4.7 Article

Ground movements modeling applying adjusted influence function

出版社

ELSEVIER
DOI: 10.1016/j.ijmst.2020.01.007

关键词

Mining operation; Modeling of surface deformations; Stochastic models; Probability integration method; Influence function; Salt hard coal mining copper ore mining

资金

  1. national key project The Belt and Roadtalent recruitment project named: Comparison of Mining Subsidence Research in China and Poland [G2017001]
  2. Grant for Statutory Research AGH-University of Science and Technology in Krakow, Poland [16.16.150.545]

向作者/读者索取更多资源

Mathematical modeling of surface deformations caused by underground mining operation is commonly carried out with use of empirical, numerical or stochastic models. One of the most frequently applied model for prediction of ground deformation in many countries is Knothe model. The model developed by Knothe belongs to the stochastic methods and is based on the influence function. In China a prediction method named Probability Integration Method (PIF) was established by Liu Baochen and Liao Guohua based on the stochastic medium theory. Modified version of that model allows to predict ground movements caused by mining operation in extremely complex technical and geological conditions. That model is commonly applied for coal, metal ore and salt deposits. The article presents several modifications of the mathematical model used in China and Poland. This model is very widespread in the world, therefore the generalizations proposed in the article can be implemented for the purposes of prediction surface deformations for various types of deposits in many countries. The presented generalizations were then tested on specific examples of coal mining, copper ore mining and rock salt deposit. The obtained results indicate high efficiency of methods based on the influence function in complex geological and mining conditions. (C) 2020 Published by Elsevier B.V. on behalf of China University of Mining & Technology.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据