Research on Numerical Analysis of Landslide Cataclysm Mechanism and Reinforcement Treatment Scheme in ShengLi Open-Pit Coal Mine

Author(s):  
Yanbo Zhang ◽  
Zhiqiang Kang ◽  
Chunhua Hou
2019 ◽  
Vol 4 (2) ◽  
pp. 70-74
Author(s):  
Viktória Mikita ◽  
Balázs Kovács

In this study we investigated the hydrogeological problems of an open-pit brown coal mine in the Borsod coal basin with Processing Modflow software. The coal mine is located in the valley of the Sajó-river with high transmissivity overburden layer where the traditional dewatering solutions were not encouraging due to inrush risks and low cost-efficiency. A new way of barrier forming was found out and numerically simulated to prove the efficiency of the solution. Since there are several contaminated sites in the surroundings it was a key factor to assure that the new mine dewatering technique has only a negligible effect on the groundwater regime that undisturbs the known contaminant plumes nearby.


2011 ◽  
Vol 383-390 ◽  
pp. 7697-7701
Author(s):  
Zhi Qiang Kang ◽  
Ying Chun Wang ◽  
Feng Nan ◽  
Shi Lin Yuan ◽  
Jiu Lin Yang

This article according to the ShengLi Open-pit Coal Mine landslide and the slope project special details, used the FLAC numerical calculus analysis software to conduct the research to the landslide cataclysm mechanism, has carried on the optimized analysis to the reinforcement plan.Has obtained the pre-stressed anchor rope frame beam + high pressure splitting grouting reinforcement plan government landslide most superior processing plan through the numerical calculus, thus active control ShengLi Open-pit Coal Mine slope distortion destruction.


2012 ◽  
Vol 599 ◽  
pp. 272-277 ◽  
Author(s):  
Zhi Bin Liu ◽  
Xiao Wei Yang

This paper used RBF artificial neural network to evaluate the underground water contaminated by the leachate of waste dump of open pit coal mine of Xinqiu in Fuxin. Firstly, with the advantages of neural network method in dealing with nonlinear problem, the RBF neural network model was built. Then, the normalized standard matrix was taken as training sample and the MATLAB software was used to train the training sample. Finally, the monitoring data were taken as test samples and were inputted in the RBF neural network model to evaluate the groundwater quality of study area. At the same time, the concept of degree of membership was adopted in the result making it more objective and accurate. The result shows that the ground water of this mining is seriously polluted, class of its pollution is Ⅳ-Ⅴ.The method with strong classification function and reliable evaluation results is simple and effective, and can be widely applied in all kinds of water resources comprehensive evaluation.


Author(s):  
Jiachen Wang ◽  
Wenhui Tan ◽  
Shiwei Feng ◽  
Rudi Zhou

2011 ◽  
Vol 5 ◽  
pp. 1116-1120 ◽  
Author(s):  
CHU Daozhong ◽  
ZHU Qingli ◽  
WANG Jie ◽  
ZHAO Xiaozhi

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