Quantitative Risk Assessment for Deep Tunnel Failure Based on Normal Cloud Model: A Case Study at the ASHELE Copper Mine, China

The spatial and temporal distribution of tunnel failure is very complex due to geologic heterogeneity and variability in both mining processes and tunnel arrangement in deep metal mines. In this paper, the quantitative risk assessment for deep tunnel failure was performed using a normal cloud model at the Ashele copper mine, China. This was completed by considering the evaluation indexes of geological condition, mining process, and microseismic data. A weighted distribution of evaluation indexes was determined by implementation of an entropy weight method to reveal the primary parameters controlling tunnel failure. Additionally, the damage levels of the tunnel were quantitatively assigned by computing the degree of membership that different damage levels had, based on the expectation normalization method. The methods of maximum membership principle, comprehensive evaluation value, and fuzzy entropy were considered to determine the tunnel damage levels and risk of occurrence. The application of this method at the Ashele copper mine demonstrates that it meets the requirement of risk assessment for deep tunnel failure and can provide a basis for large-scale regional tunnel failure control in deep metal mines.

Automated summary

This study conducted a quantitative risk assessment for deep tunnel failure using a normal cloud model and entropy weight method, quantifying tunnel damage levels through the expectation normalization method. It identified primary parameters controlling tunnel failure, and determined tunnel damage levels and occurrence risk using the maximum membership principle, comprehensive evaluation value, and fuzzy entropy. The application of this method at the Ashele copper mine meets the requirements of risk assessment for deep tunnel failure, providing a basis for large-scale regional tunnel failure control in deep metal mines.