Geomechanics and Engineering

  • 제18권6호
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  • Pages.595-603
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  • 2019
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Probabilistic analysis for face stability of tunnels in Hoek-Brown media

  • Li, T.Z. (Department of Civil and Architecture Engineering, Changzhou Institute of Technology) ;
  • Yang, X.L. (School of Civil Engineering, Central South University)
  • 투고 : 2019.02.01
  • 심사 : 2019.08.14
  • 발행 : 2019.08.30
⟨ 이전 논문 다음 논문 ⟩

초록

A modified model combining Kriging and Monte Carlo method (MC) is proposed for probabilistic estimation of tunnel face stability in this paper. In the model, a novel uniform design is adopted to train the Kriging, instead of the existing active learning function. It has advantage of avoiding addition of new training points iteratively, and greatly saves the computational time in model training. The kinematic approach of limit analysis is employed to define the deterministic computational model of face failure, in which the Hoek-Brown failure criterion is introduced to account for the nonlinear behaviors of rock mass. The trained Kriging is used as a surrogate model to perform MC with dramatic reduction of calls to actual limit state function. The parameters in Hoek-Brown failure criterion are considered as random variables in the analysis. The failure probability is estimated by direct MC to test the accuracy and efficiency of the proposed probabilistic model. The influences of uncertainty level, correlation relationship and distribution type of random variables are further discussed using the proposed approach. In summary, the probabilistic model is an accurate and economical alternative to perform probabilistic stability analysis of tunnel face excavated in spatially random Hoek- Brown media.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation

참고문헌

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피인용 문헌

  1. Kinematic Stability of Tunnel Face in Non-uniform Soils vol.24, pp.2, 2019, https://doi.org/10.1007/s12205-019-0996-z
  2. Deterministic and probabilistic analysis of tunnel face stability using support vector machine vol.25, pp.1, 2021, https://doi.org/10.12989/gae.2021.25.1.017