收藏切换
Experimental Study on Void Detection Behind Subway Tunnel Lining and Signal Denoising Method: Using a Combined Denoising Method Based on Butterworth Bandpass Filter and Daubechies8 Wavelet Transform
收藏切换
Yan BAO1, Lejun PEI1, Chao TANG2, 3, Li WANG1
Urban Rapid Rail Transit | 2026, 39(1) : 118 - 123
Less
收藏切换
Urban Rapid Rail Transit | 2026, 39(1): 118-123
• Civil Engineering Technology •
Experimental Study on Void Detection Behind Subway Tunnel Lining and Signal Denoising Method: Using a Combined Denoising Method Based on Butterworth Bandpass Filter and Daubechies8 Wavelet Transform
Full
Yan BAO1, Lejun PEI1, Chao TANG2, 3, Li WANG1
Affiliations
  • 1.The Key Laboratory of Urban Security and Disaster Engineering of China Ministry of Education, Beijing University of Technology, Beijing 100124
  • 2.China University of Geosciences, Beijing, Beijing 100083
  • 3.Beijing Urban Construction Exploration & Surveying Design Research Institute Co., Ltd., Beijing 100101
doi: 10.3969/j.issn.1672-6073.2026.01.014
Outline
收藏切换

With the increase of service life of urban rail transit, the stability of subway tunnel lining is getting worse and worse. Aiming at the problem of difficulty in detecting voids behind lining, impact echo method has been developed as a new means to detect such diseases. Among them, effectively extracting echo oscillation characteristics from complex signals of detection data has become the key to detecting voids behind lining. Based on the voids detection experiment behind "segment-surrounding rock" full-scale model, this paper proposes a denoising method based on Butterworth bandpass filter signal coarse screening strategy combined with local noise smoothing based on Daubechies8 wavelet transform. This method uses 4th-order bandpass filter and sets high and low frequency cutoff frequencies of 1 kHz and 50 kHz to effectively suppress signals and noise outside the frequency band; wavelet transform based on improved adaptive threshold method smoothes signal spikes and finely removes residual noise within the frequency band. The results show that after denoising the two-dimensional time-frequency diagram of experimental data by this method, high-frequency spikes are suppressed in the frequency domain, low-frequency discrete noise is removed, and echo signals in the passband are smoothed; the distribution characteristics of echo signals are retained in the time domain to reduce noise interference. The joint denoising method proposed in this paper significantly improves the extraction effect of the echo signal oscillation characteristics, and provides a preliminary research basis for the impact echo method to detect voids behind the lining of subway tunnels.

urban rail transit  /  subway tunnel  /  void behind lining  /  impact echo  /  Butterworth bandpass filtering  /  Daubechies8 wavelet transform  /  signal denoising
鲍艳, 裴乐君, 唐超, 王丽. 地铁隧道衬砌壁后空洞检测试验与信号去噪方法研究——基于Butterworth带通滤波与Daubechies8小波变换联合去噪法. 都市快轨交通, 2026 , 39 (1) : 118 -123 . DOI: 10.3969/j.issn.1672-6073.2026.01.014
Yan BAO, Lejun PEI, Chao TANG, Li WANG. Experimental Study on Void Detection Behind Subway Tunnel Lining and Signal Denoising Method: Using a Combined Denoising Method Based on Butterworth Bandpass Filter and Daubechies8 Wavelet Transform[J]. Urban Rapid Rail Transit, 2026 , 39 (1) : 118 -123 . DOI: 10.3969/j.issn.1672-6073.2026.01.014
Year 2026 volume 39 Issue 1
127
28
Cite this Article
BibTeX
Article Info
doi: 10.3969/j.issn.1672-6073.2026.01.014
  • Receive Date:2025-04-15
  • Online Date:2026-09-20
Article Data
Affiliations
History
  • Received:2025-04-15
  • Revised:2025-07-26
Funding
Affiliations
    1.The Key Laboratory of Urban Security and Disaster Engineering of China Ministry of Education, Beijing University of Technology, Beijing 100124
    2.China University of Geosciences, Beijing, Beijing 100083
    3.Beijing Urban Construction Exploration & Surveying Design Research Institute Co., Ltd., Beijing 100101
References
Share
https://castjournals.cast.org.cn/joweb/dskgjt/EN/10.3969/j.issn.1672-6073.2026.01.014
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT