Abstract
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. Modern-day 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.
Highlights
Monitoring slope stability is an essential requirement in the field of geomechanics due to the potential threat a moving slope can cause to the workers or the business
Based on the results below, we can see that minimum inverse velocity (MIV) method results in a 75% improvement in slope failure predictions
Because the time series in the training set have a relatively similar form, we surmised that machine learning (ML) would provide prediction values that are closer to the real time of failure
Summary
Monitoring slope stability is an essential requirement in the field of geomechanics due to the potential threat a moving slope can cause to the workers or the business. Slope stability is an important concern for mining and civil engineers that deal with man-made slopes such as open-pit walls, dams, embankments of highways and railways, and hills. The causes of instability are often complex and creep theory is used in the design of rock slopes. The complexity of the causes of slope movement makes the time of slope failure prediction challenging. The use of modern monitoring technologies has helped engineers better prepare for the outcomes of slope failures in open-pit mines [1]. Many attempts have been made to develop a method to predict the time of failure. Factors affecting slope instabilities such as ground conditions, physical and geomorphological processes, and human activities cannot be determined on a continuous basis, making it challenging to predict the time of slope failure accurately [2]. Instead of developing a phenomenological model of slope failure, practitioners have relied on a detailed analysis of slope deformation [3]
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