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FE model of electrical resistivity survey for mixed ground prediction ahead of a TBM tunnel face

Authors
Kang, MinkyuKim, SoojinLee, JunhoChoi, Hangseok
Issue Date
10-5월-2022
Publisher
TECHNO-PRESS
Keywords
electrical resistivity survey; mixed ground; numerical analysis; tunnel boring machine
Citation
GEOMECHANICS AND ENGINEERING, v.29, no.3, pp.301 - 310
Indexed
SCIE
SCOPUS
Journal Title
GEOMECHANICS AND ENGINEERING
Volume
29
Number
3
Start Page
301
End Page
310
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/146641
DOI
10.12989/gae.2022.29.3.301
ISSN
2005-307X
Abstract
Accurate prediction of mixed ground conditions ahead of a tunnel face is of vital importance for safe excavation using tunnel boring machines (TBMs). Previous studies have primarily focused on electrical resistivity surveys from the ground surface for geotechnical investigation. In this study, an FE (finite element) numerical model was developed to simulate electrical resistivity surveys for the prediction of risky mixed ground conditions in front of a tunnel face. The proposed FL model is validated by comparing with the apparent electrical resistivity values obtained from the analytical solution corresponding to a vertical fault on the ground surface (i.e., a simplified model). A series of parametric studies was performed with the FE model to analyze the effect of geological and sensor geometric conditions on the electrical resistivity survey. The parametric study revealed that the interface slope between two different ground formations affects the electrical resistivity measurements during TBM excavation. In addition, a large difference in electrical resistivity between two different ground formations represented the dramatic effect of the mixed ground conditions on the electrical resistivity values. The parametric studies of the electrode array showed that the proper selection of the electrode spacing and the location of the electrode array on the tunnel face of TBM is very important. Thus, it is concluded that the developed FL numerical model can successfully predict the presence of a mixed ground zone, which enables optimal management of potential risks.
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CHOI, HANG SEOK
공과대학 (건축사회환경공학부)
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