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Microseismicity-based method for the dynamic estimation of the potential rockburst scale during tunnel excavation

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Abstract

The severity and harmfulness of a rockburst event are significantly correlated with the scale of rock mass ejection, especially when the rock mass are not supported. This paper presents a microseismicity-based method for the early estimation of rockburst occurrence and its potential scale, which is graded according to the volume of the rockburst pit (Rv). The establishment of the estimation method involves a rockburst database, a grading scheme of the rockburst scale, selection and clustering analysis of rockburst samples, training of an artificial neural network (ANN) model, and dynamic updating. Firstly, a rockburst database is established from cases that were collected from the tunnels at depths of 1900–2525 m in the Jinping II hydropower station, located in southwest China. A grading scheme regarding the rockburst scale is preliminarily proposed on the basis of statistical analysis. Next, seventy-four rockburst cases, collected in tunnels with microseismic (MS) monitoring from October 2010 to March 2011, are selected as typical rockburst samples by using cluster analysis, and the relationships between the microseismicity and rockburst scale are deeply revealed. Then, three MS parameters, namely, the cumulative number of events, the cumulative energy, and the cumulative apparent volume, are determined and used together as input indicators for the identification of the rockburst scale. The estimation model is trained and cross-validated by the ANN optimized through genetic algorithm (GA). Finally, the performance of this microseismicity-based method has been validated by thirty-one cases that occurred in the tunnels with a cumulative length of 1.85 km, excavated from April 2011 to November 2011. The result indicates that approximately 83.9% of the rockburst cases could be reliably estimated. This study provides a new and feasible method for rockburst scale estimation, which can be used separately or applied as a complementary approach to current prediction methods for risk assessment and management of rockbursts in drill-and-blast tunneling.

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Abbreviations

Rv:

the volume of the rockburst pit

MS:

microseismic

N :

cumulative number of MS events

E :

cumulative microseismic energy

V :

cumulative microseismic apparent volume

\( \dot{N} \) :

microseismic event rate

\( \dot{E} \) :

microseismic energy rate

\( \dot{V} \) :

microseismic apparent volume rate

UCS:

uniaxial compression strength

TS:

tensile strength

ANN:

artificial neural network

GA:

genetic algorithm

BPNN:

back-propagation neural network

W ij :

connective weight between neuron i and neuron j

θ i :

the neural network threshold

G-1:

the rockburst of scale grade 1

G-2:

the rockburst of scale grade 2

G-3:

the rockburst of scale grade 3

G-4:

the rockburst of scale grade 4

G-5:

the rockburst of scale grade 5

x Kj :

value of microseismic parameter j of the Kth case

x Lj :

value of microseismic parameter j of the Lth case

\( {X}_{\mathrm{ij}}^{\ast } \) :

normalization value of microseismic parameter j of case i

X ij :

value of microseismic parameter j of sample i

X j, max :

the maximum value of microseismic parameter j among total cases

X j, min :

the minimum value of microseismic parameter j among total cases

u i :

the result calculated by the artificial neural model for the ith learning sample

u i*:

expected result for the ith learning sample

P i :

the probability of rockburst intensity i

w j :

weighting coefficient of microseismic parameter j for rockburst warning

P ji :

functional relationship between parameter j and rockburst intensity i

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Acknowledgements

The authors are grateful for the financial supports from the Basic Research Program of Natural Science from Shaanxi Science and Technology Department (Grant No. 2019JQ-171), the National Natural Science Foundation of China (Grant No. U1965205), and the Fundamental Research Funds for the Central Universities (Grant No. 300102210110). The microseismic monitoring data involved in this paper is obtained from the institute of Rock and Soil Mechanics, Chinese Academy of Sciences. The authors would also express their sincere thanks to Professors Shi-Yong Wu and Ya-Xun Xiao, as well as Dr. Hua-Jun Ming who gave support and assistance during microseismicity monitoring in Jinping II hydropower station project.

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Authors and Affiliations

  1. School of Highway, Chang’an University, Xi’an, 710064, China

    Guo-Feng Liu

  2. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, China

    Guo-Feng Liu, Quan Jiang, Guang-Liang Feng & Bing-Rui Chen

  3. School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, 430071, China

    Dong-Fang Chen

  4. School of Environment and Resource, Southwest University of Science and Technology, Mianyang, 621010, China

    Zhou-Neng Zhao

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  1. Guo-Feng Liu
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Correspondence to Quan Jiang.

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Liu, GF., Jiang, Q., Feng, GL. et al. Microseismicity-based method for the dynamic estimation of the potential rockburst scale during tunnel excavation. Bull Eng Geol Environ 80, 3605–3628 (2021). https://doi.org/10.1007/s10064-021-02173-x

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