Rockburst plays a serious threat to personnel and equipment during underground engineering construction. The study of the rockburst mechanism is helpful to its prediction and prevention. Based on the characteristic analysis of a large number of rockburst cases, Li et al. proposed three stress-type and three stress-structural rockbursts and obtained the geological characteristics and occurrence criteria of these rockbursts, but the evolution process of rockbursts is still unclear. Based on the continuous-discontinuous element method, the characteristics of failure process, surrounding rock stress, motion, and energy of three stress-rockburst blocks are analyzed. The results show that rockburst failure generally goes through several stages, such as a few surfaces tensile failure, shallow shear failure, deep extension of tensile failure, shear failure communication, and rockburst occurrence. The total volume of rockburst blocks and the main distribution intervals of block diameters for different types of rockbursts are quite different, which are mainly affected by stress state and geological structure. The ejection velocity of the small block is always higher than that of the large block during the same one rockburst simulation, and the ejection velocity of the small block is from the surface. In the process of rockburst, not only the elastic strain energy is released but also the elastic strain energy is accumulated. The greater the rockburst intensity, the more the elastic strain energy is released, and the steeper the prepeak curve of elastic strain energy. The research results provide a reference for further understanding the mechanism of rockburst and lay a theoretical basis for the prevention and control of rockburst in underground engineering.

A new rock-breaking technique that harnesses the devastating power of underground rockbursts could revolutionize mining, tunneling, and construction by shattering the hardest rock without explosives.

In an era defined by technological advancement and complex engineering challenges, understanding the phenomena associated with rockbursts remains a critical endeavor. Rockbursts, sudden and violent releases of energy from rock formations, pose significant risks not only to underground workers but also to the stability of mining operations. The recent research by Cai, Chen, Rui, and their colleagues, published in the prestigious journal Natural Resources Research, delves into the intricate field of rockburst severity prediction under few-shot learning conditions using a cutting-edge feature fusion approach.

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