Probabilistic back analysis method for determining surrounding rock parameters of deep hard rock tunnel

WU Zhong-guang; WU Shun-chuan

doi:10.13374/j.issn2095-9389.2019.01.008

Volume 41 Issue 1

Jan. 2019

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WU Zhong-guang, WU Shun-chuan. Probabilistic back analysis method for determining surrounding rock parameters of deep hard rock tunnel[J]. Chinese Journal of Engineering, 2019, 41(1): 78-87. doi: 10.13374/j.issn2095-9389.2019.01.008

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WU Zhong-guang, WU Shun-chuan. Probabilistic back analysis method for determining surrounding rock parameters of deep hard rock tunnel[J]. Chinese Journal of Engineering, 2019, 41(1): 78-87. doi: 10.13374/j.issn2095-9389.2019.01.008

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Probabilistic back analysis method for determining surrounding rock parameters of deep hard rock tunnel

doi: 10.13374/j.issn2095-9389.2019.01.008

WU Zhong-guang^{1, 2
,
,},
WU Shun-chuan^{1, 3}

1.
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Research Center for Standards and Metrology, China Academy of Transportation Sciences, Beijing 100029, China
3.
Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China

More Information

Corresponding author: WU Zhong-guang, E-mail: kinliwu@163.com
Received Date: 2018-05-29
Publish Date: 2019-01-01

Abstract

Abstract

A large number of tunnel projects are being constructed or will be constructed in the mountainous areas of western China. However, they are several safety challenges in the construction of deep hard rock tunnels because of the complex topographic and geological conditions, strong geological tectonic activities, large burial depth, and high in situ stress level. Uncertainty of tunnel wall parameters is one of main factors that contribute to tunnel construction risk. The traditional deterministic back analysis method cannot reflect the uncertainty characteristics of tunnel wall parameters; therefore, within the framework of Bayesian theory, a probabilistic back analysis method based on integrating multi-source monitoring information was proposed for determining the surrounding rock parameters of deep hard rock tunnel. First, the uncertainty sources of three parameters——uniaxial compressive strength (UCS), crack initiation stress to UCS ratio, and tensile strength for the widely used damage initiation and spalling limit approach——were analyzed, and their probabilistic statistical characteristics were determined. Second, a multi-output support vector machine (MSVM) was optimized by particle swarm optimization (PSO) algorithm, and an intelligent response surface model was established to reflect the nonlinear mapping relationship between back-analyzed parameters and field monitoring data. Last, by combination with the Bayesian (B) analysis method, the B-PSO-MSVM model was established, and surrounding rock parameters were dynamically updated by applying the Markov Chain Monte Carlo simulation algorithm. The method was applied to a deep hard rock tunnel, and parameters from probabilistic back analysis were utilized to calculate the point change of the tunnel vault settlement and peripheral displacement convergence as well as the depth of excavation damage zones, and the results agreed well with the actual monitoring data. It is shown that this method can be used to back analyze multi parameters of surrounding rock quickly and probabilistically, and parameters updated can be applied for risk assessment in construction safety and structural reliability design for the hard rock tunnel.
- hard rock tunnel,
- probabilistic back analysis,
- multi-source information fusion,
- Bayesian theory,
- multi-output support vector machine

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References(52)

