Experimental study on the effect of strain rate on rock acoustic emission characteristics. (September 2020) - Liu, Xiling; Liu, Zhou; Li, Xibing; Gong, Fengqiang; Du, Kun
Abstract: To explore the variation in rock acoustic emission (AE) characteristics with strain rate, uniaxial compression tests at different loading rates and impact loading tests were conducted on granite using a MTS322 rock mechanical test system and split Hopkinson pressure bar (SHPB) system, respectively. The effect of the strain rate on the AE characteristic parameters, rock fracture properties, and destruction evolution were systematically analyzed. The results demonstrated that with increasing strain rate, the cumulative AE count decreases as a power function, and the variation between the cumulative AE count and strain rate can be fitted log-linearly with a slope of 0.48. The peak frequencies of the AE signals are mostly distributed in the zones of 0–100 kHz, 175–250 kHz, and 400–550 kHz. The signal proportion in the 0–100 kHz zone gradually increases with strain rate, while the signal proportions in the 175–250 kHz and 400–550 kHz zone exhibit decreasing trends. A transition of a sudden increase in the RA -value and decrease in the AF -value occurs when the stress reaches a certain level, and the stress level corresponding to this transition will increase with strain rate. Meanwhile, the RA–AF distribution is mostly concentrated on the abscissa in the low strain rate tests, but gradually concentrates on the longitudinal axis as the strain rate increases. This indicates that tensile cracking becomes the dominant fracture mode with increasing strain rate. The b -value decreases with increasing strain rate in the uniaxial compression tests; however, the b -value in the impact loading tests is higher than that in the uniaxial compression tests. Furthermore, to distinguish the signals generated by stress wave propagation from the signals generated by rock fracturing in the impact loading tests, a four-parameter k -means algorithm is used to conduct a clustering analysis. The results indicate that the signals can be classified into four clusters: tensile fracturing signals (cluster A), mixed stress wave and shear fracture signals (cluster B), mixed stress wave and tensile fracture signals (cluster C), and stress wave signals (cluster D).