This invention allows for the prediction of the time of failure in open pit slopes.


Slope Instability Prediction Based on Slope Movement Data

Tech ID: UA20-021

This invention describes a way to enhance current methods of detecting mine slope slippage by using inverse velocity and machine learning techniques to improve the detection of slips before they happen. The invention allows for the prediction of the time of failure in open pit slopes.

Open-pit mining, also known as opencast mining, is a surface mining technique that extracts minerals from an open pit in the ground. Open-pit mining is the most common method used throughout the world for mineral mining and does not require extractive methods or tunnels. Slopes in open pit mines must be considered as geotechnical structures. Therefore, their design and implementation must be conducted with all consideration including technical, economic, environmental and safety issues. Predicting the time of failure is a topic of major concern in the field of geological risk management. Monitoring slopes potentially affected by instability is an activity of fundamental importance in the field of geomechanics. The most popular method for slope stability analysis has been the use of the inverse velocity method developed in 1985. The approach has given the ability for multiple variations of the approach, used by different researchers.

Key Benefits

- Use of not previously explored methods
- Use of new technology, such as machine learning methods
- Solves a consistent problem in open-pit mining
- Helps achieve safety goals


- Mining applications
- Predicting the failure of open pit slopes

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