References

[1]	Baecher G B, Christian J T. Reliability and Statistics in Geotechnical Engineering. New York: John Wiley and Sons, Inc, 2003
[2]	Der Kiureghian A, Ditlevsen O. Aleatory or epistemic? Does it matter? Struct Saf, 2009, 31(2): 105 doi: 10.1016/j.strusafe.2008.06.020
[3]	Langford J C, Diederichs M S. Quantifying uncertainty in Hoek-Brown intact strength envelopes. Int J Rock Mech Min Sci, 2015, 74: 91 doi: 10.1016/j.ijrmms.2014.12.008
[4]	蔡美峰, 何滿潮, 劉東燕. 巖石力學與工程. 2版. 北京: 科學出版社, 2013 Cai M F, He M C, Liu D Y. Rock Mechanics and Engineering. 2nd Ed. Beijing: Science Press, 2013
[5]	Gilbert R B, Tang W H. Model uncertainty in offshore geotechnical reliability// Proceeding of the 27th Offshore Technology Conference. Houston, 1995: 557
[6]	Gilbert R B, Wright S G, Liedtke E. Uncertainty in back analysis of slopes: Kettleman Hills case history. J Geotech Geoenviron Eng, 1998, 124(12): 1167 doi: 10.1061/(ASCE)1090-0241(1998)124:12(1167)
[7]	Zhang L L, Zhang J, Zhang L M, et al. Back analysis of slope failure with Markov chain Monte Carlo simulation. Comput Geotech, 2010, 37(7-8): 905 doi: 10.1016/j.compgeo.2010.07.009
[8]	Zhang L L, Zuo Z B, Ye G L, et al. Probabilistic parameter estimation and predictive uncertainty based on field measurements for unsaturated soil slope. Comput Geotech, 2013, 48: 72 doi: 10.1016/j.compgeo.2012.09.011
[9]	Zhang J, Tang W H, Zhang L M. Efficient probabilistic back-analysis of slope stability model parameters. J Geotech Geoenviron Eng, 2010, 136(1): 99 doi: 10.1061/(ASCE)GT.1943-5606.0000205
[10]	Wang L, Hwang J H, Luo Z, et al. Probabilistic back analysis of slope failure——a case study in Taiwan. Comput Geotech, 2013, 51: 12 doi: 10.1016/j.compgeo.2013.01.008
[11]	Li S J, Zhao H B, Ru Z L, et al. Probabilistic back analysis based on Bayesian and multi-output support vector machine for a high cut rock slope. Eng Geol, 2016, 203: 178 doi: 10.1016/j.enggeo.2015.11.004
[12]	Miranda T, Dias D, Eclaircy-Caudron S, et al. Back analysis of geomechanical parameters by optimisation of a 3D model of an underground structure. Tunnell Undergr Space Technol, 2011, 26(6): 659 doi: 10.1016/j.tust.2011.05.010
[13]	Miro S, K?nig M, Hartmann D, et al. A probabilistic analysis of subsoil parameters uncertainty impacts on tunnel-induced ground movements with a back-analysis study. Comput Geotech, 2015, 68: 38 doi: 10.1016/j.compgeo.2015.03.012
[14]	Haas C, Einstein H H. Updating the "Decision Aids for Tunneling". J Constr Eng Manage, 2002, 128(1): 40 doi: 10.1061/(ASCE)0733-9364(2002)128:1(40)
[15]	Miranda T, Correia A G, e Sousa L R. Bayesian methodology for updating geomechanical parameters and uncertainty quantification. Int J Rock Mech Min Sci, 2009, 46(7): 1144 doi: 10.1016/j.ijrmms.2009.03.008
[16]	Zhang J, Tang W H, Zhang L M, et al. Characterising geotechnical model uncertainty by hybrid Markov Chain Monte Carlo simulation. Comput Geotech, 2012, 43: 26 doi: 10.1016/j.compgeo.2012.02.002
[17]	Peng M, Li X Y, Li D Q, et al. Slope safety evaluation by integrating multi-source monitoring information. Struct Saf, 2014, 49: 65 doi: 10.1016/j.strusafe.2013.08.007
[18]	Feng X D, Jimenez R. Bayesian prediction of elastic modulus of intact rocks using their uniaxial compressive strength. Eng Geol, 2014, 173: 32 doi: 10.1016/j.enggeo.2014.02.005
