Skip to main content

Advertisement

SpringerLink
Log in

Assessing the Probability of Strainburst Potential Via an Integration of Monte Carlo Simulation and Machine Learning Algorithms

  • Original Paper
  • Published:
Rock Mechanics and Rock Engineering Aims and scope Submit manuscript

Abstract

Strainburst poses significant risks to the safety of workers in deep underground engineering. Assessing strainburst potential plays a crucial role in disaster prevention. Due to the anisotropy and heterogeneity of rock mass, it is essential to quantify the strainburst potential from the perspective of probability. This study aims to propose a methodology that integrates Monte Carlo simulation (MCS) and machine learning (ML) algorithms to evaluate the probability of strainburst potential. First, a numerical model based on damage initiation and spalling limit (DISL) method is established. Then, a novel indicator, absolute local energy release rate (ALERR), is proposed to assess strainburst potential, which describes the absolute value of elastic strain energy released after rock mass failure. The location of the maximum value of ALERR is used to determine the depth of outburst pit, which is adopted to evaluate the strainburst potential level. So, the quantitative relationship between strainburst potential and ALERR can be established. Thereafter, a strainburst dataset including six variables and the depth of outburst pit is obtained based on the MCS method and numerical model. To improve the computational efficiency, seven different ML algorithms are integrated with MCS to calculate the probability of strainburst potential, respectively. Finally, the proposed methodology is used for the strainburst potential evaluation in the 3# headrace tunnel of Jinping II hydropower station. Results show that random forest (RF) and gradient boosting (GB) algorithms have better evaluation performance and can be combined with MCS to calculate the probability of strainburst potential reliably. The proposed methodology can provide valuable guidance for the probability assessment of strainburst potential.

Highlights

  • A new methodology that integrates Monte Carlo simulation (MCS) and machine learning (ML) algorithms is proposed to evaluate the probability of strainburst potential.

  • A novel indicator, absolute local energy release rate (ALERR), is proposed to assess strainburst potential.

  • A numerical model, which combines MCS and damage initiation and spalling limit (DISL) approaches, is established to simultaneously simulate the brittle failure behavior of hard rock and variability of rock mechanics parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Adoko AC, Gokceoglu C, Wu L, Zuo QJ (2013) Knowledge-based and data-driven fuzzy modeling for rockburst prediction. Int J Rock Mech Min Sci 61:86–95

    Article  Google Scholar 

  • Aladejare AE, Akeju VO (2020) Design and sensitivity analysis of rock slope using monte carlo simulation. Geotech Geol Eng 38:573–585

    Article  Google Scholar 

  • Barton N, Lien R, Lunde J (1974) Engineering classification of rock masses for the design of tunnel support. Rock Mech 6(4):189–236

    Article  Google Scholar 

  • Cai M (2016) Prediction and prevention of rockburst in metal mines-a case study of sanshandao gold mine. J Rock Mech Geotech Eng 8(2):204–211

    Article  Google Scholar 

  • Cai M, Kaiser PK (2018) Rockburst support reference book: volume 1—Rockburst phenomenon and support characteristics. MIRARCO—Mining Innovation, Laurentian University, Sudbury

  • Castro LAM, Bewick RP, Carter TG (2012) An overview of numerical modelling applied to deep mining. In: Sousa V, Fernandes A (eds) Innovative numerical modeling in geomechanics. CRC Press, London, pp 393–414

    Google Scholar 

  • Chen WZ, Lu SP, Guo XH, Qiao CJ (2009) Research on unloading confining pressure tests and rockburst criterion based on energy theory. Chin J Rock Mech Eng 28(8):1530–1540

    Google Scholar 

  • Cook NGW (1966) The design of underground excavations: in the 8th US symposium on rock mechanics USRMS. Am Rock Mech Assoc 6(4):45–52

    Google Scholar 

  • Diederichs MS (2003) Rock fracture and collapse under low confinement conditions. Rock Mech Rock Eng 36(5):339–381

    Article  Google Scholar 

  • Diederichs MS (2007) Mechanistic interpretation and practical application of damage and spalling prediction criteria for deep tunnelling. Can Geotech J 44(9):1082–1116

    Article  Google Scholar 

  • Diederichs MS, Carter T, Martin CD (2010) Practical rock spall prediction in tunnels. Proceedings of international tunnelling Association world tunnel congress, pp 1–8

  • Dong LJ, Li XB, Peng K (2013) Prediction of rockburst classification using random forest. T Nonferr Metal Soc 23(2):472–477

    Article  Google Scholar 

  • Durrheim RJ (2010) Mitigating the risk of rockbursts in the deep hard rock mines of South Africa: 100 years of research. In: Brune J (ed) Extracting the science: a century of mining research. Society for mining, metallurgy and exploration, Littleton, pp 156–171

    Google Scholar 

  • Edelbro C (2009) Numerical modelling of observed fallouts in hard rock masses using an instantaneous cohesion-softening friction-hardening model. Tunn Undergr Sp Tech 24(4):398–409

