基于BP神经网络的回采巷道围岩稳定性分类

Classification of surrounding rock stability of roadway based on BP neural network

针对袁店矿回采巷道围岩条件复杂、现有分类方案不够完善、巷道收敛率较大等问题,探索一种准确、高效、易于使用的回采巷道围岩稳定性预测方法.以袁店矿区回采巷道作为工程研究背景,采用BP神经网络预测原理,在分析出回采巷道围岩稳定性的影响因素基础之上,设计出一种基于BP神经网络的回采巷道围岩分类方法,利用Matlab7.0BP神经网络工具箱,实现算法编程和GUI友好的用户操作界面,结果证明此方法具有相当高的预测精度.

As the tunnel surrounding rock conditions in Yuandian mine is complex, the current classification scheme is not perfect, and the roadway convergence rate is larger, this paper attempts to explore an accurate, efficient and easy prediction method of roadway surrounding rock stability. Taking Yuandian mine roadway as the engineering research background, this paper uses the BP neural network forecasting principles, and designs a classification scheme of surrounding rock stability of roadway based on BP neural network after an on - site analysis of the factors influencing the stability of tunnel surrounding rock. By using MATLAB7.0BP neural network toolbox, it realizes the algorithm programming and GUI friendly user＇s interface. Results show that this method has a high precision of forecasting.