Mechanism of subsidence-buckling and instability of slopes in thick-layered rigid rock under mining

The deformation and failure of mining slopes in layered rocks predominantly result from shear landslides.However,the instability process of the Pusa rock avalanche in Guizhou,China,revealed a unique damage phenomenon:prominent breaking and toppling of rock blocks occurred in the central section of the mountain,with a lack of commonly observed shear landslide features.This paper aims to investigate the underlying reasons behind this distinctive damage pattern.The study employs various methods including geological survey,UAV aerial survey,physical simulation,and discrete element numerical simulation.The findings indicate that the geological conditions,characterized by a hard upper layer and a soft lower layer along with underground mining activities,play a significant role in triggering the landslide.Furthermore,the presence of a columnar structured rock mass emerges as the primary factor influencing the instability of the Pusa rock avalanche.To elucidate the mining failure mechanism of the rock mass with vertical joints,we propose a"subsidence-buckling"failure model.Following the subsidence and collapse of the roof rock mass in the goaf,the columnar rock mass in the upper and middle portions of the slope undergoes deflection and deformation,forming a three-hinged arch structure.This structural configuration converts the pressure exerted by the overlying rock mass into both vertical pressure and lateral thrust.Under the influence of external loads,the slope experiences buckling failure,ultimately leading to instability upon fragmentation.By shedding light on these findings,this study contributes to a better understanding of the spatiotemporal evolution of mining slope fractures and their impact on slope stability.

Discrete rough set analysis of two different soil-behavior-induced landslides in National Shei-Pa Park,Taiwan

The governing factors that influence landslide occurrences are complicated by the different soil conditions at various sites.To resolve the problem,this study focused on spatial information technology to collect data and information on geology.GIS,remote sensing and digital elevation model（DEM） were used in combination to extract the attribute values of the surface material in the vast study area of SheiPa National Park,Taiwan.The factors influencing landslides were collected and quantification values computed.The major soil component of loam and gravel in the Shei-Pa area resulted in different landslide problems.The major factors were successfully extracted from the influencing factors.Finally,the discrete rough set（DRS） classifier was used as a tool to find the threshold of each attribute contributing to landslide occurrence,based upon the knowledge database.This rule-based knowledge database provides an effective and urgent system to manage landslides.NDVI（Normalized Difference Vegetation Index）,VI（Vegetation Index）,elevation,and distance from the road are the four major influencing factors for landslide occurrence.The landslide hazard potential diagrams（landslide susceptibility maps） were drawn and a rational accuracy rate of landslide was calculated.This study thus offers a systematic solution to the investigation of landslide disasters.