    Article  Google Scholar 

  • Feng XT, Chen BR, Zhang CQ, Li SJ, Wu SY (2013) Mechanism, warning and dynamic control of rockburst development processes. Science Press, Beijing

    Google Scholar 

  • Feng GL, Feng XT, Chen BR, Xiao YX, Yu Y (2015) A microseismic method for dynamic warning of rockburst development processes in tunnels. Rock Mech Rock Eng 48(5):2061–2076

    Article  Google Scholar 

  • Gong FQ, Yan JY, Li XB (2018) A new criterion of rock burst proneness based on the linear energy storage law and the residual elastic energy index. Chin J Rock Mech Eng 37(9):1993–2014

    Google Scholar 

  • Gong FQ, Yan JY, Li XB, Luo S (2019) A peak-strength strain energy storage index for rock burst proneness of rock materials. Int J Rock Mech Min Sci 117:76–89

    Article  Google Scholar 

  • Guo JQ, Zhao Q, Wang JB, Zhang J (2015) Rockburst prediction based on elastic strain energy. Chin J Rock Mech Eng 34(9):1886–1893

    Google Scholar 

  • Hajiabdolmajid V, Kaiser PK, Martin CD (2002) Modelling brittle failure of rock. Int J Rock Mech Min Sci 39(6):731–741

    Article  Google Scholar 

  • Hawkes I (1966) Significance of in-situ stress levels. Proceedings of the 1st International Society for Rock Mechanics and Rock Engineering Congress, Portugal, Lisbon

  • Heal D, Hudyma M, Potvin Y (2006) Evaluating rockburst damage potential in underground mining. proceedings of the 41st US Symposium on rock mechanics (USRMS), Golden, pp 1020–1025

  • Hedley DGF (1991) A five-year review of the Canada-Ontario industry rockburst project. Canada centre for mineral and energy technology, Ottawa

    Book  Google Scholar 

  • Jia QJ, Wu L, Li B, Chen CH, Peng YX (2019) The comprehensive prediction model of rockburst tendency in tunnel based on optimized unascertained measure theory. Geotech Geol Eng 37(4):3399–3411

    Article  Google Scholar 

  • Kaiser PK, Cai M (2012) Design of rock support system under rockburst condition. J Rock Mech Geotech Eng 4(3):215–227

    Article  Google Scholar 

  • Keneti A, Sainsbury BA (2018) Review of published rockburst events and their contributing factors. Eng Geol 246:361–373

    Article  Google Scholar 

  • Kidybiński A (1981) Bursting liability indices of coal. Int J Rock Mech Min Sci 18(4):295–304

    Article  Google Scholar 

  • Langford JC, Diederichs MS (2015) Reliable support design for excavations in brittle rock using a global response surface method. Rock Mech Rock Eng 48(2):669–689

    Article  Google Scholar 

  • Li N, Jimenez R (2018) A logistic regression classifier for long-term probabilistic prediction of rock burst hazard. Nat Hazards 90(1):197–215

    Article  Google Scholar 

  • Liang WZ, Zhao GY, Wu H, Dai B (2019a) Risk assessment of rockburst via an extended MABAC method under fuzzy environment. Tunn Undergr Sp Tech 83:533–544

    Article  Google Scholar 

  • Liang WZ, Zhao GY, Wang X, Zhao J, Ma CD (2019b) Assessing the rockburst risk for deep shafts via distance-based multi-criteria decision making approaches with hesitant fuzzy information. Eng Geol 260:105211

    Article  Google Scholar 

  • Martin CD, Kaiser PK, McCreath DR (1999) Hoek-brown parameters for predicting the depth of brittle failure around tunnels. Can Geotech J 36(1):136–151

    Article  Google Scholar 

  • Mitri HS, Tang B, Simon R (1999) FE modelling of mining-induced energy release and storage rates. J S Afr I Min Metall 99(2):103–110

    Google Scholar 

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  • Pu YY, Apel DB, Xu HW (2019) Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier. Tunn Undergr Sp Tech 90:12–18

    Article  Google Scholar 

  • Qiu SL, Feng XT, Zhang CQ, Wu WP (2011) Development and validation of rockburst vulnerability index(RVI) in deep hard rock tunnels. Chin J Rock Mech Eng 30(6):1126–1141

    Google Scholar 

  • Qiu SL, Feng XT, Jiang Q, Zhang CQ (2014) A novel numerical index for estimating strainburst vulnerability in deep tunnels. Chin J Rock Mech Eng 33(10):2007–2017

    Google Scholar 

  • Russenes BF (1974) Analysis of rock spalling for tunnels in steep valley sides (MSc Thesis). Norwegian lnstitute of Technology

  • Simser BP (2019) Rock burst management in Canadian hard rock mines. J Rock Mech Geotech Eng 11(5):1036–1043

