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Related Experiment Video

Updated: Jan 10, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

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Published on: May 1, 2018

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

Jian Liu^1,2, Wei Yan¹, Gaohang Lv¹

¹School of Qilu Transportation, Shandong University, Jinan 250014, China.

Sensors (Basel, Switzerland)

|November 27, 2025

Summary

This study used geological radar to detect tunnel lining defects. Different filling materials like water, air, and crushed stone showed distinct signal characteristics, enabling accurate defect identification.

Keywords:

defect detection ground-penetrating radar time–frequency-domain analysis tunnel lining structure waveform characteristics

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Last Updated: Jan 10, 2026

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Area of Science:

Geophysics
Civil Engineering
Non-destructive Testing

Background:

Tunnel lining integrity is crucial for infrastructure safety.
Detecting concealed defects like cracks and voids behind tunnel linings presents signal interference and feature recognition challenges.

Purpose of the Study:

To analyze geological radar signal characteristics for differentiating concealed defects behind tunnel linings.
To evaluate the effectiveness of time-domain, frequency-domain, and time-frequency-domain analyses for defect classification.

Main Methods:

A 1:1 reinforced concrete-steel arch frame composite lining model was tested.
Simulated surrounding rock defects with crushed stone, air, and water filling media.
Analyzed geological radar signal data in time, frequency, and time-frequency domains.

Main Results:

Water-filled areas exhibited strong reflections (peak frequency 712 MHz) with enhanced high-frequency components.
Air-filled zones showed a spectral peak at 531 MHz and broadened high-frequency bandwidth.
Crushed stone areas displayed a spectral peak at 507 MHz with rapid high-frequency attenuation and energy dispersion.
Time-domain analysis revealed distinct waveform amplitudes and shapes for each filling medium.

Conclusions:

Peak frequency, amplitude attenuation, and waveform shape are key indicators for discriminating filling materials.
Time-frequency domain feature fusion enhances geological radar detection accuracy in complex tunnel lining structures.