Detecting problems in
tunnel lining structures is often hampered by challenges such as complex
equipment setup, low detection efficiency, and unrepresentative results. To
address these issues, a novel tunnel-lining
radar detection robot has been designed and developed. This robot features a
four-rotor and four-drive
configuration and is equipped with a 900 MHz wireless
radar, and it can operate at speeds of up to 18 km/h. The robot traverses the
circumferential lining surface using precisely calibrated mechanical indices
and a custom gravity sensor to verify its path. The radar robot collects
structural damage data, which is analyzed using a lightweight radar signal
network model. This model automatically classifies common abnormalities, such
as reinforcing bars, insufficient thickness, noncompactness, and cavities. The
proposed method generates a signal time window that reflects the top and bottom
boundaries of the detected cavities, thereby enabling precise positioning and
depth measurements. Practical engineering testing and application are conducted
in the Xiaolijian and Lushan tunnels, with subsequent on-site drilling verification. The application and verification results
show that: (1) The radar robot can quickly and stably complete the automatic
collection of structural data, significantly improving the accuracy compared
with manual methods. The detection time for a 4 000 m2 lining is shortened from 3 to 0.5 h. (2) The F1 score and mAP@0.5 of the radar signal lightweight network classification
module are 0.93 and 97.51%, respectively, outperforming traditional radar map
models and requiring less computational effort. (3) The robot′ scavity depth predictions have an accuracy exceeding 80%, with the
output position and depth information aligning closely with the field drilling
results, validating the effectiveness of the radar robot for automatic damage
detection.
ZHOU Jianqiang
,
WEI Zheng
,
WANG Kexin
,
TAN Fuying
. Effective Detection
Technology for Tunnel Lining Using Radar Robots[J]. Tunnel Construction, 2024
, 44(8)
: 1686
-1696
.
DOI: 10.3973/j.issn.2096-4498.2024.08.016