[19]	Wang Y, Cao Z J. Probabilistic characterization of Young's modulus of soil using equivalent samples. Eng Geol, 2013, 159: 106 doi: 10.1016/j.enggeo.2013.03.017
[20]	Cao Z J, Wang Y, Li D Q. Quantification of prior knowledge in geotechnical site characterization. Eng Geol, 2016, 203: 107 doi: 10.1016/j.enggeo.2015.08.018
[21]	Wang Y, Aladejare A E. Bayesian characterization of correlation between uniaxial compressive strength and Young's modulus of rock. Int J Rock Mech Min Sci, 2016, 85: 10 doi: 10.1016/j.ijrmms.2016.02.010
[22]	Contreras L F, Brown E T, Ruest M. Bayesian data analysis to quantify the uncertainty of intact rock strength. J Rock Mech Geotech Eng, 2018, 10(1): 11 doi: 10.1016/j.jrmge.2017.07.008
[23]	Li D Q, Zheng D, Cao Z J, et al. Response surface methods for slope reliability analysis: Review and comparison. Eng Geol, 2016, 203: 3 doi: 10.1016/j.enggeo.2015.09.003
[24]	Lv Q, Sun H Y, Low B K. Reliability analysis of ground-support interaction in circular tunnels using the response surface method. Int J Rock Mech Min Sci, 2011, 48(8): 1329 doi: 10.1016/j.ijrmms.2011.09.020
[25]	Zhao H B, Ru Z L, Chang X, et al. Reliability analysis of tunnel using least square support vector machine. Tunnell Undergr Space Technol, 2014, 41: 14 doi: 10.1016/j.tust.2013.11.004
[26]	Lv Q, Chan C L, Low B K. Probabilistic evaluation of ground-support interaction for deep rock excavation using artificial neural network and uniform design. Tunnell Undergr Space Technol, 2012, 32: 1 doi: 10.1016/j.tust.2012.04.014
[27]	Gomes H M, Awruch A M. Comparison of response surface and neural network with other methods for structural reliability analysis. Struct Saf, 2004, 26(1): 49 doi: 10.1016/S0167-4730(03)00022-5
[28]	Li X, Li X B, Su Y H. A hybrid approach combining uniform design and support vector machine to probabilistic tunnel stability assessment. Struct Saf, 2016, 61: 22 doi: 10.1016/j.strusafe.2016.03.001
[29]	Lv Q, Xiao Z P, Ji J, et al. Moving least squares method for reliability assessment of rock tunnel excavation considering ground-support interaction. Comput Geotech, 2017, 84: 88 doi: 10.1016/j.compgeo.2016.11.019
[30]	Drucker H, Burges C J C, Kaufman L, et al. Support vector regression machines. Adv in Neural Inf Process Syst, 1996, 28(7): 779
[31]	Hurtado J E. An examination of methods for approximating implicit limit state functions from the viewpoint of statistical learning theory. Struct Saf, 2004, 26(3): 271 doi: 10.1016/j.strusafe.2003.05.002
[32]	Tuia D, Verrelst J, Alonso L, et al. Multioutput support vector regression for remote sensing biophysical parameter estimation. IEEE Geosci Remote Sens Lett, 2011, 8(4): 804 doi: 10.1109/LGRS.2011.2109934
[33]	Zheng D J, Cheng L, Bao T F, et al. Integrated parameter inversion analysis method of a CFRD based on multi-output support vector machines and the clonal selection algorithm. Comput Geotech, 2013, 47: 68 doi: 10.1016/j.compgeo.2012.07.006
[34]	Li S J, Zhao H B, Ru Z L, et al. Probabilistic back analysis based on Bayesian and multi-output support vector machine for a high cut rock slope. Eng Geol, 2016, 203: 178 doi: 10.1016/j.enggeo.2015.11.004
[35]	Kennedy J, Eberhart R. Particle swarm optimization//Proceedings of ICNN'95 International Conference on Neural Networks. Perth, 1995: 1942
