吉林大学学报(工学版) ›› 2026, Vol. 56 ›› Issue (8): 2150-2161.doi: 10.13229/j.cnki.jdxbgxb.20250012

• 交通运输工程·土木工程 • 上一篇    

基于扩频与极化增益补偿的多层钢筋探测

夏子涵1(),薛松涛1,2,3,谢丽宇1,3,4(),刘江5,周雷军5   

  1. 1.同济大学 土木工程学院,上海 200092
    2.日本东北工业大学 工学部建筑学科,宫城县 仙台市 982-577
    3.同济大学 土木工程防灾减灾全国重点实验室,上海 200092
    4.新疆大学 土木工程与建筑学院,乌鲁木齐 ;830049
    5.上海秋元华林建设集团有限公司,上海 201318
  • 收稿日期:2025-01-06 出版日期:2026-08-01 发布日期:2026-09-02
  • 通讯作者: 谢丽宇 E-mail:xzhooo@tongji.edu.cn;liyuxie@tongji.edu.cn
  • 作者简介:夏子涵(1996-),男,博士研究生. 研究方向:结构健康监测,探地雷达. E-mail: xzhooo@tongji.edu.cn
  • 基金资助:
    国家自然科学基金项目(52178298);国家自然科学基金项目(52078375);国家重点研发计划重点专项项目(2021YFE0112200)

Multi-layer rebar detection method based on spread spectrum and polarization gain compensation

Zi-han XIA1(),Song-tao XUE1,2,3,Li-yu XIE1,3,4(),Jiang LIU5,Lei-jun ZHOU5   

  1. 1.College of Civil Engineering,Tongji University,Shanghai 200092,China
    2.Department of Architecture,Tohoku Institute of Technology,Sendai 982-8577,Japan
    3.State Key Laboratory of Disaster Reduction in Civil Engineering,Tongji University,Shanghai 200092,China
    4.School of Civil Engineering and Architecture,Xinjiang University,Urumqi 830049,China
    5.Shanghai Choyoin Construction Group Co. ,Ltd. ,Shanghai 201318,China
  • Received:2025-01-06 Online:2026-08-01 Published:2026-09-02
  • Contact: Li-yu XIE E-mail:xzhooo@tongji.edu.cn;liyuxie@tongji.edu.cn

摘要:

针对现有的探地雷达(GPR)技术在多层钢筋密集场景下,仍面临分辨率不足、信号重叠以及信噪比降低等问题,提出了一种基于扩频与极化增益补偿的多层钢筋探测方法。该方法通过优化频谱扩展技术,有效克服了频率跳变和数据融合中信息丢失的问题。同时,利用金属的电磁反射特性与极化反转增益放大机制,增强了钢筋反射信号的强度,从而显著提升钢筋探测的能力。CST数值模拟结果表明,金属反射信号的极化偏转特性能够显著提高回波信号中钢筋反射的信噪比,极化匹配传输效率最大可提升约20 dB。此外,在gprMax中对频谱扩展前后的探地雷达数据进行了模拟计算与后处理分析。结果表明,本文所提出的探地雷达扩频技术能够有效提升距离分辨率,解决了窄距双层钢筋反射信号混淆问题,成功实现了其独立定位,最大误差仅为 5.25%。此外,本文还进行了多信噪比工况下的鲁棒性分析,进一步验证了该方法在复杂实际环境中的适用性和可靠性。

关键词: 钢筋无损检测, 探地雷达, 频谱扩展, 数据融合, 金属反射信号, 极化反转, 增益补偿, 雷达距离分辨率

Abstract:

To address the issues of insufficient resolution, signal overlap, and reduced signal-to-noise ratio that existing Ground Penetrating Radar(GPR) technology still faces in multi-layer dense rebar scenarios, a multi-layer rebar detection method based on spread spectrum and polarization gain compensation is proposed. The proposed approach optimizes the spread spectrum technique to effectively mitigate the issues of frequency discontinuities and information loss during data fusion. Additionally, it leverages the electromagnetic reflective properties of metal and the amplification mechanism of polarization reversal to enhance the intensity of rebar reflection signals, thereby significantly improving detection performance. The numerical simulation results obtained using CST demonstrate that the polarization deflection characteristics of metal reflection signals can significantly enhance the SNR of rebar reflections in echo signals. The polarization matching transmission efficiency can be improved by up to approximately 20 dB. Additionally, simulations and post-processing analyses were performed on GPR data before and after spectrum expansion using gprMax. The results demonstrate that the GPR frequency expansion technique proposed in this study effectively enhances distance resolution, resolves the issue of overlapping reflection signals from closely spaced double-layered rebars, and successfully achieves their independent localization, with a maximum error of only 5.25%. In addition, the robustness analysis under multiple SNR conditions is also carried out in this paper to further validate the applicability and reliability of the method in complex practical environments.

