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Probabilistic Stability Analysis of a Tunnel Face in Spatially Random Hoek–Brown Rock Masses with a Multi-Tangent Method

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Abstract

Tunnel excavations in heavily fractured rock masses are often subjected to the high risk of face instability. To solve this problem, the probabilistic stability analysis of tunnel face is performed in this contribution, in which the fractured rock masses are modelled as spatially random media that follow the Hoek–Brown failure criterion. The method of Karhunen–Loève expansion is adopted to characterize the spatial variabilities of Hoek–Brown parameters. Under this circumstance, the conventional tangent technique fails to integrate the Hoek–Brown failure criterion into the kinematical approach of limit analysis framework. Thus, the multi-tangent method which permits to use multiple tangent lines to represent the nonlinear Hoek–Brown failure envelope is proposed. A discretized three-dimensional failure mechanism of tunnel face is adopted to determine critical face pressures within the framework of limit analysis. Due to a large number of input variables required by the generation of random fields, the global sensitivity analysis and a sparsity scheme are employed to reduce the problem dimension. The method of spare polynomial chaos expansion is then employed to perform Monte Carlo simulation with a significant reduction of calls to the computationally expensive original model. Finally, the parametric analysis on the deterministic model and probabilistic model is performed to gain an insight into the proposed approach.

Highlights

  • The three-dimensional stability of a tunnel face driven in Hoek-Brown rock masses is evaluated by combining the limit analysis and random field theory.

  • The method of Karhunen-Loève expansion is adopted to characterize the spatial variabilities of Hoek-Brown parameters.

  • A multi-tangent method is proposed to determine the equivalent shear strength parameters of rock masses.

  • A fast and accurate probabilistic model for tunnel face reliability analysis is obtained with the sparse polynomial chaos expansion method.

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Abbreviations

σ 1 :

Maximum principle stress

σ 3 :

Minimum principle stress

σ c :

Uniaxial compressive strength

m i :

Constant related to the hardness of the rock masses

GSI:

Geological strength index

D :

Artificial disturbance factor

c t :

Equivalent cohesion of rock masses

φ t :

Equivalent internal friction angle of rock masses

G i :

Lognormal random field of parameter i

G ln i :

Normal random field of parameter lni

μ i :

Mean value of parameter i

σ i :

Standard deviation of parameter i

M :

Number of truncation terms in Karhunen–Loève expansion

ξ j :

Independent variable of standard normal distribution

λ j :

Eigenvalue of the autocorrelation function

ψ j :

Eigenfunction of the autocorrelation function

ε err :

Error of the Karhunen–Loève expansion

Ω:

Domain of the random field

ρ i :

Autocorrelation function

ρ i , j :

Cross-correlation between random fields of i and j

θ h :

Horizontal autocorrelation distance

θ v :

Vertical autocorrelation distance

COV:

Coefficient of variation

ω :

Angular velocity of the failure mechanism

O :

Rotation center

E :

Center of the circular tunnel face

r E :

Length of OE

β E :

Rotation angle of OE

d :

Tunnel diameter

δ :

Side length of discretized element

[σ 3]n :

Minimum principle stress of the element n

γ :

Unit weight of rock masses

l :

Number of layers in the domain Ω

h n :

Burial depth of the element n

F :

Computational model

Y :

Model response

L :

Number of input variables

ψ α :

Multivariate polynomial

η j :

Unknown coefficients of the PCE

P :

Number of terms in the truncated PCE

H α :

Univariate polynomial

α :

Degree of the univariate polynomial

p :

PCE order

||α||q :

q-Quasi-norm of α

χ :

Experimental design

N :

Size of experimental design

Ψ :

Space-independent matrix with dimensions of N × P

S :

Sobol’s indices

R 2 :

Coefficient of determination

ε cut :

Cutoff value:

\(Q_{tgt}^{2}\) :

Target accuracy

p max :

Maximum PCE order

g T :

Performance function

σ U :

Applied face pressure

σ T :

Critical face pressure

N MCS :

Size of MCS population

I :

Indicator function

P f :

Failure probability

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Acknowledgements

This study was financially supported by National Natural Science Foundation of China (Grant No. 42102321 and Grant No. 52108388), The National Key Research and Development Program of China (Grant No. 2017YFE0119500) and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUGGC09). The financial funding is greatly appreciated.

