基于主成分分析与BP神经网络的TBM围岩可掘性分级实时识别方法研究

Real-time Identification Method of TBM Surrounding Rock Excavatability Grade Based on Principal Component Analysis and BP Neural Network

TBM围岩可掘性等级实时在线识别和预警对TBM安全高效以及智能化掘进意义重大,基于新疆EH隧洞工程直径为7. 0 m的敞开式TBM实际掘进数据与地质数据,通过TBM掘进性能与施工风险的特征参数指标对围岩进行可掘性分级。在对不同围岩下区分度较好的掘进参数进行主成分分析之后,获得表征围岩可掘性等级的2个主成分指标,并在此基础上构建BP神经网络对围岩可掘性等级进行识别。同时,为提高模型响应速度,设计了一个MATLAB程序,从而获得了实用性较强的围岩可掘性等级实时识别方法。

The on-line real-time identification and early warning of TBM surrounding rock excavatability grade is significant for safe,high-efficient and intelligent TBM tunneling.An open-type TBM with a diameter of 7.0 m is applied to EH tunnel in Xinjiang,and the practical boring data and geological data are analyzed.The surrounding rock excavatability is classified according to the characteristic parameter indicators reflecting the TBM tunneling performance and construction risk.Further,after analyzing the excavation parameters of better discrimination quality under different surrounding rocks with principal component analysis method,two principal component indicators for characterizing the excavatability grade of surrounding rock are obtained;and based on which,the BP neural network is constructed to identify the surrounding rock excavatability grade.Meanwhile,in order to increase the response speed of the model,a MATLAB program is designed to obtain real-time identification method of surrounding rock excavatability grade with better practicability.