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.