Abstract
Surface movement and deformation induced by underground coal mining causes slopes to collapse. Global Navigation Satellite System (GNSS) real-time monitoring can provide early warnings and prevent disasters. A stability analysis of high-steep slopes was conducted in a long wall mine in China, and a GNSS real-time monitoring system was established. The moving velocity and displacement at the monitoring points were an integrated response to the influencing factors of mining, topography, and rainfall. Underground mining provided a continuous external driving force for slope movement, the steep terrain provided sufficient slip conditions in the slope direction, and rainfall had an acceleration effect on slope movement. The non-uniform deformation, displacement field, and time series images of the slope body revealed that ground failure was concentrated in the area of non-uniform deformation. The non-uniform deformation was concentrated ahead of the working face, the speed of deformation behind the working face was reduced, the instability of the slope body was increased, and the movement of the top of the slope was larger than at the foot. The high-steep slope stability in the mine was influenced by the starting deformation (low stability), iso-accelerated deformation (increased stability), deformation deceleration (reduced stability), and deformation remaining unchanged (improved stability).
Highlights
Natural disasters, such as earthquakes, tsunamis, volcanoes, landslides, ground subsidence, debris flows, and pit collapses, can be extremely harmful to human societies
The Three Gorges Dam is in internationally renowned engineering project because of its high water storage capacity, which has resulted in changes in the groundwater level
This has been followed by the application of a variety of predictive models, such as the grey model (GM), polynomial regression model, machine forest model, logical regression model, strength reduction method, and back propagation (BP) neural network [13,14,15,16]
Summary
Natural disasters, such as earthquakes, tsunamis, volcanoes, landslides, ground subsidence, debris flows, and pit collapses, can be extremely harmful to human societies. Previous attempts to develop slope failure early warning systems have mainly been conducted through field surveys and investigations, studies of stratigraphic geomorphology to statistically analyze the possibility of a landslide occurrence, or through the installation of observation stations, displacement meters, and depth meters to obtain monitoring data This has been followed by the application of a variety of predictive models, such as the grey model (GM), polynomial regression model, machine forest model, logical regression model, strength reduction method, and back propagation (BP) neural network [13,14,15,16]. In In thisthis study, a GNSS real-time monitoring system was was usedused for the assess the dynamic dynamic deformation of the rock overlying and the high-steep slopewall in long wall mining.
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