作者机构：
[丁德馨; 王有团; 李广悦; 王永东; 刘玉龙] Key Discipline Lab. for National Defence for Biotechnology in Uranium Mining and Hydrometallurgy, University of South China, Hengyang 421001, China
通讯机构：
[Ding, D.-X.] K;Key Discipline Lab. for National Defence for Biotechnology in Uranium Mining and Hydrometallurgy, University of South China, China
作者机构：
[Ding De-xin] Cent S Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China.;Nanhua Univ, Sch Architectural Resources & Environm Engn, Hengyang 421001, Peoples R China.
通讯机构：
[Ding De-xin] C;Cent S Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China.
关键词：
mining induced surface subsidence;fuzziness and interaction of parameters;artificial neural fuzzy inference system
摘要：
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.
作者机构：
[毕忠伟; 张志军] School of Resources and Safety Engineering, Central South University, Changsha 410083, China;[毕忠伟; 熊正为; 张志军; 丁德馨] School of Architecture Resources and Environment Engineering, Nanhua University, Hengyang 421001, China
通讯机构：
School of Resources and Safety Engineering, Central South University, China
作者机构：
[毕忠伟] School of Resources and Safety Engineering, Central South University, Changsha 410083, China;[张新华; 毕忠伟; 丁德馨] School of Architecture and Resources Environment Engineering, Nanhua University, Hengyang 421001, China
摘要：
The theory of Monte-Carlo simulation method is that the event happened probability was estimated by happened frequency in the experiments. So it is very important to ascertain the times of simulation. The probability and statistic theory are used to determine the optimal simulation times through strict mathematic reasoning. The study of two cases is made to show the availability of the presented method, and some relative problems were discussed.
作者机构：
[丁德馨; 闫春岭; 毕忠伟] School of Resources and Safety Engineering, Central South University, Changsha 410083, China;School of Architecture, Resources and Environment Engineering, Nanhua University, Hengyang 421001, China
通讯机构：
School of Resources and Safety Engineering, Central South University, China
摘要：
Current design method for circular sliding slopes is not so reasonable that it often results in slope sliding. As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.
