Georadar Waveform Characterization of Tunnel Lining Rear Defects and Joint Detection Method in Time and Frequency Domains

Aiming at the signal interference and feature recognition difficulties existing in the detection of concealed defects such as cracks and voids behind the tunnel lining, this study carried out a 1:1 reinforced concrete-steel arch frame composite lining model test; simulated the surrounding rock defects scenarios of three types of filling media, namely crushed stone, air, and water; and analyzed the time-domain, frequency-domain, and time-frequency-domain characteristics of the geological radar signal data. The research finds that the water-filled area generates a strong reflection due to the high dielectric constant, with the spectral peak reaching 712 MHz and the high-frequency component significantly enhanced. The peak frequency of the air-filled zone spectrum is 531 MHz, and the high-frequency bandwidth is broadened. The spectral peak of the crushed stone filling area is 507 MHz, with fast high-frequency attenuation and energy dispersion. The time-domain waveforms show that the amplitude in the water-filled area is the highest and the tailing is obvious, the waveform in the air-filled area is sharp, and the amplitude in the crushed stone-filled area is gentle. The peak frequency of the spectrum, the amplitude attenuation law, and the waveform shape can be used as the key indicators for discriminating the category of filling materials. The analysis method of feature fusion in the time-frequency domain has important engineering application value for improving the detection accuracy of geological radar in complex lining structures.

Authors

Institutions

Publication Details

Journal
Sensors
Published
2025-11-20
DOI
https://doi.org/10.3390/s25227086
Primary Topic
Geophysical Methods and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Georadar Waveform Characterization of Tunnel Lining Rear Defects and Joint Detection Method in Time and Frequency Domains

Wei Yan, Gaohang Lv, Quanyi Xie, Lei Kou et al.
Sensors
Geophysical Methods and Applications
article

Georadar Waveform Characterization of Tunnel Lining Rear Defects and Joint Detection Method in Time and Frequency Domains

Wei Yan, Gaohang Lv, Quanyi Xie, Lei Kou, Bo Li, Jian Liu, Xiao Zhang, Guo‐Min Lu
article en

Abstract

Aiming at the signal interference and feature recognition difficulties existing in the detection of concealed defects such as cracks and voids behind the tunnel lining, this study carried out a 1:1 reinforced concrete-steel arch frame composite lining model test; simulated the surrounding rock defects scenarios of three types of filling media, namely crushed stone, air, and water; and analyzed the time-domain, frequency-domain, and time-frequency-domain characteristics of the geological radar signal data. The research finds that the water-filled area generates a strong reflection due to the high dielectric constant, with the spectral peak reaching 712 MHz and the high-frequency component significantly enhanced. The peak frequency of the air-filled zone spectrum is 531 MHz, and the high-frequency bandwidth is broadened. The spectral peak of the crushed stone filling area is 507 MHz, with fast high-frequency attenuation and energy dispersion. The time-domain waveforms show that the amplitude in the water-filled area is the highest and the tailing is obvious, the waveform in the air-filled area is sharp, and the amplitude in the crushed stone-filled area is gentle. The peak frequency of the spectrum, the amplitude attenuation law, and the waveform shape can be used as the key indicators for discriminating the category of filling materials. The analysis method of feature fusion in the time-frequency domain has important engineering application value for improving the detection accuracy of geological radar in complex lining structures.

SensorsVol. 25(22)
Guizhou University (CN), Shandong Transportation Research Institute (CN), Jinan Institute of Quantum Technology (CN), Weifang University of Science and Technology (CN), Laoshan Laboratory
Openalex Percentile: Top 56%
Geophysical Methods and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.