Development of an Algorithm for the Prediction of Slope Failure in Surface Mines

Masood, Maheer M.; Verma, Tikendra Nath; Seervi, Vikram

doi:10.1007/s40033-023-00513-7

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, predicting the precise occurrence of collapse poses a difficult challenge due to its dependence on several elements, such as geological conditions, mine design, Hydrogeological conditions, and the structural characteristics of the rock mass. Nevertheless, by conducting a comprehensive geotechnical study and implementing regular monitoring systems, it becomes feasible to predict the occurrence of collapse based on scientific principles 4 .…”

Section: Introductionmentioning

confidence: 99%

Slope Monitoring Techniques in Opencast Mines: A Review of Recent Advances

Singh,

Masood,

Verma

2024

jmmf

Self Cite

View full text Add to dashboard Cite

The process of excavating rock mass induces changes in the stress distribution within the slope, rendering it prone to deformation over a specific duration. The potential consequence of movements along the weak planes is the ultimate breakdown of the slope. Various monitoring techniques, including visual inspection, laser scanning, Lidar scanning, total stations, Global Positioning Systems (GPS), state-of-the-art radar scanning, and micro-seismic monitoring, are currently employed in mining environments to forecast slope failure and deformation rate. This article will discuss the need to implement a continuous slope monitoring system, including categorizing such systems and an overview of the current state of existing slope monitoring technologies. The paper also discusses the applications of UAVs (Unmanned Aerial Vehicles) in slope monitoring. The research proposes implementing a consistent and continuous slope monitoring strategy grounded on empirical data when planning big and deep opencast mines. This approach is crucial for guaranteeing optimal safety measures and enhanced productivity levels.

show abstract

Section: Introductionmentioning

confidence: 99%