Geomechanical analysis plays a major role in providing a safe working environment in an active mine. Geomechanical analysis includes but is not limited to providing active monitoring of pit walls and predicting slope failures. During the analysis of a slope failure, it is essential to provide a safe prediction, that is, a predicted time of failure prior to the actual failure. Modernday monitoring technology is a powerful tool used to obtain the time and deformation data used to predict the time of slope failure. This research aims to demonstrate the use of machine learning (ML) to predict the time of slope failures. Twenty-two datasets of past failures collected from radar monitoring systems were utilized in this study. A two-layer feed-forward prediction network was used to make multistep predictions into the future. The results show an 86% improvement in the predicted values compared to the inverse velocity (IV) method. Eighty-two percent of the failure predictions made using ML method fell in the safe zone. While 18% of the predictions were in the unsafe zone, all the unsafe predictions were within five minutes of the actual failure time, all practical purposes making the entire set of predictions safe and reliable.
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.
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