Skip to main content
SpringerLink
Log in

A review of tunnel rockburst prediction methods based on static and dynamic indicators

  • Review Article
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

Rockbursts frequently occur in tunneling projects and pose a serious threat to workers and the environment. Therefore, accurate prediction of rockbursts is of great practical significance. Currently, various rockburst prediction methods exist, with static and dynamic indicators playing a key role. This paper analyzes the importance of rockburst prediction methods based on Citespace software. The results indicate that microseismic monitoring, acoustic emission, and machine learning are the most important methods. The paper focuses on four common rockburst prediction methods: empirical methods, microseismic monitoring, acoustic emission, and machine learning, from the perspective of static and dynamic indicators. The performance and application of static and dynamic indicators in the four common prediction methods in recent years are summarized, the limitations of static and dynamic indicators at this stage are discussed, and possible future development directions are proposed. This paper provides the necessary perspective and tools for better understanding the advantages and disadvantages of static and dynamic indicators in the four rockburst prediction methods.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Abbreviations

\({\sigma }_{\theta }\) :

Tangential stress in surrounding rock, MPa

\({\sigma }_{c}\) :

Uniaxial compressive strength of rock mass, MPa

Rb :

Uniaxial saturated compressive strength

NDS:

Normalized bias stress

\({\sigma }_{1}\) :

Maximum principal in situ stress, MPa

Rt :

Uniaxial tensile strength

Bi :

Rock brittleness coefficient, \({\sigma }_{c}/{\sigma }_{t}\)

BIM:

Brittleness index modified

BSR:

Brittle shear ratio

WTG :

Rockburst index

Mcoal :

Modulus after destruction

E:

Young’s modulus, GPa

Kcr:

Critical mining stress index

Kv :

Rock mass integrity coefficient

S:

Stress index

\({{\text{W}}}_{{\text{et}}}\) :

Strain energy storage index, kJ m−3

IRB :

Rockburst risk index

H:

Buried depth in the tunnel, m

\({\sigma }_{RB}\) :

Maximum stress of rockburst, MPa

R0 :

Tunnel diameter

\({\sigma }_{3}^{\prime}\) :

Minimum principal stress at destruction, MPa

\({\sigma }_{t}\) :

Tensile strength of rock mass, MPa

\({A}_{CF}^{\prime}\) :

Peak energy impact index

\({\sigma }_{0}\) :

Maximum ground stress in the surrounding rock before excavation

\({\varepsilon }_{p}\) :

Peak strain

\({LERS}_{i}\) :

Localized energy release rate

Uh :

The energy dissipated to overcome frictional and support resistance during impact ground pressure, kJ m−3

RPI:

Rockburst propensity index

\({\sigma }_{rm}^{\prime}\) :

Triaxial rock strength based on the Hoek–Brown strength criterion

DT:

Failure duration index

\({U}_{ET}^{e}\) :

Elastic strain energy density at the unloading level

\({U}_{ET}^{d}\) :

Dissipated energy density at the unloading level

\({W}_{P}\) :

Energy conservation index

WE :

Work done by pressure

\({\varepsilon }_{r}\) :

Residual strain

\({\omega }_{e}\) :

Pre-peak elastic energy density

φst :

Dissipative strain energy

Bq :

Rockburst energy index

\({U}_{q}^{e}\) :

Elastic strain energy density

\({U}^{a}\) :

Destructive energy density

RERI:

Relative energy release index

LERR:

Local energy release rate

\({U}_{imin}\) :

Minimum elastic strain energy density

\({U}_{e}\) :

Elastic energy density at peak

\({U}_{BIM}^{0}\) :

Peak elastic strain energy density

\({U}_{d}\) :

Dissipated energy density at peak

φsp :

Elastic strain energy

\({\sigma }_{p}\) :

Peak stress

\({W}_{ET}^{P}\) :

Peak strength strain energy storage index

\({\sigma }_{r}\) :

Residual stress

\({K}_{ED}^{p}\) :

The ratio of peak point elastic strain energy to dissipation energy

Eimax :

Maximum strain energy density

Eimin :

Minimum strain energy density

\({K}_{ED}^{f}\) :

The ratio of elastic strain energy to dissipation energy at the failure point

References

Download references

Funding

This work was supported by the National Engineering Research Centre Open Project (EC2022011), the National Major Research Instrument Development Project (52227901), the Anhui Provincial Universities Outstanding Young Research Funding Project (2022AH030088), the Anhui Provincial Universities Collaborative Innovation Funding Project (GXXT-2022-020).

Author information

Authors and Affiliations

  1. School of Civil Engineering and Architecture, Anhui University of Science and Technology, Huainan, 232001, China

    Qinghe Zhang, Weiguo Li, Tianle Zheng, Zhiwei Liang & Xiaorui Wang

  2. State Key Laboratory Mine Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan, 232001, China

    Qinghe Zhang & Liang Yuan

Authors
  1. Qinghe Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weiguo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liang Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tianle Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiwei Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaorui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study's conception and design. The first draft of the manuscript was written by Qinghe Zhang. Material preparation, data collection, and analysis were performed by Weiguo Li, Liang Yuan, Tianle Zheng, Zhiwei Liang, and Xiaorui Wang. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Qinghe Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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 (e.g. a society or other partner) 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

Zhang, Q., Li, W., Yuan, L. et al. A review of tunnel rockburst prediction methods based on static and dynamic indicators. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06657-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11069-024-06657-3

Keywords

Search

Navigation