Skip to main content
SpringerLink
Log in

OMHT method for weak signal processing of GPR and its application in identification of concrete micro-crack

地质雷达弱信号处理OMHT 法及其在混凝土微裂缝识别中的应用

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

In the light of the problem of weak reflection signals shielded by strong reflections from the concrete surface, the detection and the recognition of hidden micro-cracks in the shield tunnel lining were studied using the orthogonal matching pursuit and the Hilbert transform(OMHT method). First, according to the matching pursuit algorithm and the strong reflection-forming mechanism, and based on the sparse representation theory, a sparse dictionary, adapted to the characteristics of the strong reflection signal, was selected, and a matching decomposition of each signal was performed so that the weak target signal submerged in the strong reflection was displayed more strongly. Second, the Hilbert transform was used to extract multiple parameters, such as the instantaneous amplitude, the instantaneous frequency, and the instantaneous phase, from the processed signal, and the ground penetrating radar (GPR) image was comprehensively analyzed and determined from multiple angles. The results show that the OMHT method can accurately weaken the effect of the strong impedance interface and effectively enhance the weak reflected signal energy of hidden micro-crack in the shield tunnel segment. The resolution of the processed GPR image is greatly improved, and the reflected signal of the hidden micro-crack is easily visible, which proves the validity and accuracy of the analysis method.

摘要

针对混凝土表面强反射等原因造成的异常强反射信号屏蔽下部微弱目的信号的问题, 采用正交 贪婪匹配追踪与希尔伯特变换相结合的方法(OMHT 法)进行了盾构隧道衬砌隐伏微裂缝的检测与识别. 首先, 依据匹配追踪算法和强反射形成机理, 基于稀疏表示理论, 选取了与强反射信号特征相适 应的稀疏字典, 对每道信号进行匹配分解处理, 使淹没于强反射中的微弱目标体信号得到较好的展示. 其次, 对处理后的信号进行希尔伯特变换提取雷达剖面瞬时振幅、瞬时频率和瞬时相位等多个参数信 息, 从多个角度对地质雷达图像进行综合分析和判断. 结果表明, OMHT 法可精准弱化强阻抗界面的影响, 并有效增强管片隐伏微裂缝弱反射信号能量. 处理后的地质雷达图像分辨率明显提高, 隐伏微 裂缝反射信号清晰可见, 证明了该分析技术对隐伏微裂缝识别的有效性和准确性.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. LIU Hao-ran, LING Tong-hua, LI Di-yuan, HUANG Fu, ZHANG Liang. A quantitative analysis method for GPR signals based on optimal biorthogonal wavelet [J]. Journal of Central South University, 2018, 25(4): 879–891. DOI: https://doi.org/10.1007/s11771-018-3791-y.

    Article  Google Scholar 

  2. LING Tong-hua, ZHANG Liang, GU Dan-ping, LIU Hao-ran, CAO Feng. Analysis of cracking mechanism and bearing capacity of shield tunnel segment with voids behind [J]. Journal of Railway Science and Engineering, 2018, 15(9): 2293–2300. DOI: https://doi.org/10.19713/j.cnki.43-1423/u.2018.09.016. (in Chinese)

    Google Scholar 

  3. NOBES D C. Ground penetrating radar response from voids: A demonstration using a simple model [J]. Ndt & E International, 2017, 91: 47–53. DOI: https://doi.org/10.1016/j.ndteint.2017.05.007.

    Article  Google Scholar 

  4. CASSIDY N J, EDDIES R, DODS S. Void detection beneath reinforced concrete sections: The practical application of ground-penetrating radar and ultrasonic techniques [J]. Journal of Applied Geophysics, 2011, 74(4): 263–276. DOI: https://doi.org/10.1016/j.jappgeo.2011.06.003.

    Article  Google Scholar 

  5. KILIC G, EREN L. Neural network based inspection of voids and karst conduits in hydro-electric power station tunnels using GPR [J]. Journal of Applied Geophysics, 2018, 151: 194–204. DOI: https://doi.org/10.1016/j.jappgeo.2018.02.026.

    Article  Google Scholar 

  6. LAI W L, DÉROBERT X, ANNAN P. A review of ground penetrating radar application in civil engineering: A 30-year journey from locating and testing to imaging and diagnosis [J]. Ndt & E International, 2018, 96: 58–78. DOI: https://doi.org/10.1016/j.ndteint.2017.04.002.

    Article  Google Scholar 

  7. ZHOU X P, XIA E M, YANG H Q, QIAN Q H. Different crack sizes analyzed for surrounding rock mass around underground caverns in Jinping I hydropower station [J]. Theoretical and Applied Fracture Mechanics, 2012, 57(1): 19–30. DOI: https://doi.org/10.1016/j.tafmec.2011.12.004.

    Article  Google Scholar 

  8. ZHOU X P, LIAN Y J, WONG L N Y, BERTO F. Understanding the fracture behavior of brittle and ductile multi-flawed rocks by uniaxial loading by digital image correlation [J]. Engineering Fracture Mechanics, 2018, 199: 438–460. DOI: https://doi.org/10.1016/j.engfracmech.2018.06.007.

    Article  Google Scholar 

  9. REBOLLO-NEIRA L. Cooperative greedy pursuit strategies for sparse signal representation by partitioning [M]. Elsevier North-Holland, Inc. 2016.

  10. WANG Jun, ZHOU Si-chao, XIA Li-min. Human interaction recognition based on sparse representation of feature covariance matrices [J]. Journal of Central South University, 2018, 25(2): 304–314. DOI: https://doi.org/10.1007/s11771-018-3738-3.

