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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

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Abstract

To evaluate rockbursts in deep-lying long tunnels, a multiple factor analysis and predictions were conducted. It is necessary to establish whether the primary evaluation indexes cover the entire development–occurrence–evolution process for rockbursts and how best to determine the weights of the final selected indexes. We developed an evaluation model with attribute reduction and chose 5 out of 11 primary evaluation indexes to cover the typical characteristics of energy storage, rockburst proneness, and risk of failure. The weights of the primary evaluation indexes and the offset distance were then determined using the entropy weight ideal point method. Combining geostress field inversion and rock mechanic tests, the evaluation model was applied to the case of the Sangzhuling Tunnel along the Sichuan–Tibet railway. Rockburst prediction results achieved 41.2% and 94.1% accuracy when using the Manhattan and Euclidean distance functions, respectively. A more specific classification of the evaluation indexes may optimize the weight assignment and help obtain more accurate results. This paper provides a reliable method for rockburst predictions for hard rock and deep-lying long tunnels, which may have good prospects for engineering applications.

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Abbreviations

σ c :

Uniaxial compressive strength (UCS) of rock, MPa

σ t :

Tensile strength of rock, MPa

σ max :

Maximum principal stress of the cavern, MPa

σ θ :

Maximum tangential stress of the cavern, MPa

σ L :

Axial stress of tunnel, MPa

K v :

Intactness index of rock mass

W et  . :

Elastic energy index

E s :

Maximum elastic strain energy index, kJ/m3

H :

Maximum buried depth of the tunnel, m

B :

Strength brittleness coefficient

I s :

Point load strength of rock, MPa

RQD :

Rock quality designation

E :

Elastic modulus of rock, GPa

v :

Poisson’s ratio

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Acknowledgements

The authors thank the editors and the anonymous reviewers for the valuable comments on this study.

Availability of data and material

The data and material used to support the findings of this study are available from the corresponding author upon request.

Funding

The financial support of the National Natural Science Foundation of China (Grant No. 41672295), General Project of the Science and Technology Department in Sichuan province (Grant No.17YYJC0799), Science and Technology Project of Department of Transportation of Sichuan Province (Grant No.2015B1-1), and Scientific project of China Railway Eryuan Engineering Group Co., Ltd. (Grant No. KYY2020122(20-22)) are gratefully acknowledged.

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Authors and Affiliations

  1. Faculty of Geosciences and Environmental Engineering, South-west Jiaotong University, West Park of Hi-Tech Zone, Chengdu, Sichuan, 611756, China

    Hang Zhou, Shikuo Chen, Hanrui Li & Tong Liu

  2. China Railway Eryuan Engineering Group Co., Ltd., Chengdu, 610031, Sichuan, China

    Huanlong Wang

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  1. Hang Zhou
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  2. Shikuo Chen
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  3. Hanrui Li
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  4. Tong Liu
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Contributions

Shikuo Chen and Hang Zhou contributed to the study conception and design. Hang Zhou performed the simulations and wrote the first draft. Material preparation and data collection were performed by Hang Zhou, Hanrui Li, Tong Liu, and Huanlong Wang. Hang Zhou and Shikuo Chen analyzed the calculation and test data. All authors commented on previous versions of the manuscript. Shikuo Chen revised the paper and gave final approval of the version to be submitted.

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Correspondence to Shikuo Chen.

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The code used to support the findings of this study are available from the corresponding author upon request.

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Zhou, H., Chen, S., Li, H. et al. 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. Bull Eng Geol Environ 80, 3885–3902 (2021). https://doi.org/10.1007/s10064-021-02175-9

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