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Research on Tunnel Boring Machine Tunnel Water Disaster Detection and Radar Echo Signal Processing.

Authors :
Lu, Gaoming
Ma, Yan
Zhang, Qian
Wang, Jianfei
Du, Lijie
Hao, Guoqing
Source :
Buildings (2075-5309); Jun2024, Vol. 14 Issue 6, p1737, 18p
Publication Year :
2024

Abstract

This study focused on the detection of water inrush in tunnels excavated by full-section hard rock tunnel boring machines (TBMs) and employed ground penetrating radar methods for conducting research on radar signal processing algorithms. The research demonstrates that conventional techniques are inadequate for eliminating the interference of TBM equipment on radar signal propagation. This study employs a radar antenna array method for signal transmission, utilizing a wavelet double-threshold filtering algorithm and wave propagation theory to suppress clutter. These methods exhibit strong signal reception capabilities and are effective in eliminating 13.1% of the direct wave components. The adoption of a novel, efficient radar signal imaging algorithm simplifies the imaging process. Results of verification indicate that the synthetic aperture algorithm, enhanced with cross-correlation calculation, yields the optimal imaging effect. This investigation, which was conducted in conjunction with the construction of a diversion tunnel in a specific region, has confirmed the applicability of the ground penetrating radar method for the detection of water inrush in TBM tunnels by conducting a comparative analysis of the direct wave removal algorithm and the integration of the optimal imaging algorithm. The innovative application of ground penetrating radar within TBM tunnels, along with a targeted technology to mitigate signal interference from metal equipment, has led to the selection of an appropriate algorithm for both signal processing and imaging. This approach offers a novel solution for the detection of water source disasters in TBM tunnels. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20755309
Volume :
14
Issue :
6
Database :
Complementary Index
Journal :
Buildings (2075-5309)
Publication Type :
Academic Journal
Accession number :
178158637
Full Text :
https://doi.org/10.3390/buildings14061737