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Fusion of Hydraulic Tomography and Displacement Back Analysis for Underground Cavern Stability Investigation
Author(s) -
Gao Xu,
Yeh TianChyi Jim,
Yan EChuan,
Wang Wenke,
Cai JingSen,
Liang Yue,
Ge Jian,
Qi Yueming,
Hao Yonghong
Publication year - 2018
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2018wr023296
Subject(s) - hydraulic conductivity , displacement (psychology) , geotechnical engineering , stability (learning theory) , kriging , probability density function , geology , estimator , probability distribution , deformation (meteorology) , soil science , mathematics , computer science , statistics , psychology , machine learning , psychotherapist , oceanography , soil water
This paper first elucidates the importance of hydraulic heterogeneity of a rock mass on the design of an underground water‐sealed storage cavern. It then introduces an information fusion approach that integrates hydraulic tomography, displacement back analysis, and a stochastic successive linear estimator to map spatially varying hydraulic conductivity ( K s ), Young's modulus ( E ′ ), cohesion ( c ′ ), and internal friction angle ( ϕ ′ ). This algorithm yields statistically unbiased estimate of the K s , E ′ , c ′ , and ϕ ′ fields and their uncertainty due to insufficient samples of their heterogeneity. Afterward, this uncertainty is translated into the probability of failure distribution around the cavern for the risk assessment. Results show that heterogeneity of K s plays an important role in the deformation and failure of the cavern. They also show that the proposed approach can reveal more detailed parameter distributions than kriging approach, based on a large number of samples of the parameters before excavations. Further, the predicted probability of failure distribution based on the proposed approach can effectively reflect the actual failure locations.