3.8 Article

Stochastic Stability Analysis of Tunnels Considering Randomness of Rock Mass Properties

Journal

JOURNAL OF MINING AND ENVIRONMENT
Volume 12, Issue 4, Pages 1121-1139

Publisher

SHAHROOD UNIV TECHNOLOGY
DOI: 10.22044/jme.2021.11310.2113

Keywords

Underground excavations; Probabilistic stability analysis; Rock mass property variability; Finite difference method; Monte Carlo simulation

Funding

  1. Geodata Engineering SpA

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The study presents an approach for the probabilistic stability analysis of tunnels considering the heterogeneity of geo-mechanical properties. A stochastic procedure is followed to account for the variability in rock mass property characterization, and the results suggest that the presented method could be more reliable compared to conventional deterministic methods.
The purpose of this work is to present an approach for the probabilistic stability analysis of tunnels considering the heterogeneity of geo-mechanical properties. A stochastic procedure is followed to account for the variability in the rock mass property characterization. The finite difference method is coupled with the Monte Carlo simulation technique to incorporate the randomness of rock mass properties. Moreover, a particular performance function is defined to investigate the excavation serviceability based on the permissible deformations. In order to validate the analysis, the probabilistic and the deterministic results are compared with the in-situ measurements. It can be observed that in both the probabilistic and deterministic analyses the largest displacements occur in the invert. In contrast, the smallest displacements are recorded in the sidewalls. Utilizing the performance function, the probability of failure for the invert, crown, left, and right wall is estimated as 100%, 68.8%, 16.2%, and 20.9%, respectively. Comparing the measured and calculated convergences, it is conjectured that the deterministic analysis underestimates the displacements, while the measured values are very close to the mean values predicted by the probabilistic analysis. The results obtained indicate that the presented approach could be a reliable technique compared to the conventional deterministic method.

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