基于雷达机器人的隧道衬砌高效检测技术研究

Effective Detection Technology for Tunnel Lining Using Radar Robots

为解决隧道衬砌结构病害检测中设备布设困难、检测效率低下、结果不具代表性等问题,设计由四旋翼-四驱动结构搭载900 MHz无线雷达、运行速度为18 km/h的隧道衬砌雷达检测机器人,设定力学指标满足机器人沿衬砌环向曲面行走的功能,并研制重力传感器用于路径校准。依据雷达机器人采集的结构损伤信息,开发雷达信号轻量化网络模型,对钢筋、厚度不足、不密实与空洞4类衬砌内部常见的异常信号进行自动分类,并输出空洞顶部与底部反射的信号时间窗,可用于空洞信号的精准定位与深度测算。在小笠尖隧道与鹿山隧道开展实际工程检测应用,并完成现场打孔验证。工程应用及验证结果表明:1)雷达机器人能快速稳定完成结构数据的自动化采集,与人工检测相比,检测数据精度较高,检测4000 m^(2)衬砌由3 h缩短至0.5 h;2)雷达信号轻量化网络分类精确度指标F1分数与mAP@0.5分别为0.93与97.51%,优于雷达图谱模型并且计算量较小;3)定位模块在不同位置处对空洞深度尺寸的预测准确率均大于80%,输出位置、深度信息与现场打孔结果一致,证明了雷达机器人在病害自动化检测中的有效性。

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 4000 m^(2) 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.