scholarly journals Study on the Instability Mechanism and Grouting Reinforcement Repair of Large-Scale Underground Stopes

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chengyu Xie ◽  
Nan Jia ◽  
Liwen He
Keyword(s):  
Large Scale ◽  
Slip Surface ◽  
Underground Mining ◽  
Diffusion Mechanism ◽  
Combination Method ◽  
Chain Process ◽  
Maximum Shear Strain ◽  
Analysis Theory ◽  
Multiphysics Coupling ◽  
Grouting Reinforcement

The environmental conditions due to unreasonable mining in underground stopes, the slurry diffusion mechanism in the grouting reinforcement of a stope within its influence, the causes of large-scale instability collapse, and the catastrophic stope process are analyzed, and limit upper line analysis theory and numerical analysis methods are comprehensively adopted, revealing the continuous catastrophic collapse mode of large-scale underground stopes. The method of determining the stope instability collapse boundary and the slip surface within the range based on the theory of the maximum shear strain increment is proposed, and the diffusion radius and range of the grouting slurry during the reinforcement process, which considers the multifield coupling factors, are obtained. The results show that the U-shaped hidden danger area formed after the collapse of the large-scale underground stope. The influence range reaches six adjacent stopes, which are symmetrically distributed around the collapse; the mining instability is manifested as a catastrophic chain process of stress change, energy accumulation, state change, and instability collapse. The damage mode of instability collapse is a combination method of wedge collapse, circular arc rotation, triangular translation, and strip slip. According to the multiphysics coupling numerical calculation, the diffusion radius of the grouting slurry is 12 m, exhibiting an elliptical distribution. The research results can be used to comprehensively control the underground mining environment, thus effectively solving the safety problems faced by tunnel or roadway excavations above the goaf.

Analysis of Slope Failure Mechanisms Considering Micropile-Soil Interaction

2013 ◽  
Vol 535-536 ◽  
pp. 565-568 ◽  
Author(s):  
Hong Jian Liao ◽  
Cheng Lin Tian ◽  
Hang Zhou Li
Keyword(s):  
Large Scale ◽  
Slope Failure ◽  
Slip Surface ◽  
Loess Soil ◽  
Scale Model ◽  
Maximum Shear Strain ◽  
Loess Slope ◽  
Slip Surfaces ◽  
Large Scale Model ◽  
Stress And Deformation

A large scale model test was carried out in loess slope, in which the stress and deformation characteristics of slopes reinforced with different arrangements of micropiles were studied. The mechanism of the micropile-soil interaction and the reinforcement effect of micropiles in loess slope were analysed. Based on the scale of in-situ loess slope and the physical mechanics parameters of loess soil, a numerical model was established by using finite difference method. For a reasonable arrangement of micropiles in step-shaped slope, the critical slip surfaces were determined considering the influence of slope inclination, ratio of step height and loading position. The micropiles were arranged in the step-shaped slope based on the critical slip surface, and the relationship between the ultimate bearing capacity of slope and shear strength parameters of loess soil was studied. The maximum shear strain of micropile-soil and moment of micropiles were calculated, and then the mechanism of the micropile-soil interaction was analysed.

A Drug Target Interaction Prediction Based on LINE-RF Learning

2020 ◽  
Vol 15 (7) ◽  
pp. 750-757
Author(s):  
Jihong Wang ◽  
Yue Shi ◽  
Xiaodan Wang ◽  
Huiyou Chang
Keyword(s):  
Network Topology ◽  
Drug Target ◽  
Large Scale ◽  
Representation Learning ◽  
New Drugs ◽  
Combination Method ◽  
Learning Methods ◽  
Network Representation ◽  
On Line ◽  
Clinical Experiments

Background: At present, using computer methods to predict drug-target interactions (DTIs) is a very important step in the discovery of new drugs and drug relocation processes. The potential DTIs identified by machine learning methods can provide guidance in biochemical or clinical experiments. Objective: The goal of this article is to combine the latest network representation learning methods for drug-target prediction research, improve model prediction capabilities, and promote new drug development. Methods: We use large-scale information network embedding (LINE) method to extract network topology features of drugs, targets, diseases, etc., integrate features obtained from heterogeneous networks, construct binary classification samples, and use random forest (RF) method to predict DTIs. Results: The experiments in this paper compare the common classifiers of RF, LR, and SVM, as well as the typical network representation learning methods of LINE, Node2Vec, and DeepWalk. It can be seen that the combined method LINE-RF achieves the best results, reaching an AUC of 0.9349 and an AUPR of 0.9016. Conclusion: The learning method based on LINE network can effectively learn drugs, targets, diseases and other hidden features from the network topology. The combination of features learned through multiple networks can enhance the expression ability. RF is an effective method of supervised learning. Therefore, the Line-RF combination method is a widely applicable method.

