21 research outputs found
Generation and verification of three-dimensional network of fractured rock masses stochastic discontinuities based on digitalization
Minimum rock bolt force and minimum static acceleration in tetrahedral wedge stability: A probabilistic study
Deformation and Failure Mechanism of a Collapse Induced by Underground Mining -- A Study of the Pusa Collapse in Guizhou Province of China
The rock structures at Pusa Village, located in the karst mountainous area of Nayong County, Guizhou Province, China are gently dipping. The upper part of this mountain is composed of hard rock layers while the lower section is made up of soft rocks which includes six (6) coal seams. Mining the coal seams excited the rest of the slope body leading to instability of the upper portions. On August 28, 2017, a massive landslide occurred in this area which resulted in the generation of 82, 500 cubic meters of debris. Significant casualties and social impacts were recorded. Twenty-six (26) fatalities and nine (9) missing people were reported. In this paper, the field engineering, geological conditions and long-term mining activity are investigated. A numerical model is developed to simulate and analyze the failure and deformation process of the Pusa collapse. The failure factors of the Pusa collapse can mainly be attributed to the unique geology of the slope rock mass, underground mining activities, topography and rainfall. The intensified mining activities increased the compressive stress in the upper layers and caused the development of fractures and cracks. The failure process of the Pusa collapse can be summarized into three stages, namely: (1) deformation of the roof layers, (2) cracking on the crest of the slope, (3) rapid deformation and collapse. The upper slope with high strength rocks developed crack—toppling failure while the lower slope with low strength rocks developed crack-slide failure. In summary, the slope deformation and failure mechanism are tensional, toppling, and shear failure
Clinical Experience with the Alwall Disposable Plate Dialyser in Regular Dialysis Treatment
Determination of representative volume element with consideration of linear anisotropy using geostatistics approach
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Hierarchical probabilistic regionalization of volcanism for Sengan region in Japan using multivariate statistical techniques and geostatistical interpolation techniques.
Sandia National Laboratories, under contract to Nuclear Waste Management Organization of Japan (NUMO), is performing research on regional classification of given sites in Japan with respect to potential volcanic disruption using multivariate statistics and geo-statistical interpolation techniques. This report provides results obtained for hierarchical probabilistic regionalization of volcanism for the Sengan region in Japan by applying multivariate statistical techniques and geostatistical interpolation techniques on the geologic data provided by NUMO. A workshop report produced in September 2003 by Sandia National Laboratories (Arnold et al., 2003) on volcanism lists a set of most important geologic variables as well as some secondary information related to volcanism. Geologic data extracted for the Sengan region in Japan from the data provided by NUMO revealed that data are not available at the same locations for all the important geologic variables. In other words, the geologic variable vectors were found to be incomplete spatially. However, it is necessary to have complete geologic variable vectors to perform multivariate statistical analyses. As a first step towards constructing complete geologic variable vectors, the Universal Transverse Mercator (UTM) zone 54 projected coordinate system and a 1 km square regular grid system were selected. The data available for each geologic variable on a geographic coordinate system were transferred to the aforementioned grid system. Also the recorded data on volcanic activity for Sengan region were produced on the same grid system. Each geologic variable map was compared with the recorded volcanic activity map to determine the geologic variables that are most important for volcanism. In the regionalized classification procedure, this step is known as the variable selection step. The following variables were determined as most important for volcanism: geothermal gradient, groundwater temperature, heat discharge, groundwater pH value, presence of volcanic rocks and presence of hydrothermal alteration. Data available for each of these important geologic variables were used to perform directional variogram modeling and kriging to estimate values for each variable at 23949 centers of the chosen 1 km cell grid system that represents the Sengan region. These values formed complete geologic variable vectors at each of the 23,949 one km cell centers