Artificial Neural Networks for Predicting Rockburst in Deep Mining

摘要：

Rockburst is a kind of dynamic instability phenomenon for surrounding rock mass in deep mining．It has complicated nonlinear relationship between rockburst and its factors．Based on the analysis of main factors influencing rockburst, the mining depth H, the ratio of rocks maximal tangential stress to rocks uniaxial compressive strength, the ratio of rocks uniaxial compressive strength to rocks uniaxial tensile strength, and the elastic energy index was selected as the prediction indexes of rockburst．The model to predict rockburst was established by applying the theory of artificial neural network (ANN)．A large amount of on-site data was used as learning and training samples．Then the predicted results from the model and theoretical results are compared and analyzed．The results show that it is feasible and appropriate to select mining depth H as a main factor, the model is valid to predict rockburst in deep mining by ANN．

关键词： deep mining rockburst nonlinear mining depth artificial neural network

作者: SONG Changsheng LI Dehai

作者单位: School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454000, Henan, China

会议类型: 国际会议

会议名称: The 2007 International Symposium on Safety Science and Technology(2007采矿科学与安全技术国际学术会议)

会议地点: 河南焦作

会议语种:英文

页码: 846-851

在线出版日期: 2007-04-17（万方平台首次上网日期，不代表论文的发表时间）