小近距隧道台阶法开挖爆破振动信号特征分析

Characteristic analysis of blasting vibration signal in small-clearance tunnel excavation by bench method

隧道爆破振动信号具有典型的非平稳冲击特征,其信号特征是爆破参数优化的重要依据。在对厦门海沧疏港通道小净距隧道台阶法掘进爆破信号进行有效监测基础上,利用经验小波变换(Empirical Wavelet Transform, EWT)对爆破信号进行频带细化分析和时频特征提取,并对隧道爆破雷管起爆毫秒延时时间进行了识别。分析表明:大跨度小间距隧道爆破采用台阶法可显著降低爆破振动强度,减小了爆破效应对中夹岩的扰动。经验小波变换具有优良的信号自适应分解能力,其分析精度优于传统的小波和经验模态分解算法,适合用于隧道爆破信号精细化特征提取过程。经验小波变换识别得到的雷管毫秒延时时间与设计延时时间中值最为接近,识别精度高,为隧道掘进爆破网路安全评估和参数优化提供一种可借鉴方法。

Tunnel blasting vibration signals have typical non-stationary impact characteristics, and the signal characteristics are an important basis for optimizing blasting parameters. Based on the blasting signals effective monitoring of the small clearance tunnel excavation using bench method of Xiamen Haicang Shugang tunnel, Empirical Wavelet Transform(EWT) is used to perform refined analysis and extract the time-frequency characteristic of the blasting signals, and identify the millisecond delay of detonators for tunnel blasting. The analysis shows that the step method used in the blasting of large-span and small-spacing tunnels can significantly reduce the intensity of blasting vibration and reduce the disturbance of the blasting effect on the interlaid rock. The empirical wavelet transform has excellent signal adaptive decomposition capabilities, and its analysis accuracy is better than traditional wavelet and empirical mode decomposition algorithms, it is suitable for the refined feature extraction process of tunnel blasting signals. The millisecond interval of the detonator obtained by the empirical wavelet transform identification is the closest to the median value of the design delay interval, and the identification accuracy is high. It provides a reference method for tunnel blasting network security evaluation and parameter optimization.