为解决TBM掘进掌子面围岩参数感知难的问题，依托西北地区某供水工程，通过建立TBM主机振动监测系统，分析各类围岩条件下的主机振动信号。基于Savitzky-Golay算法对频谱曲线进行平滑处理，过滤掉TBM自振的影响后，统计分析各类围岩条件下振动峰值、平均极值、平均幅值和有效值的分布规律，采用8种数学模型建立振动特征参数与岩石单轴抗压强度、岩体完整性系数之间的映射关系。研究结果表明： 1）TBM主机自振频率在40 Hz左右； 2）4项振动特征参数的均值都随围岩类别变差而减小，同时随岩石单轴抗压强度和岩体完整性系数增加呈线性增长趋势； 3）围岩参数与主机振动特征参数映射关系的建立，可为基于主机振动的围岩感知技术研究提供参考。
To address the
challenge of accurately perceiving surrounding rock parameters at the tunnel
face bored by a tunnel boring machine(TBM), a case study is conducted on a
water supply project in Northwest China, and a TBM mainframe vibration
monitoring system is established to analyze the mainframe vibration signals
under various rock conditions. The impact of TBM selfvibration
is mitigated through the smooth processing of spectral curves using the
SavitzkyGolay algorithm. The distribution patterns of
vibration peak, extreme mean value, average amplitude, and root mean square
value across different rock conditions are statistically analyzed.
Additionally, mapping relations among vibration characteristic parameters,
uniaxial compressive strength of rock mass, and rock integrity coefficients are
established using eight mathematical models. Key findings include (1) the selfvibration frequency of the TBM mainframe is approximately 40 Hz; (2)
the mean values of vibration peak, extreme mean value, average amplitude, and
root mean square value diminish as the quality of surrounding rock
deteriorates, but increase linearly with the uniaxial compressive strength and
integrity coefficient of the rock mass; (3) These relationships offer valuable
insights for developing rock sensing technologies based on mainframe vibration
characteristics.
