Comprehensive safety evaluation method of surrounding rock during underground cavern construction

The deformation instability of surrounding rock and the collapse of rock blocks are two common failure modes observed during the construction of underground caverns. Therefore, a comprehensive safety evaluation method (CSEM) of surrounding rock for application during underground cavern construction is presented in this paper. The method can be used to evaluate the deformation stability of surrounding rock and predict the collapse of rock blocks, rapidly. First, a deformation stability prediction and evaluation method (DSPEM) for rock mass is established. It combines the safety evaluation method based on a deformation statistical analysis and an inverse deformation prediction method using measured deformation data of surrounding rock. This approach possesses the unique characteristics of complementary and mutual verification. In addition, a comprehensive early-warning index system (CWIS) for assessing the deformation stability of surrounding rock is presented. It consists of three types of warning indicators and three levels of warning values. The early-warning values can be adjusted reasonably by the prediction correction method according to the measured displacements. For the collapse of rock blocks controlled by structural surfaces, a rapid prediction method of rock block collapse (RPMBC) based on the block theory is then presented. Finally, combining the DSPEM, RPMBC, and CWIS, the CSEM of surrounding rock for assessing the safety of cavern construction is established. It can provide technical support for decision making on the safety of surrounding rock during the construction of underground caverns. (C) 2019 Tongji University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.

Automated summary

This paper presents a comprehensive safety evaluation method for surrounding rock during underground cavern construction, including deformation stability and rock block collapse prediction. The method combines complementary and mutually verifying characteristics, along with a comprehensive early-warning index system.