基于盾构掘进参数的孤石地层识别方法研究

为了在盾构掘进过程中准确识别孤石地层，保证施工安全，运用理论分析与工程数据验证，研究孤石地层识别方法。基于盾构掘进比能，结合主掘进参数，提出修正比能（SM），构建孤石地层SM识别模型及识别矩阵。运用BP神经网络技术，以实测掘进参数作为训练样本，建立孤石地层神经网络识别模型，具有极高的识别精度。利用盾构掘进数据对孤石地层识别方法进行工程验证。研究结果表明： 1) 修正比能法具有较强的容错能力、稳定性及特异性，识别效果优于掘进比能法； 2) 2组实测数据下，神经网络识别结果与识别矩阵的吻合率达到98.3%和98.8%； 3) 以修正比能法为基础，结合神经网络法作为辅助与参考对孤石地层进行双重识别，具有较好的工程实际意义。

Recognition Methods for Boulder Geology Based on Shield Tunneling Parameters

In order to recognize the boulder geology accurately during shield tunneling and ensure construction safety, the recognition methods are studied by theoretical analysis and engineering data validation. And then the modulated specific energy(SM) is put forward and the SM recognition model and recognition matrix of boulder geology are constructed based on the shield tunneling specific energy and main tunneling parameters. By using the BP neural network technology, the monitored tunneling parameters are identified as training samples to establish neural network recognition model of the boulder geology, which has very high recognition accuracy. Finally, the recognition methods are verified by using shield tunneling data. The results show that: (1) The SM method has high fault tolerance, stability and specificity, whose pre judgment effect is better than that of the tunneling specific energy method. (2) The coincidence rates of neural network pre judgment to recognition matrix under 2 sets of monitored data reach 98.3% and 98.8%, respectively. (3) The dual recognition method for boulder geology by taking SM method as basis and neural network method as an auxiliary and reference has a good practical significance.