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软岩锚注巷道围岩变形量的时序预测
引用本文:吴宇,王连国,李青锋.软岩锚注巷道围岩变形量的时序预测[J].采矿与安全工程学报,2006,23(4):456-459.
作者姓名:吴宇  王连国  李青锋
作者单位:中国矿业大学,理学院,江苏,徐州,221008
基金项目:国家自然科学基金重大项目(50490270),教育部科学技术研究重点项目(106084),国家重点基础研究发展规划(973)计划项目(2005CB221502)
摘    要:为反映软岩锚注巷道围岩的变形在时间上的变化规律,利用径向基函数RBF神经网络强大的非线性映射能力,通过已经观测到的巷道围岩变形数据,建立软岩锚注巷道围岩变形量的时序预测模型.利用训练好的模型和当前观测数据得到了软岩巷道在锚注之后3,9,15及30d的顶底变形量和两帮相对变形量.实例分析表明,该预测模型能弥补现场观测和数值模拟的不足,预测结果具有较高的精度.同时也说明锚注支护能有效控制软岩巷道围岩的变形.

关 键 词:锚注  RBF神经网络  时序预测
文章编号:1673-3363(2006)04-0456-04
修稿时间:2006年5月9日

Time Series Prediction for Deformation of Surrounding Rocks in Soft Rock Bolt-Grouting Roadway
WU Yu,WANG Lian-guo,LI Qing-feng.Time Series Prediction for Deformation of Surrounding Rocks in Soft Rock Bolt-Grouting Roadway[J].Journal of Mining and Safety Engineering,2006,23(4):456-459.
Authors:WU Yu  WANG Lian-guo  LI Qing-feng
Abstract:In order to reflect the change in deformation of surrounding rocks in soft rock bolt-grouting roadway along with time,we have built the time series prediction model of the deformation of surrounding rock in soft rock bolt-grouting roadway based on the RBF neural network and the data of the deformation observed from the locale.Using the well trained model and the data observed from the locale,the roof-to-floor and the side-to-side displacements of the soft rock roadway bolt-grouted after 3,9,15,30 days are obtained.The analysis of actual examples indicates that 1)the prediction can make up the limitations of the numerical simulation and locale monitoring and the predicting result is reliable,and 2) the bolt-grouting support can control the deformation of surrounding rock in soft rock roadway at the same time.
Keywords:bolt-grouting  RBF neural network  time series prediction
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