Article(id=1308453971056677430, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453947069452650, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.01.014, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744646400000, receivedDateStr=2025-04-15, revisedDate=1753459200000, revisedDateStr=2025-07-26, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889336599, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889336599, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889336599, creator=13701087609, updateTime=1789889336599, updator=13701087609, issue=Issue{id=1308453947069452650, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='1', pageStart='1', pageEnd='165', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789889330879, creator='13701087609', updateTime=1789889788596, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308455866915639450, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453947069452650, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308455866915639451, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453947069452650, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=118, endPage=123, ext={EN=ArticleExt(id=1308453972973474359, articleId=1308453971056677430, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=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, columnId=1152669336394183038, journalTitle=Urban Rapid Rail Transit, columnName=Civil Engineering Technology, runingTitle=null, highlight=null, articleAbstract=

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.

, authors=Yan BAO¹, Lejun PEI¹, Chao TANG^{2, 3}, Li WANG¹, authorsList=Yan BAO, Lejun PEI, Chao TANG, Li WANG, authorCompany=null, correspAuthors=Chao TANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1308453973581648440, articleId=1308453971056677430, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=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, columnId=1152669336603898239, journalTitle=都市快轨交通, columnName=土建技术, runingTitle=null, highlight=null, articleAbstract=

随着城市轨道交通服役年限的增长，地铁隧道衬砌壁后稳定性每况愈下，针对衬砌壁后空洞检测难的问题，冲击回波法被开发作为检测此类病害的新手段，其中有效提取检测数据复杂信号中的回波振荡特征成为检测壁后空洞的关键。依托“管片-围岩”足尺模型壁后空洞检测试验，提出基于Butterworth带通滤波的信号粗筛策略联合基于Daubechies8小波变换的局部噪声平滑的去噪方法。该方法使用4阶带通滤波，设置1和50 kHz的高、低频截止频率，有效抑制频段外的信号及噪声；基于改进的自适应阈值法的小波变换平滑信号尖刺，精细化去除频段内的残余噪声。结果表明，试验数据的二维时频图经该方法去噪后，在频域上抑制高频尖峰，去除低频离散噪声，通带内的回波信号得到平滑处理；在时域上保留回波信号的分布特征，减少噪声干扰。所提联合去噪法显著改善回波信号振荡特征的提取效果，为冲击回波法检测地铁隧道衬砌壁后空洞提供前期研究基础。

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鲍艳，女，博士，教授，博士生导师，从事重大基础设施智慧运维研究，baoy@bjut.edu.cn

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鲍艳，女，博士，教授，博士生导师，从事重大基础设施智慧运维研究，baoy@bjut.edu.cn

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