Shield machines are widely used in the construction of urban subway tunnels. Clear geological conditions are the prerequisite for safe and efficient shield tunneling. The vibration signal generated by the disc cutter cutting the rock contains rich information during the shield tunneling process. This paper analyzes the basic characteristics of vibration generated by the interaction between the disc cutter and the rock through theoretical calculation and numerical simulation. By measuring the shield vibration data under different surrounding rock strength on site and extracting the main characteristic indicators, a method is established relying on the BP neural network model for identifying the surrounding rock strength of the excavation face, which is characterized by the time domain-frequency domain of the vibration signal and successfully applied in the Jinan Metro Line 6 project. The research results show that the vibration frequency of the disc cutter force is concentrated at 0–25 Hz and 40–60 Hz, and the rock strength mainly affects the vibration amplitude of the disc cutter. The vibration signal characteristics generated during shield tunneling are highly sensitive to the surrounding rock strength, which is manifested as the greater the surrounding rock strength, the more obvious the time domain characteristic response of the vibration signal, and the more concentrated the main frequency of the IMF component. The accuracy of the model established in this paper for identifying the surrounding rock strength of excavation face is 98.88 %, and good application effects have been achieved in engineering.

中文翻译：

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在盾构隧道掘进中基于振动信号识别地层特征


盾构机广泛用于城市地铁隧道的建设。清晰的地质条件是安全高效盾构隧道掘进的前提。滚刀切割岩石产生的振动信号在盾构掘进过程中包含丰富的信息。本文通过理论计算和数值模拟，分析了滚刀与岩石相互作用产生的振动的基本特征。通过现场测量不同围岩强度下的盾构振动数据并提取主要特征指标，建立了一种依托BP神经网络模型识别开挖面围岩强度的方法，该方法具有振动信号时域-频域的特点，并在济南地铁6号线工程中成功应用。研究结果表明，滚刀力的振动频率集中在 0–25 Hz 和 40–60 Hz，岩石强度主要影响滚刀的振动幅度。盾构掘进过程中产生的振动信号特性对围岩强度高度敏感，表现为围岩强度越大，振动信号的时域特征响应越明显，IMF 分量的主频越集中。本文建立的模型对开挖面围岩强度的识别精度为98.88 %，在工程中取得了良好的应用效果。