Study on the Acoustic Emission Characteristics and Failure Precursors of Water-Rich Frozen Sandstone under Different Lateral Unloading Rates

Abstract

The artificial freezing method is used to cross the water-rich soft rock strata in order to exploit deep coal resources. At present, studies that consider both freezing effect and unloading rate are insufficient. To study the influences of the excavation rate using the artificial freezing method on the unloading deformation and failure of the water-rich surrounding rock, we carry out mechanical and synchronous acoustic emission (AE) tests on frozen (−10 °C) sandstone samples under different lateral unloading rates. Combined with the AE signals, the stress, strain and failure process are analysed to determine the mechanical behaviours of frozen rock samples under different lateral unloading rates. The damage difference between normal temperature rock and frozen rock during lateral unloading is studied. According to acoustic emission signals, the damage relationships among acoustic emission amplitude, energy, cumulative acoustic emission energy (CAEE), stress and strain were compared and analyzed. In this paper, acoustic emission 3D positioning system is used to monitor the fracture propagation trajectory in the process of unloading confining pressure of frozen sandstone. The results show that the peak stress of frozen sandstone during lateral unloading is about 2.5 times of that at 20 °C. More than 2 AE amplitudes per second are regarded as the precursor of failure (FP), and point FP is taken as the first level warning. The CAEE of rock samples at 20 °C and frozen rock samples shows the same change law over time, increasing slowly before the FP point and exponentially after the FP point. Peak stress increases and axial strain decreases with the increase of unloading rate of frozen rock sample. The CAEE at point FP and the peak acoustic emission energy (AEE) and the CAEE at the time of failure increase when the unloading rate of frozen rock sample increases. Principal component analysis method was used to extract key characteristic energy to obtain a clearer AEE concentration area, which was defined as second-level early warning. The research results can provide guidance for freezing shaft construction to reduce the occurrence of disasters.