土工基础

›› 2013, Vol. 27 ›› Issue (5): 31-33.

• 工程实录 • 上一篇    下一篇

RBF神经网络在岩爆预测中的应用

张德永，王玉洲，张志豪   

  1. （中国石油工程建设公司岩土工程公司，山东青岛 266071）
  • 收稿日期:2012-12-12 出版日期:2013-10-22 发布日期:2013-10-28
  • 作者简介:张德永（1985-），男，山东潍坊人，硕士,研究方向为岩土工程。

Application of RBF neural network in rockburst prediction

ZHANG Deyong,WANG Yuzhou,ZHANG Zhihao   

  1. (China Petroleum Engineering and Construction Corp,Qingdao 266071)
  • Received:2012-12-12 Online:2013-10-22 Published:2013-10-28

摘要： 岩爆是高地应力地区危害地下工程建设的主要地质灾害之一，能否进行及时合理的岩爆等级预测成为工程建设的关键问题。选取多个理论判据，将建立的RBF神经网络预测模型应用于实际工程的岩爆预测，并与岩爆实际发生情况进行验证分析。结果表明，该预测模型的评判结果与实际情况较为吻合，对后续工程建设有较好的指导作用。

关键词: 岩爆预测, RBF神经网络, 地下洞室群

Abstract: Rock burst is one of the main geological hazards in underground constructions at the high stress areas, so it is very important to be able to predict rock burst in an accurately and timely manner. Rock burst prediction has developed from using a single indicator to using multiple and comprehensive indicators. Radial basis function (RBF) neural network is an integrated prediction method. In RBF neural network design, network is designed as high dimensional space curve fitting, in which lowdimensional model inputs is transformed to highdimensional space through hidden layer.In this way,the nonlinear mapping between the input vectors and output vectors is established. Based on several theoretical criteria specific to the project,the established RBF neural network model is applied to rock burst prediction for a real project.The results of prediction are consistent with the actual situation.

Key words: Rock Burst, RBF Neural Network, Underground Caverns