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A new approach to predicting mining induced surface subsidence
作者姓名:丁德馨  张志军  毕忠伟
作者单位:School of Resources and Safety Engineering Central South University,School of Resources and Safety Engineering,Central South University,School of Resources and Safety Engineering,Central South University,Changsha 410083,China,School of Architectural Resources and Environment Engineering,Nanhua University Hengyang 421001,China,Changsha 410083,China,School of Architectural Resources and Environment Engineering,Nanhua University Hengyang 421001,China,Changsha 410083,China,School of Architectural Resources and Environment Engineering,Nanhua University Hengyang 421001,China
基金项目:中国科学院资助项目;湖南省自然科学基金;湖南省教育厅资助项目
摘    要:1 INTRODUCTIONMining induced surface subsidence often re-sults in various kinds of damages to the structuresandinfrastructures in the subsidence area1 4]. Thepipes will be broken and fractured,the buildingswill be caused to tilt or collapse and the roadfoun-dation and acequia will be damaged because of thesubsidence . Especially in the case of open stopemining under hard rock formation,this subsidencewill suddenly occurr . For example , at about 11p.m.on December 27 ,1999 ,large scale of…

收稿时间:25 November 2005
修稿时间:25 December 2005

A new approach to predicting mining induced surface subsidence
Ding De-xin , Zhang Zhi-jun and Bi Zhong-wei.A new approach to predicting mining induced surface subsidence[J].Journal of Central South University of Technology,2006,13(4):438-444.
Authors:Ding De-xin  Zhang Zhi-jun and Bi Zhong-wei
Affiliation:(1) School of Resources and Safety Engineering, Central South University, 410083 Changsha, China;(2) School of Architectural, Resources and Environment Engineering, Nanhua University, 421001 Hengyang, China
Abstract:There are many parameters influencing mining induced surface subsidence. These parameters usually interact with one another and some of them have the characteristic of fuzziness. Current approaches to predicting the subsidence cannot take into account of such interactions and fuzziness. In order to overcome this disadvantage, many mining induced surface subsidence cases were accumulated, and an artificial neuro fuzzy inference system(ANFIS) was used to set up 4 ANFIS models to predict the rise angle, dip angle, center angle and the maximum subsidence, respectively. The fitting and generalization prediction capabilities of the models were tested. The test results show that the models have very good fitting and generalization prediction capabilities and the approach can be applied to predict the mining induced surface subsidence.
Keywords:mining induced surface subsidence  fuzziness and interaction of parameters  artificial neural fuzzy inference system
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