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Appendix: Work rate equation for face stability analysis

Appendix: Work rate equation for face stability analysis

Let σT denotes the pressure provided by the shield machine to retain tunnel face stability, so its work rate can be calculated by

$$W_{{\sigma_{T} }} = \iint_{S} {\overrightarrow {{\sigma_{T} }} \cdot \overrightarrow {v\;} {\text{d}}S} = - \omega \sigma_{T} \sum\limits_{j} {\left( {S_{j0} R_{j0} \cos \beta_{j0} } \right)} ,$$
(32)

where Sj0 is the area of the j-th element on tunnel face; Rj0 is the corresponding rotation radius; βj0 is the angle between the rotation radius and the negative direction of the Y-axis.

The work rate done by gravity of rock masses can be calculated as

$$W_{\gamma } = \iiint_{V} {\overrightarrow {{\gamma_{{}} }} \cdot \overrightarrow {{v_{{}} }} {\text{d}}V} = \omega \gamma \sum\limits_{i} {\sum\limits_{j} {\left( {V_{ij} R_{ij} \sin \beta_{ij} } \right)} } ,$$
(33)

where γ is the unit weight of rock masses; Vij is the volume of the element determined by local coordinate system; Rij is the corresponding rotation radius; βij is the angle between the rotation radius and the negative direction of the Y-axis.

The internal energy dissipation can be expressed by

$$W_{D} = \sum {\iint_{S} {c_{t} \cdot v} \cdot \cos \varphi_{t} {\text{d}}S} = \omega \sum\limits_{i} {\sum\limits_{j} {\left( {\left[ {c_{t} } \right]_{ij} \cos \left[ {\varphi_{t} } \right]_{ij} S_{ij} R_{ij} } \right)} } ,$$
(34)

where [ct]ij and [φt]ij represent the corresponding equivalent cohesion and internal friction angle of the element; Sij denotes the elementary area on the failure surface and Rij is the corresponding rotation radius.

By equating the internal energy dissipation and external work rate, the required face pressure can be obtained as

$$\sigma_{T} = \gamma dN_{\gamma } - N_{c} ,$$
(35)

where Nγ, Nc are non-dimensional coefficients as

$$N_{\gamma } = \frac{{\sum\nolimits_{i} {\sum\nolimits_{j} {\left( {V_{ij} R_{ij} \sin \beta_{ij} } \right)} } }}{{d\sum\nolimits_{j} {\left( {S_{j0} R_{j0} \cos \beta_{j0} } \right)} }},$$
(36)
$$N_{c} = \frac{{\sum\nolimits_{i} {\sum\nolimits_{j} {\left( {\left[ {c_{t} } \right]_{ij} \cos \left[ {\varphi_{t} } \right]_{ij} S_{ij} R_{ij} } \right)} } }}{{\sum\nolimits_{j} {\left( {S_{j0} R_{j0} \cos \beta_{j0} } \right)} }}.$$
(37)

The expressions of [ct]ij and [φt]ij are specified by Eqs. (5) and (6). They are determined by the Hoek–Brown parameters and the stress state of the element of interest. More details for the derivation can be obtained from (Mollon et al. 2011a).

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Li, T., Pan, Q., Shen, Z. et al. Probabilistic Stability Analysis of a Tunnel Face in Spatially Random Hoek–Brown Rock Masses with a Multi-Tangent Method. Rock Mech Rock Eng 55, 3545–3561 (2022). https://doi.org/10.1007/s00603-022-02821-y

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