scholarly journals An Experimental Study of Horizontal Bearing Capacity of Vertical Steel Floral Tube Micropiles with Double Grouting

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Kaiyang Wang ◽  
Yanjun Shang
Keyword(s):  
Bearing Capacity ◽  
Large Scale ◽  
Slip Surface ◽  
Bending Moment ◽  
Shear Capacity ◽  
Soil Reinforcement ◽  
Scale Model ◽  
Soil Pressure ◽  
Floral Tube ◽  
Grouting Pressure

This paper examines the performance of a novel technology, vertical steel floral tube micropiles with double grouting. It is the combination of micropile technology and double grouting technology. A large-scale model tank was applied to impart horizontal bearing capacity, and the slope soil pressure and flexural performance of the micropile were investigated under four experimental conditions. The peak grouting pressure during the double grouting process was defined as the fracturing pressure of the double grouting, and it was positively correlated to the interval time between first grouting and secondary grouting. Compared with traditional grouting, double grouting increased the horizontal bearing capacity of the single micropile with the vertical steel floral tube by 24.42%. The horizontal bearing capacity was also 20.25% higher for the structure with three micropiles, compared with a 3-fold value of horizontal sliding resistance. In the test, the maximum bending moment acting on the pile above the sliding surface was located 2.0–2.5 m away from the pile top, and the largest negative bending moment acting on the pile below the slip surface was located 4.0 m away from the pile top. The ultimate bending moment of the single pile increased by 12.8 kN·m with double grouting, and the bending resistance increased by 96.2%. The experimental results showed that the double grouting technology significantly improved the horizontal bearing capacity of the micropile with the steel floral tube, and the soil reinforcement performance between piles was more pronounced. Also, the shear capacity and the flexural capacity were significantly improved compared with the original technology.

scholarly journals Reaction Diffusion and Chemotaxis for Decentralized Gathering on FPGAs

2009 ◽  
Vol 2009 ◽  
pp. 1-15 ◽  
Author(s):  
Bernard Girau ◽  
César Torres-Huitzil ◽  
Nikolaos Vlassopoulos ◽  
José Hugo Barrón-Zambrano
Keyword(s):  
Large Scale ◽  
Complex Dynamics ◽  
Diffusion Mechanism ◽  
Reaction Diffusion ◽  
Fpga Implementation ◽  
Cellular Slime Mold ◽  
Reaction Diffusion Model ◽  
Cellular Slime ◽  
Computational Resources ◽  
Embedded Implementation

We consider here the feasibility of gathering multiple computational resources by means of decentralized and simple local rules. We study such decentralized gathering by means of a stochastic model inspired from biology: the aggregation of theDictyostelium discoideumcellular slime mold. The environment transmits information according to a reaction-diffusion mechanism and the agents move by following excitation fronts. Despite its simplicity this model exhibits interesting properties of self-organization and robustness to obstacles. We first describe the FPGA implementation of the environment alone, to perform large scale and rapid simulations of the complex dynamics of this reaction-diffusion model. Then we describe the FPGA implementation of the environment together with the agents, to study the major challenges that must be solved when designing a fast embedded implementation of the decentralized gathering model. We analyze the results according to the different goals of these hardware implementations.

Prediction of the Ground Vibration Rate during Large-scale Explosions in the Underground Conditions

2021 ◽  
pp. 41-45
Author(s):  
V.N. Tyupin ◽  
Keyword(s):  
Rock Mass ◽  
Large Scale ◽  
Underground Mining ◽  
Calculated Data ◽  
Ground Vibration ◽  
Open Pit ◽  
Vibration Velocity ◽  
Earth Surface ◽  
Seismic Safety ◽  
The Earth

