In a mining operation, any noticeable instability can pose a catastrophic threat to the lives of workers. Slope instability can also disrupt the chain of production in a mine, resulting in a loss to the business. Due to the potential threat associated with rock mass movement, it is necessary to be able to predict the time of slope failure. In the past couple of decades, innovations in slope monitoring equipment have made it possible to scan a broad rock face in a short period of time with sub-millimeter accuracy. The data collected from instruments such as Slope Stability Radar (SSR) are commonly used for slope failure predictions, however, it has been challenging to find a method that can provide the time of failure accurately. The aim of this paper is to demonstrate the use of different methods to optimize slope failure predictions. Various methods investigated for research presented in this article include: Minimum Inverse Velocity (MIV), Maximum Velocity (MV), Log Velocity (LV), Log Inverse Velocity (LIV), and Spline regression (SR). Based on the different methods investigated, the Minimum Inverse Velocity method provided the most consistent and accurate results. The use of MIV method resulted in about 75% better predictions than the other methods.
Key words: Monitoring, slope failure, slope instabilities, slope movement, rock failure.
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