危岩体崩塌灾害监测预警试验研究

岩体崩塌破坏的突发性使其成为最难预防的地质灾害之一, 严重威胁人类的生命财产安全. 边坡岩块体崩塌破坏多是系统不稳定导致的动力破坏, 因此应用动力学指标进行监测预警更为有效. 本研究通过引入多种时域动力学监测指标, 开展了岩体崩塌破坏全过程的监测预警实验研究. 通过振动幅值、峭度指标等时域动力学指标监测, 可有效识别岩体分离破坏前兆现象, 提前55 s实现岩块体崩塌的早期预警. 多个时域动力学指标均可识别岩体破坏前的非协调性动力特征, 其中变异系数在识别这一振荡特征上优势明显, 可通过识别这一震荡特征实现崩塌灾害的早期预警. 此外, 破坏前岩体的振动速度是稳定岩体的2.1倍, 岩体破坏发生时刻赋存较大的冲击能量, 是岩体启程剧动的主要原因之一, 可通过综合分析峭度指标等时域动力学指标, 实现分离破坏前兆更为合理的判识. 本研究不仅为岩体崩塌灾害早期预警提供了新的数据与技术方法支持, 也为崩塌岩体启程剧动机制与破坏后运动特性研究提供了新的启示. 

MONITORING AND EARLY WARNING EXPERIMENT OF ROCK COLLAPSE

Rock collapse has been a hot issue in the study of geological hazards for many years, and it is difficult to prevent because of its sudden disintegration, which is a serious threaten to human life and property safety. The rock collapse is caused by the dynamic failure of the system instability, so it can be more effective to apply the dynamic monitoring index in early warning. By introducing time-domain dynamic monitoring indicators, the whole process of rock collapse is monitored in real time. Through vibration amplitude, kurtosis index and other indexes, the failure precursor in the detachment phase is analyzed. As the early warning method based on detachment precursor recognition has better timeliness in the early warning of rock collapse, it can realize the early warning of rock block collapse 55 s in advance. The experimental results show that the time-domain dynamic index monitoring can identify the obvious incompatible dynamic response before rock failure, and the variation coefficient has obvious advantages in identifying the oscillation feature. The early warning of collapse disaster can be realized by identifying this oscillation feature. Furthermore, the vibration velocity of rock mass before the collapse is 2.1 times that in the stable phase, and the large impact energy of rock mass at the time of failure is one of the main reasons for the high-speed rockslide. By analyzing the kurtosis index and other time-domain dynamic indexes, the reasonable identification of the failure precursors can be achieved. The study provides new data support and enlightenment for the early warning of rock collapse, the mechanism of rock collapse and the rock movement characteristics after failure.