    Article  Google Scholar 

  11. TANG Jing-tian, LI Guang, ZHOU Cong, LI Jin, LIU Xiao-qiong, ZHU Hui-jie. Power-line interference suppression of MT data based on frequency domain sparse decomposition [J]. Journal of Central South University, 2018, 25(9): 2150–2163. DOI: https://doi.org/10.1007/s11771-018-3904-7.

    Article  Google Scholar 

  12. MALLAT S G, ZHANG Z. Matching pursuits with time-frequency dictionaries [J]. IEEE Trans on Signal Processing, 1993, 41(12): 3397–3415. DOI: https://doi.org/10.1109/78.258082.

    Article  Google Scholar 

  13. GHOFRANI S, AMIN M G, ZHANG Y D. High-resolution direction finding of non-stationary signals using matching pursuit [J]. Signal Processing, 2013, 93(12): 3466–3478. DOI: https://doi.org/10.1016/j.sigpro.2013.03.016.

    Article  Google Scholar 

  14. ZHANG Zhao-heng, DING Jian-ming, WU Chao, LIU Jian-hui. Impulsive component extraction using shift-invariant dictionary learning and its application to gear-box bearing early fault diagnosis [J]. Journal of Central South University, 2019, 26(4): 824–838. DOI: https://doi.org/10.1007/s11771-019-4052-4.

    Article  Google Scholar 

  15. STUDER D, HOFFMANN U, KOENIG T. From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition [J]. Journal of Neuroscience Methods, 2006, 153(2): 261–275. DOI: https://doi.org/10.1016/j.jneumeth.2005.11.006.

    Article  Google Scholar 

  16. YU Zhi-xiong, XUE Gui-yu, ZHOU Chuang-bing. Application of complex signal analysis to process ground penetrating radar data [J]. Chinese Journal of Rock Mechanics & Engineering, 2005, 24(5): 798–802. DOI: https://doi.org/10.3321/j.issn:1000-6915.2005.05.011. (in Chinese)

    Google Scholar 

  17. SAHOO S, BISWAL P, DAS T, SABUT S. De-noising of ECG signal and QRS detection using hilbert transform and adaptive thresholding [J]. Procedia Technology, 2016, 25: 68–75. DOI: https://doi.org/10.1016/j.protcy.2016.08.082.

    Article  Google Scholar 

  18. SCHOTTE K, NAGY W, NUTTENS T, WULF A D, BOGAERT P V. Impact of tidal level fluctuations on the structural behaviour of a segmental tunnel lining [J]. Tunnelling & Underground Space Technology, 2017, 64: 184–208. DOI: https://doi.org/10.1016/j.tust.2017.01.014.

    Article  Google Scholar 

  19. YANG Chun-shan. Influence of construction when the tunnel is first excavation followed by working well on tunnel deformation and control countermeasures [D]. Guangzhou: South China University of Technology, 2015. (in Chinese)

    Google Scholar 

  20. KATEBI H, REZAEI A H, HAJIALILUE-BONAB M, TARIFARD A. Assessment the influence of ground stratification, tunnel and surface buildings specifications on shield tunnel lining loads (by FEM) [J]. Tunnelling and Underground Space Technology incorporating Trenchless Technology Research, 2015, 49: 67–78. DOI: https://doi.org/10.1016/j.tust.2015.04.004.

    Article  Google Scholar 

  21. LI Biao. Structure of mining tunnel detection and evaluation of the service performance [D]. Chongqing: Chongqing Jiaotong University, 2014. (in Chinese)

    Google Scholar 

  22. LAI Jin-xing, QIU Jun-ling, PAN Yun-peng, CAO Xiao-jun, LIU Chi, FAN Hao-bo. Comprehensive monitoring and analysis of segment cracking in shield tunnels [J]. Modern Tunnelling Technology, 2015, 52(2): 186–191. DOI: https://doi.org/10.13807/j.cnki.mtt.2015.02.028. (in Chinese)

    Google Scholar 

  23. LI C Q, BAJI H, MAHMOODIAN M, YANG W. Risk-cost optimized maintenance strategy for tunnel structures [C]// International Conference on Smart Infrastructure and Construction. 2016.

Download references

Author information

Authors and Affiliations

  1. School of Civil Engineering, Changsha University of Science and Technology, Changsha, 410114, China

    Tong-hua Ling  (凌同华), Liang Zhang  (张亮), Fu Huang  (黄阜), Dan-ping Gu  (谷淡平) & Bin Yu  (余彬)

  2. Hunan Province Engineering Laboratory of Bridge Structure, Changsha University of Science and Technology, Changsha, 410114, China

    Liang Zhang  (张亮)

  3. College of Civil Engineering, Hunan City University, Yiyang, 413000, China

    Sheng Zhang  (张胜)

Authors
  1. Tong-hua Ling  (凌同华)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Zhang  (张亮)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fu Huang  (黄阜)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dan-ping Gu  (谷淡平)
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bin Yu  (余彬)
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sheng Zhang  (张胜)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liang Zhang  (张亮).

Additional information

Foundation item: Projects(51678071, 51608183) supported by the National Natural Science Foundation of China; Projects(CX2018B530, CX2018B531) supported by the Postgraduate Research and Innovation-funded Project of Hunan Province, China; Projects(16BCX13, 16BCX09) supported by Changsha University of Science and Technology, China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ling, Th., Zhang, L., Huang, F. et al. OMHT method for weak signal processing of GPR and its application in identification of concrete micro-crack. J. Cent. South Univ. 26, 3057–3065 (2019). https://doi.org/10.1007/s11771-019-4236-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-019-4236-y

Key words

关键词

Search

Navigation