At present, to ensure seismic safety in massive explosions, the analytical dependence of the determination of the vibration velocity of M.A. Sadovsky rock mass is mainly used. This dependence is widely used in the creation of seismic-safe technologies for mineral deposits open-pit and underground mining. However, scientific research and production experience showed that the rate of oscillation depends on the energy parameters of the explosive, the diameter and length of its charges, the number of simultaneously exploded charges, the number of deceleration stages, the deceleration interval, etc. The purpose of this article is to predict the speed fluctuations of the massif on the earth surface when conducting the underground explosions depending on the parameters of large-scale explosions and physical-technical properties of the rock masses in the areas of explosion of the protected object. The formulas for calculating the velocity of rock mass on the earth surface during large-scale explosions in the underground conditions are substantiated and presented. The formulas were used for calculating the vibration velocities of the rock mass on the earth surface in accordance with the parameters of drilling and blasting operations during large-scale explosions in the mines of GK VostGOK. Comparison of theoretical (calculated) data and the results of actual measurements indicates their convergence. By changing the controlled parameters in the calculation formulas, it is possible to quantitatively reduce the seismic effect of a large-scale explosions on the protected objects. Further research will be aimed at studying the influence of tectonic faults, artificial contour crevices, filling massif or mined-out space on the rate of seismic-explosive vibrations during blasting operations in the mines. The research results can be used in the preparation of rules for conducting large-scale explosions at the underground mining.

Analisys of mud inrush water sources into underground working of the Sokolovskoe ore deposit

2020 ◽  
pp. 56-67
Author(s):  
E. Yu. Efremov
Keyword(s):  
Hydraulic Conductivity ◽  
Large Scale ◽  
Underground Mining ◽  
Temporal Distribution ◽  
High Water ◽  
The North ◽  
Initial Block ◽  
Groundwater Supply ◽  
Spatio Temporal ◽  
Inrush Water

There is a serious threat of groundwater inrush from overlying sedimentary layers for underground mining. When ore is extracted using block caving method, the area of overburden collapse over ore zone disrupts the natural structure of high hydraulic-conductivity and low hydraulic-conductivity layers. This process creates conditions for the accumulation and transfer of groundwater to mine workings, which lead to accidents, up to disastrous proportions. The research aim is to determine the spatio-temporal distribution of mud inrushes, and to identify groundwater supply sources of inrushes to reduce the geotechnical risks of underground mining in Sokolovskaya mine. Research methods include localization, classification, and analysis of monitoring data, comparison of mud inrushes distribution with geostatistical parameters of the main aquifers.The majority of large-scale accidents caused by mud inrushes are confined to the central and northern area of caved rock zone. The most risky stage of the ore body extraction is the initial block at the lower extraction level. The sources of water supply for the majority of the mud inrushes are high water level areas of the Cretaceous aquifer to the north and west of the mine. Rational targeted drainage aimed at draining the identified areas of the aquifer is the best way to reduce the risk of accidents.

Tracking the Land Use/Land Cover Change in an Area with Underground Mining and Reforestation via Continuous Landsat Classification

2019 ◽  
Vol 11 (14) ◽  
pp. 1719 ◽  
Author(s):  
Jiaxin Mi ◽  
Yongjun Yang ◽  
Shaoliang Zhang ◽  
Shi An ◽  
Huping Hou ◽  
...  
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Land Cover ◽  
Large Scale ◽  
Underground Mining ◽  
Mining Area ◽  
Random Forest Classifier ◽  
Land Use Land Cover ◽  
Lulc Change ◽  
Mining Areas

Understanding the changes in a land use/land cover (LULC) is important for environmental assessment and land management. However, tracking the dynamic of LULC has proved difficult, especially in large-scale underground mining areas with extensive LULC heterogeneity and a history of multiple disturbances. Additional research related to the methods in this field is still needed. In this study, we tracked the LULC change in the Nanjiao mining area, Shanxi Province, China between 1987 and 2017 via random forest classifier and continuous Landsat imagery, where years of underground mining and reforestation projects have occurred. We applied a Savitzky–Golay filter and a normalized difference vegetation index (NDVI)-based approach to detect the temporal and spatial change, respectively. The accuracy assessment shows that the random forest classifier has a good performance in this heterogeneous area, with an accuracy ranging from 81.92% to 86.6%, which is also higher than that via support vector machine (SVM), neural network (NN), and maximum likelihood (ML) algorithm. LULC classification results reveal that cultivated forest in the mining area increased significantly after 2004, while the spatial extent of natural forest, buildings, and farmland decreased significantly after 2007. The areas where vegetation was significantly reduced were mainly because of the transformation from natural forest and shrubs into grasslands and bare lands, respectively, whereas the areas with an obvious increase in NDVI were mainly because of the conversion from grasslands and buildings into cultivated forest, especially when villages were abandoned after mining subsidence. A partial correlation analysis demonstrated that the extent of LULC change was significantly related to coal production and reforestation, which indicated the effects of underground mining and reforestation projects on LULC changes. This study suggests that continuous Landsat classification via random forest classifier could be effective in monitoring the long-term dynamics of LULC changes, and provide crucial information and data for the understanding of the driving forces of LULC change, environmental impact assessment, and ecological protection planning in large-scale mining areas.

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