Ground-Pipeline Penetrating Radar Joint Detection Technology and Imaging Method for Urban Road Cavity Investigation

被引:0
|
作者
Liu H. [1 ,2 ]
Lai S. [1 ]
Chen J. [1 ]
Yue Y. [1 ]
Chen Z. [1 ]
Liu F. [3 ]
Meng X. [1 ]
Hu Q. [4 ,5 ]
机构
[1] School of Civil Engineering, Guangzhou University, Guangzhou
[2] Guangdong Engineering Research Center for Underground Infrastructural Protection in Coastal Clay Area, Guangzhou University, Guangzhou
[3] Jinan Rail Transit Grup Co., Ltd., Jinan
[4] Shanghai Institute of Disaster Prevention and Relief, Tongji University, Shanghai
[5] Key Laboratory of Urban Safety Risk Monitoring and Early Warning, The Ministry of Emergency Management, Shanghai
来源
关键词
back-propagation; cavity; ground penetrating radar(GPR); migration; pipe penetrating radar(PPR);
D O I
10.11908/j.issn.0253-374x.22493
中图分类号
学科分类号
摘要
Cavities under urban roads have increasingly become a great threat to traffic safety. Most cavities are caused by the leakage of underground pipelines. Pipe penetrating radar(PPR)has been widely used in the cavity inspection near the underground pipeline. However, due to the attenuation of electromagnetic waves in the underground medium, it cannot accurately detect the upper surface of the cavity. This paper proposes a ground-pipeline penetrating radar joint detection technology to improve the accuracy of cavity inspection. An underground object detection platform is established to detect air and water-filled cavities with different sizes. In combination with the back-projection algorithm and antenna pattern correction, the cavity near the pipeline can be migrated by proposed joint imaging method. Laboratory experiment results show that the PPR has a higher resolution in cavity inspection compared with ground penetrating radar(GPR)detection. In addition, the undesired diffractive artifacts at the target edges can be suppressed while improving the migration accuracy with the proposed join imaging method. Both the top and bottom of the cavity as well as the reinforcement inside the concrete pipe can be reconstructed with high-resolution by the proposed joint imaging method. The result is helpful to promote the practical application of GPR and PPR to cavity inspection near the underground pipeline. © 2023 Science Press. All rights reserved.
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页码:179 / 187
页数:8
相关论文
共 25 条
  • [1] HU Qunfang, ZHENG Zehao, LIU Hai, Et al., Application of 3D ground penetrating radar to leakage detection of urban underground pips, Journal of Tongji University (Natural Science), 48, 7, (2020)
  • [2] LIU Hai, SHI Zhenshi, LI Jianhui, Et al., Detection of road cavities in urban cities by 3D ground-penetrating radar, Sensors and Materials, 86, 3
  • [3] LI Liu, YU Hang, XU Hang, Et al., Underground object classification using deep 3-D convolutional networks and multiple mirror encoding for GPR data, IEEE Geoscience and Remote Sensing Letters, 19, (2020)
  • [4] LIU Hai, XING Bangan, ZHU Jinfeng, Et al., Quantitative stability analysis of ground penetrating radar systems, IEEE Geoscience and Remote Sensing Letters, 15, 4, (2018)
  • [5] LIU Lanbo, QIAN Rongyi, Ground penetrating radar: A critical tool in near-surface geophysics, Chinese Journal of Geophysics, 58, 8, (2015)
  • [6] LUO T X, LAI W W., GPR pattern recognition of shallow subsurface air voids, Tunnelling and Underground Space Technology, 99, (2020)
  • [7] LUO T X, LAI W W, GIANNOPOULOS A., Forward modelling on GPR responses of subsurface air voids, Tunnelling and Underground Space Technology, 103, (2020)
  • [8] DAI Yi, XIE Fei, Detection and automatic identification of voids by ground penetrating radar in pipes based on machine learning, Urban Geotechnical Investigation & Surveying, (2021)
  • [9] DAVIES J P, CLARKE B A, WHITER J T, Et al., Factors influencing the structural deterioration and collapse of rigid sewer pipes, Urban Water, 3, 1, (2001)
  • [10] LIU Hai, HUANG Zhaogang, YUE Yunpeng, Et al., Characteristics analysis of ground penetrating radar signals for groundwater pipe leakage environment, Journal of Electronics and Information Technology, 44, 4, (2022)