基于GPR反射波信號多維分析的隧道病害智能辨識

高永濤; 徐俊; 吳順川; 呂建華; 陳文

doi:10.13374/j.issn2095-9389.2018.03.005

基于GPR反射波信號多維分析的隧道病害智能辨識

doi: 10.13374/j.issn2095-9389.2018.03.005

1) 北京科技大學土木與資源工程學院, 北京 100083;
2) 中電建路橋集團有限公司, 北京 100044

詳細信息

中圖分類號: U456.3
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出版歷程
- 收稿日期: 2017-10-23

An intelligent identification method to detect tunnel defects based on the multidimensional analysis of GPR reflections

1) School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2) Power China Road Bridge Group Co., Ltd, Beijing 100044, China

摘要

摘要: 隨著我國隧道工程建設的快速發展,由隧道病害引發的隧道質量和安全問題越發常見.通過地質雷達探測隧道病害對于減少隧道質量和安全問題具有十分重要的意義,為了提高病害探測的效率及可靠性,基于雷達反射波信號多維度分析,提出一種隧道病害智能辨識的新方法.根據反射波信號時域、頻域及時頻域分析結果提取病害信號辨識的6個典型特征,利用支持向量機算法對典型特征的訓練構建病害信號的二分類模型,實現了病害水平分布范圍的自動辨識;再依據病害信號的第一本征模態函數分量振幅包絡計算病害深度分布范圍,最終實現隧道病害的智能辨識.結合某隧道回填層雷達實測數據對智能辨識算法的性能進行評價,與人工辨識結果的對比表明,該智能算法對于病害的辨識能力較強,病害的識別率高達100%,但辨識結果中同時存在少量誤判,準確率達78.6%,滿足工程應用的需求.該算法可用于隧道工程各類地質雷達探測數據中病害的智能辨識,而對于其他領域的地質雷達探測數據,本文研究成果亦可為不同類型探測目標智能辨識算法的設計提供可行思路.
- 地質雷達 /
- 隧道病害 /
- 智能辨識 /
- 特征提取 /
- 支持向量機 /
- 希爾伯特-黃變換
Abstract: Due to the rapid construction of tunnels in China, problems that are associated with both quality and safety have become apparent. Therefore, the control and treatment of various tunnel defects are gradually becoming a primary focus during both construction and operation of tunnels. Further, a ground penetrating radar (GPR), which is based on the ultra-high frequency pulse electromagnetic wave theory, provides advantages such as efficiency and convenience. Further, GPR has been extensively used to perform nondestructive detection of tunnel defects in order to ensure sufficient quality and safety. To improve the efficiency and reliability of the GPR detection process, a novel method that identified tunnel defects using the GPR images in an intelligent manner was proposes based on the multidimensional analysis of GPR reflections. Six typical identifying features of defect signals were initially extracted based on time domain, frequency-domain, and time-frequency domain analyses. Further, automatic identification of the horizontal distribution of the defect was obtained by searching for all the defect signals using a classification model constructed by a support vector machine, which was used for training the model with the typical features. Furthermore, by calculating the depth distribution of defects according to the first intrinsic mode function (IMF1) component envelope of the defect signals, intelligent identification of tunnel defects can be achieved. A comparison between the results of the intelligent and artificial identification mechanisms when applied to a tunnel backfill measured GPR data depicts that the intelligent method illustrates a strong ability to identify defects in GPR data. Further, only a few errors are produced:the identification rate and accuracy of test data are 100% and 78.6%, respectively, which satisfies the engineering application requirements. This method can be used to intelligently identify the defects in different types of GPR data in tunnel engineering. Furthermore, the results of this study can provide some hints about the design of intelligent identification algorithms that can be applied in other areas of GPR detection with various detection target types.
- ground penetrating radar (GPR) /
- tunnel defect /
- intelligent identification /
- feature extraction /
- support vector machine (SVM) /
- hilbert-huang transform (HHT)

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參考文獻(7)

[4]	Baryshnikov V D, Khmelin A P, Denisova E V. GPR detection of inhomogeneities in concrete lining of underground tunnels. J Min Sci, 2014, 50(1):25
[8]	Fadi A A. An Automated Framework for Defect Detection in Concrete Bridge Decks Using Fractals and Independent Component Analysis[Dissertation]. Michigan:Western Michigan University, 2010
[9]	Cui Y A, Wang L, Xiao J P. Automatic feature recognition for GPR image processing. Int J Comput Inf Eng, 2010, 4(1):14
[11]	Al-Nuaimy W, Huang Y, Nakhkash M, et al. Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition. J Appl Geophys, 2000, 43(24):157
[12]	Maas C, Schmalzl J. Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar. Comput Geosci, 2013, 58:116
[13]	Laurence M, Raffaele P, Loredana M, et al. Automated detection of reflection hyperbolas in complex GPR images with no a priori knowledge on the medium. IEEE Trans Geosci Remote Sens, 2016, 54(1):580
[15]	Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A, 1998, 454:903