Interference law and gain method of reinforcement layer on GPR detection signal of cavities behind tunnel linings

Cavities behind tunnel linings can weaken lining performance and are commonly detected using ground-penetrating radar (GPR). In reinforced concrete linings, reinforcement reflections may overlap with cavity responses and reduce image clarity. To examine this effect, controlled model tests were conducted using reinforced and non-reinforced lining specimens. Air-filled and water-filled cavities with different sizes and burial depths were compared. The results show that the reinforcement layer partly disturbs the continuity of hyperbolic features and reduces reflection amplitude, especially when the cavity is close to the reinforcement layer. Under the tested conditions, the depth interpretation error was generally within 10%. The size-related result should be regarded as the apparent cavity thickness along the radar wave propagation direction, rather than the directly measured geometric diameter, with an error within 16%. For air-filled cavities, the amplitude difference decreased with burial depth, and an empirical compensation formula was established with a verification error within 15%. For water-filled cavities, the amplitude difference was more closely related to cavity size, and the compensation error was within 8%. The proposed gain relationship is an empirical correction method applicable to conditions similar to those tested.

Authors

Institutions

Publication Details

Journal
Australian Journal of Civil Engineering
Published
2026-07-23
DOI
https://doi.org/10.1080/14488353.2026.2707584
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

Interference law and gain method of reinforcement layer on GPR detection signal of cavities behind tunnel linings

Gaohang Lv, Quanyi Xie, Zhou Lizhi, Liu Jian
Australian Journal of Civil Engineering
Geophysical Methods and Applications
article

Interference law and gain method of reinforcement layer on GPR detection signal of cavities behind tunnel linings

Gaohang Lv, Quanyi Xie, Zhou Lizhi, Liu Jian
article en

Abstract

Cavities behind tunnel linings can weaken lining performance and are commonly detected using ground-penetrating radar (GPR). In reinforced concrete linings, reinforcement reflections may overlap with cavity responses and reduce image clarity. To examine this effect, controlled model tests were conducted using reinforced and non-reinforced lining specimens. Air-filled and water-filled cavities with different sizes and burial depths were compared. The results show that the reinforcement layer partly disturbs the continuity of hyperbolic features and reduces reflection amplitude, especially when the cavity is close to the reinforcement layer. Under the tested conditions, the depth interpretation error was generally within 10%. The size-related result should be regarded as the apparent cavity thickness along the radar wave propagation direction, rather than the directly measured geometric diameter, with an error within 16%. For air-filled cavities, the amplitude difference decreased with burial depth, and an empirical compensation formula was established with a verification error within 15%. For water-filled cavities, the amplitude difference was more closely related to cavity size, and the compensation error was within 8%. The proposed gain relationship is an empirical correction method applicable to conditions similar to those tested.

Australian Journal of Civil Engineering
Shandong University (CN)
Openalex Percentile: Top 12%
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.