Key words: non-destructive detection of rebar, ground penetrating radar(GPR), spectrum expansion, data fusion, metal reflection signals, polarization reversal, gain compensation, radar range resolution

中图分类号: 

  • TU375

图1

单基地SFCW雷达系统示意图"

图2

探地雷达的信号传播路径示意图"

图3

双基地SFCW雷达系统示意图"

图4

双基地SFCW雷达的测量数据组成"

图5

圆极化的金属反射示意图"

图6

双极化喇叭天线模型CST示意图"

图7

入射电磁波极化匹配示意图"

图8

极化匹配效率评估"

图9

gprMax模型示意图"

图10

探地雷达B?scan数据对比"

图11

滤直达波的增益处理数据对比"

图12

时间尺度放大的数据对比"

图13

频谱扩展前后探地雷达A-Scan原始数据"

图14

两层钢筋高度的反演结果"

表1

两层钢筋高度的反演结果"

钢筋距离底

部高度/cm

真实结果频谱未扩展频谱扩展2倍
原始数据后处理原始数据后处理
第一层钢筋7.007.26无法计测7.216.89
第一层误差/%-3.71-31.57
第二层钢筋4.00无法计测无法计测无法计测4.21
第二层误差/%-3.71--5.25

图15

高、中、低信噪比工况对比"

表2

三种信噪比工况的反演结果"

钢筋距离

底部高度

真实

结果

SNR=

30 dB

SNR=

20 dB

SNR=

10 dB

第一层钢筋7.007.187.206.62
第一层误差/%-2.572.865.43
第二层钢筋4.004.154.16

无法

计测

第二层误差/%-3.754-
[1] Xu X, Li J, Qiao X, et al. Fusion of multiple time‐domain GPR datasets of different center frequencies[J]. Near Surf Geophys, 2019,17: 141-150.
[2] Zhao W, Yuan L, Forte E, et al. Multi-frequency GPR data fusion with genetic algorithms for archaeological prospection[J]. Remote Sens-Basel, 2021, 13: No.2804.
[3] Zhao W, Lu G. A novel multifrequency GPR data fusion algorithm based on time-varying weighting strategy[J]. IEEE Geosci Remote S, 2021,19: 1-4.
[4] Guan Z, Liu W. Multi-Frequency GPR data fusion through a joint sliding window and wavelet transform-weighting method for top-coal structure detection[J]. Applied Sciences, 2024,14: No.2721.
[5] Bi W, Zhao Y, Shen R, et al. Multi-frequency GPR data fusion and its application in NDT[J]. Ndt & E Int, 2020,115: No.102289.
[6] de Coster A, Lambot S. Fusion of multifrequency GPR data freed from antenna effects[J]. IEEE J-Stars, 2018,11: 664-674.
[7] Annan A P. Electromagnetic principles of ground penetrating radar[J]. Ground Penetrating Radar: Theory and Applications,2009,1: 3-41.
[8] Leucci G. Ground penetrating radar: the electromagnetic signal attenuation and maximum penetration depth[J]. Scholarly Research Exchange, 2008,2008: No. 926091.
[9] Liu Y, Li K, Jia Y, et al. Wideband RCS reduction of a slot array antenna using polarization conversion metasurfaces[J]. IEEE T Antenn Propag, 2015,64:326-331.
[10] Lambot S, Slob E C, van den Bosch I, et al. Modeling of ground-penetrating radar for accurate characterization of subsurface electric properties[J]. IEEE T Geosci Remote, 2004,42: 2555-2568.
[11] 任仕召, 魏光辉, 潘晓东, 等. 典型雷达装备带内连续波辐射效应试验研究[J]. 强激光与粒子束, 2020,32(5):61-66.
Ren Shi-zhao, Wei Guang-hui, Pan Xiao-dong, et al. Experimental study on radiation effect of in-band continuous wave on typical radar equipment[J]. High Power Laser and Particle Beams, 2020, 32(5): 61-66.
[12] Hiebel M. Fundamentals of Vector Network Analysis[M]. Berlin: Rohde & Schwarz, 2007.
[13] Zhang J, Ye S, Lin Y, et al. A modified model for quasi-monostatic ground penetrating radar[J]. IEEE Geosci Remote S, 2019,17:406-410.
[14] Qureshi M A, Schmidt C H, Eibert T F. Efficient near-field far-field transformation for nonredundant sampling representation on arbitrary surfaces in near-field antenna measurements[J]. IEEE T Antenn Propag, 2012,61: 2025-2033.
[15] Mourmeaux N, Tran A P, Lambot S. Soil permittivity and conductivity characterization by full-wave inversion of near-field GPR data[C]∥Proceedings of the 15th International Conference on Ground Penetrating Radar, Brussels, Belgium, 2014: 497-502.
[16] Noon D A. Stepped-frequency radar design and signal processing enhances ground penetrating radar performance[D]. St. Lucia: Department of Electrical and Computer Engineering,The University of Queensland, 1996.
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