. S., 2018. Evaluation Method of Rockburst: State-of-the-Art Literature Review. Tunnelling and Underground Space Technology, 81: 632-659. https://doi.org/10.1016/j.tust.2018.08.029 [40] Zhu, L., Yang, J. Z., Wang, K., et al., 2009. Analysis of Heterogeneous Soil Water Using Information Entropy and Multifractal Theory. Earth Science, 34(6): 1037-1042(in Chinese with English abstract). [41] 陈海军, 郦能惠, 聂德新, 等, 2002. 岩爆预测的人工神经网络模型. 岩土工程学报, 24(2): 229-232. doi: 10.3321/j.issn:1000-4548.2002.02.023 [42] 陈仕阔, 李涵睿, 周航, 等, 2021. 基于岩爆危险性评价的川藏交通廊道某深埋硬岩隧道线路方案比选研究. 水文地质工程地质, 48(5): 81-90. [43] 程乾生, 1997. 属性识别理论模型及其应用. 北京大学学报(自然科学版), 33(1): 12-20. doi: 10.3321/j.issn:0479-8023.1997.01.002 [44] 冯夏庭, 肖亚勋, 丰光亮, 等, 2019. 岩爆孕育过程研究. 岩石力学与工程学报, 38(4): 649-673. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201904002.htm [45] 宫凤强, 李夕兵, 2007. 岩爆发生和烈度分级预测的距离判别方法及应用. 岩石力学与工程学报, 26(5): 1012-1018. doi: 10.3321/j.issn:1000-6915.2007.05.021 [46] 郭京梁, 张宏飞, 徐旺春, 等, 2019. 拉萨地体东南部整体地壳成分及其成因分析. 地球科学, 44(6): 1809-1821. doi: 10.3799/dqkx.2019.050 [47] 国家铁路局, 2016. TB 10003-2016. 铁路隧道设计规范. 北京: 中国铁道出版社. [48] 何怡帆, 李天斌, 曹海洋, 2020. 隧道施工期岩爆危险性评价的属性识别模型及工程应用. 水文地质工程地质, 47(2): 102-111. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG202002014.htm [49] 贾义鹏, 吕庆, 尚岳全, 等, 2014. 基于粗糙集-理想点法的岩爆预测. 浙江大学学报(工学版), 48(3): 498-503. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDZC201403019.htm [50] 李鹏翔, 陈炳瑞, 周扬一, 等, 2019. 硬岩岩爆预测预警研究进展. 煤炭学报, 44(增刊2): 447-465. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB2019S2006.htm [51] 李术才, 周宗青, 李利平, 等, 2013. 岩溶隧道突水风险评价理论与方法及工程应用. 岩石力学与工程学报, 32(9): 1858-1867. doi: 10.3969/j.issn.1000-6915.2013.09.018 [52] 李天斌, 孟陆波, 王兰生, 等, 2016. 高地应力隧道稳定性及岩爆、大变形灾害防治. 北京: 科学出版社, 361-391. [53] 潘桂棠, 任飞, 尹福光, 等, 2020. 洋板块地质与川藏交通廊道工程地质关键区带. 地球科学, 45(7): 2293-2304. doi: 10.3799/dqkx.2020.070 [54] 宋博文, 张克信, 徐亚东, 等, 2020. 中国古近纪构造-地层区划及地层格架. 地球科学, 45(12): 4352-4369. doi: 10.3799/dqkx.2020.122 [55] 王栋, 李天斌, 蒋良文, 等, 2017. 川藏交通廊道某超深埋隧道地应力特征及岩爆分析. 铁道工程学报, 34(4): 46-50. doi: 10.3969/j.issn.1006-2106.2017.04.010 [56] 王光远, 1990. 未确知信息及其数学处理. 哈尔滨建筑工程学院学报, 23(4): 1-9. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBJ199004000.htm [57] 汪明武, 李丽, 金菊良, 2008. 岩爆预测的改进集对分析模型. 岩土力学, 28(增刊1): 511-518. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX2008S1102.htm [58] 王庆武, 巨能攀, 杜玲丽, 等, 2018. 拉林铁路桑日至加查段三维地应力场反演分析. 岩土力学, 39(4): 1450-1462. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201804038.htm [59] 王迎超, 靖洪文, 吉咸伟, 等, 2014. 深埋地下工程岩爆烈度分级预测的RS-功效系数模型. 中南大学学报(自然科学版), 45(6): 1992-1997. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNGD201406033.htm [60] 王元汉, 李卧东, 李启光, 等, 1998. 岩爆预测的模糊数学综合评判方法. 岩石力学与工程学报, 17(5): 493-501. doi: 10.3321/j.issn:1000-6915.1998.05.003 [61] 吴枋胤, 何川, 汪波, 等, 2021. 基于应力判据法的拉林铁路岩爆烈度分级. 西南交通大学学报, 56(4): 792-800. https://www.cnki.com.cn/Article/CJFDTOTAL-XNJT202104015.htm [62] 严健, 何川, 汪波, 等, 2019. 雅鲁藏布江缝合带深埋长大隧道群岩爆孕育及特征. 岩石力学与工程学报, 38(4): 769-781. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201904011.htm [63] 张晨, 王清, 陈剑平, 等, 2011. 金沙江流域泥石流的组合赋权法危险度评价. 岩土力学, 32(3): 831-836. doi: 10.3969/j.issn.1000-7598.2011.03.032 [64] 张镜剑, 傅冰骏, 2008. 岩爆及其判据和防治. 岩石力学与工程学报, 27(10): 2034-2042. doi: 10.3321/j.issn:1000-6915.2008.10.010 [65] 中华人民共和国住房和城乡建设部, 中华人民共和国国家质量监督检验检疫总局, 2014. GB50218-2014. 工程岩体分级标准. 北京: 中国计划出版社. [66] 周航, 陈仕阔, 张广泽, 等, 2020. 基于功效系数法和地应力场反演的深埋长大隧道岩爆预测研究. 工程地质学报, 28(6): 1386-1396. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202006025.htm [67] 朱磊, 杨金忠, 王康, 等, 2009. 基于信息熵与多重分形理论的非均匀流动分析. 地球科学, 34(6): 1037-1042. doi: 10.3321/j.issn:1000-2383.2009.06.020 -
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