[36]	馮夏庭, 周輝, 李邵軍, 等. 復雜條件下巖石工程安全性的智能分析評估和時空預測系統. 巖石力學與工程學報, 2008, 27(9): 1741 doi: 10.3321/j.issn:1000-6915.2008.09.002 Feng X T, Zhou H, Li S J, et al. System of intelligent evaluation and prediction in space-time for safety of rock engineering under hazardous environment. Chin J Rock Mech Eng, 2008, 27(9): 1741 doi: 10.3321/j.issn:1000-6915.2008.09.002
[37]	Diederichs M S. The 2003 Canadian geotechnical colloquium: mechanistic interpretation and practical application of damage and spalling prediction criteria for deep tunnelling. Can Geotech J, 2007, 44(9): 1082 doi: 10.1139/T07-033
[38]	Hoek E, Carranza-Torres C T, Corkum B, et al. Hoek-Brown failure criterion - 2002 edition//Proceedings of NARMS-Tac Conference. Toronto, 2002: 267
[39]	Perras M A, Diederichs M S. Predicting excavation damage zone depths in brittle rocks. Int J Rock Mech Geotech Eng, 2016, 8(1): 60 doi: 10.1016/j.jrmge.2015.11.004
[40]	Dammyr ?. Prediction of brittle failure for TBM tunnels in anisotropic rock: a case study from Northern Norway. Rock Mech Rock Eng, 2016, 49(6): 2131 doi: 10.1007/s00603-015-0910-z
[41]	Langford J C, Diederichs M S. Reliable support design for excavations in brittle rock using a global response surface method. Rock Mech Rock Eng, 2015, 48(2): 669 doi: 10.1007/s00603-014-0567-z
[42]	Ghazvinian E, Perras M A, Diederichs M S, et al. The effect of anisotropy on crack damage thresholds in brittle rocks//Proceedings of the 47th US Rock Mechanics/Geomechanics Symposium. San Francisco, 2013: 503
[43]	Martin C D, Kaiser P K, McCreath D R. Hoek-Brown parameters for predicting the depth of brittle failure around tunnels. Can Geotech J, 1999, 36(1): 136 doi: 10.1139/t98-072
[44]	Griffith A. The theory of rupture//Proceedings of the 1st International Congress on Applied Mechanics. Delft, 1924: 55
[45]	Murrell S A F. A criterion for brittle fracture of rocks and concrete under triaxial stress and the effect of pore pressure on the criterion//Proceedings of the 5th Rock Mechanics Symposium. Oxford, 1963: 563
[46]	Cai M. Practical estimates of tensile strength and Hoek-Brown strength parameter m_i of brittle rocks. Rock Mech Rock Eng, 2010, 43(2): 167 doi: 10.1007/s00603-009-0053-1
[47]	Perras M A, Diederichs M S. A review of the tensile strength of rock: concepts and testing. Geotech Geol Eng, 2014, 32(2): 525 doi: 10.1007/s10706-014-9732-0
[48]	Metropolis N, Rosenbluth A W, Rosenbluth M N, et al. Equations of state calculations by fast computing machines. J Chem Phys, 1953, 21(6): 1087 doi: 10.1063/1.1699114
[49]	Hastings W K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 1970, 57(1): 97 doi: 10.1093/biomet/57.1.97
[50]	余勝威. MATLAB優化算法案例分析與應用. 北京: 清華大學出版社, 2014 Yu S W. Case Analysis and Application of MATLAB Optimization Algorithm. Beijing: Tsinghua University Press, 2014
[51]	吳成, 張平. 高地應力硬巖洞室開挖破壞區數值模擬方法探討. 水文地質工程地質, 2012, 39(6): 35 https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201206010.htm Wu C, Zhang P. Analysis of numerical simulation methods for excavation failure zone of deep underground opening in hard rocks with high geostress. Hydrogeol Eng Geol, 2012, 39(6): 35 https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201206010.htm
[52]	Li X, Li X B, Su Y H. A hybrid approach combining uniform design and support vector machine to probabilistic tunnel stability assessment. Struct Saf, 2016, 61: 22 doi: 10.1016/j.strusafe.2016.03.001