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Nonlinear deformation behaviors and a new approach for the classification and prediction of large deformation in tunnel construction stage: a case study.

Authors :
Liu, Weiwei
Chen, Jianxun
Chen, Lijun
Luo, Yanbin
Shi, Zhou
Wu, Yunfei
Source :
European Journal of Environmental & Civil Engineering; Mar2022, Vol. 26 Issue 5, p2008-2036, 29p
Publication Year :
2022

Abstract

Reasonable evaluation and prediction of squeezing condition to avoid large deformation disaster in tunnels have been an important research issue. In order to accurately and rapidly predict the large deformation under complex geological conditions, this paper proposes a classification and prediction method for quick identification of large deformation in tunnel construction stage in a case study of Muzhailing Tunnel. Previous prediction methods of large deformation are usually based on the stages of geological survey and engineering design, and there are great difficulties in obtaining some prediction indexes in soft and fractured strata, such as uniaxial compressive strength. In this paper, the nonlinear deformation behaviors of surrounding rock were analyzed. The geo-stress condition, strata occurrence, rock strength, rock intactness and groundwater condition were chosen as evaluation indexes (including eight sub-indexes). The classification of large deformation was carried out based on the deformation statistics in Muzhailing Tunnel. And the fuzzy prediction was conducted and compared with other methods in engineering. The comparison results show that the new method has higher accuracy and applicability in predicting the large deformation of weak rock mass. This study provides a new approach for rapid identification and prediction of large deformation in tunnel construction stage. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19648189
Volume :
26
Issue :
5
Database :
Complementary Index
Journal :
European Journal of Environmental & Civil Engineering
Publication Type :
Academic Journal
Accession number :
156055447
Full Text :
https://doi.org/10.1080/19648189.2020.1744482