    Article  Google Scholar 

  • Sinha S, Walton G (2018) A progressive s-shaped yield criterion and its application to rock pillar behavior. Int J Rock Mech Min Sci 105:98–109

    Article  Google Scholar 

  • Stacey TR (1981) A simple extension strain criterion for fracture of brittle rock. Int J Rock Mech Min Sci 18(6):469–474

    Article  Google Scholar 

  • Su GS, Feng XT, Jiang Q, Chen GQ (2006) Study on new index of local energy release rate for stability analysis and optimal design of underground rock mass engineering with high geostress. Chin J Rock Mech Eng 25(12):2453–2460

    Google Scholar 

  • Tao ZY (1988) Support design of tunnels subjected to rockbursting. International society for rock mechanics international symposium, Madrid

    Google Scholar 

  • Turchaninov IA, Markov GA, Gzovsky MV, Kazikayev DM, Frenze UK, Batugin SA, Chabdarova UI (1972) State of stress in the upper part of the Earth’s crust based on direct measurements in mines and on tectonophysical and seismological studies. Phys Earth Planet Inter 6(4):229–234

    Article  Google Scholar 

  • Wang JL (2013) Mechanical characteristics of deeply buried marble and its technical application (PhD Thesis). Kunming University of Science and Technology

  • Wang CL, Wu AX, Lu H, Bao TC, Liu XH (2015) Predicting rockburst tendency based on fuzzy matter–element model. Int J Rock Mech Min Sci 75:224–232

    Article  Google Scholar 

  • Wang XT, Li SC, Xu ZH, Xue YG, Hu J, Li ZQ, Zhang B (2019) An interval fuzzy comprehensive assessment method for rock burst in underground caverns and its engineering application. B Eng Geol Environ 78(7):5161–5176

    Article  Google Scholar 

  • Wang MW, Liu QY, Wang X, Shen FQ (2020) Prediction of rockburst based on multidimensional connection cloud model and set pair analysis. Int J Geomech 20(1):04019147

    Article  Google Scholar 

  • Xu XL (2017) Research on the experiment and meso-simulation of tensile characteristics and its fracture mechanism of brittle rock (PhD Thesis). University of Science and Technology Beijing

  • Xu J, Jiang JD, Xu N, Liu QS, Gao YF (2017) A new energy index for evaluating the tendency of rockburst and its engineering application. Eng Geol 230:46–54

    Article  Google Scholar 

  • Xue Y, Cao ZZ, Du F, Zhu L (2018) The influence of the backfilling roadway driving sequence on the rockburst risk of a coal pillar based on an energy density criterion. Sustainability 10(8):2609

    Article  Google Scholar 

  • Xue YG, Bai CH, Kong FM, Qiu DH, Li LP, Su MX, Zhao Y (2020) A two-step comprehensive evaluation model for rockburst prediction based on multiple empirical criteria. Eng Geol 268:105515

    Article  Google Scholar 

  • Yang FJ, Zhou H, Lu JJ, Zhang CQ, Hu DW (2015) An energy criterion in the process of rockburst. Chin J Rock Mech Eng 34(S1):2706–2714

    Google Scholar 

  • Zhang CS, Chen XR, Hou J, Chu WJ (2010) Study of mechanical behavior of deep-buried marble at Jinping II hydropower station. Chin J Rock Mech Eng 29(10):1999–2009

    Google Scholar 

  • Zhang CQ, Zhou H, Feng XT (2011) An index for estimating the stability of brittle surrounding rock mass: FAI and its engineering application. Rock Mech Rock Eng 44(4):401–414

    Article  Google Scholar 

  • Zhang CQ, Feng XT, Zhou H, Qiu SL, Wu WP (2012) Case histories of four extremely intense rockbursts in deep tunnels. Rock Mech Rock Eng 45(3):275–288

    Article  Google Scholar 

  • Zhou J, Li XB, Mitri HS (2018) Evaluation method of rockburst: state-of-the-art literature review. Tunn Undergr Sp Tech 81:632–659

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key Research and Development Program of China (2018YFC0604606), the Natural Science Foundation of China (51904334), and the Natural Science Foundation of Hunan Province, China (2022JJ40601, 2021JJ40751).

Author information

Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Weizhang Liang, Guoyan Zhao & Ju Ma

  2. Deep Mining Laboratory of Shandong Gold Group Co., Ltd., Laizhou, 261400, China

    Huanxin Liu

  3. School of Resource Environment and Safety Engineering, University of South China, Hengyang, 421001, China

    Ying Chen & Ming Lan

Authors
  1. Weizhang Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huanxin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guoyan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ju Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ming Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ju Ma.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liang, W., Liu, H., Zhao, G. et al. Assessing the Probability of Strainburst Potential Via an Integration of Monte Carlo Simulation and Machine Learning Algorithms. Rock Mech Rock Eng 56, 129–142 (2023). https://doi.org/10.1007/s00603-022-03067-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00603-022-03067-4

Keywords

Search

Navigation