Core samples at 6 levels below −930m in the new shaft of Xincheng Gold Mine were collected in this case study, and eight evaluation indexes were selected in terms of lithology condition, stress condition and rock condition. It was adopted the entropy method and the technique for order preference by similarity to ideal solution (TOPSIS method) to establish a multi-index rock burst comprehensive prediction model. The stress and elastic strain energy distribution of surrounding rock after shaft excavation were analyzed by using numerical simulation method. Combined with the triaxial test results and the seismology theory, the rock burst risk after shaft excavation was analyzed from the perspective of energy. The results show that:(1) rock burst risk doesn't increase linearly with depth, but is the result of many factors such as rock property, rock mass integrity, in-situ stress environment and energy accumulation caused by excavation;(2)the shaft excavation in deep of Xincheng Gold Mine has strong rock burst risk, it is necessary to take monitoring and pressure relief measures; it is suggested to change the traditional technology “short excavation height and short support height” and adopted the method of flexible yieldable support to control rock burst.

1. Introduction

with the increasing of shaft excavation depth, the in-situ stress in the surrounding rock mass increases. Under the condition of high stress and strong excavation unloading, it leads to the rapid stress releasing and the high stress concentration in transverse or longitudinal direction in the local surrounding rock. Once the compressive shear stress on the surface of the surrounding rock exceeds its strength, it will produce fracture, fragmentation, plastic expansion, rock burst etc. Meanwhile, rock burst has a great impact on the shaft construction safety. At present, the rock burst mechanism is still not clear, so rock burst prediction is very complicated. Researchers who main study rock burst on the aspects of strength (Peng and Wang, 1996), stiffness, stability (Zhang and Fu, 2008), damage (Zuo et al.,2005) mutation (Fei et al.,1995), fractal (He et al.,2018) and energy (Xie et al., 2005), have paid much attention. In rock burst prediction and analysis, some modern methods are mainly used, such as finite element calculation, fuzzy comprehensive evaluation and artificial neural network. A large number of engineering examples show that rock burst is a very complex dynamic instability phenomenon, which involves many influence factors, including internal factors such as rock fabric, strength and other physical and mechanical properties, and external factors such as stress and energy changes caused by excavation as well.

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