Article(id=1281627199727964188, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.01.0326, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757433600000, receivedDateStr=2025-09-10, revisedDate=1762704000000, revisedDateStr=2025-11-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1783493335896, onlineDateStr=2026-07-08, pubDate=1769529600000, pubDateStr=2026-01-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783493335896, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783493335896, creator=13701087609, updateTime=1783493335896, updator=13701087609, issue=Issue{id=1281627156769903305, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='1', pageStart='1', pageEnd='280', issueExtLink='null', onlineDate='null', pubDate='1769529600000', pubDateStr='2026-01-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783493325654, creator='13701087609', updateTime=1783588714348, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027246013624992, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027246013624993, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=167, endPage=173, ext={EN=ArticleExt(id=1281627199925096477, articleId=1281627199727964188, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Hollowing signal acquisition device and intelligent identification algorithm, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=

Traditional manual methods for detecting wall hollowing suffered from strong subjectivity, low efficiency, and difficulties in large-scale application. To address these issues, this study proposed an intelligent detection method based on a fully automatic hollowing signal acquisition device and an optimized signal processing algorithm. Firstly, a fully automatic hollowing signal acquisition device capable of stable operation on building walls was designed to achieve standardized tapping and high-precision acoustic signal acquisition. Secondly, VMD and EEMD optimized by Bayesian Optimization (BO) were employed to denoise the original signals, thereby enhancing the features of hollowing signals. Then, MSC and MFCC features of the signals were extracted and fused at the frame level to form an MFCC+MSC feature set. Finally, a majority voting ensemble learning model was utilized for classification, enabling high-precision hollowing detection. The results indicate that the classification accuracy of the proposed method reaches 99.31%, significantly outperforming traditional methods. These results validate the feasibility and effectiveness of combining automated devices with optimized signal processing techniques for wall hollowing detection.

, authors=Yinhui ZHOU¹, Yong DING^{**, 1}, Denghua LI^{2, 3}, authorsList=Yinhui ZHOU, Yong DING, Denghua LI, authorCompany=null, correspAuthors=Yong DING, 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=1281627200197726238, articleId=1281627199727964188, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=空鼓信号采集装置及其智能化识别算法, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

为解决墙面空鼓监测中传统人工检测方法存在的主观性强、效率低、难以大规模应用等问题，提出一种基于全自动空鼓信号采集装置与优化信号处理算法的智能化识别算法。首先，设计一种能够稳定运行于建筑墙面的全自动空鼓信号采集装置，采集标准化敲击与高精度声学信号；然后，采用贝叶斯优化（BO）的变分模态分解（VMD）与集合经验模态分解（EEMD）对原始信号作降噪处理，增强空鼓信号特征；然后，提取信号的梅尔频谱（MSC）和梅尔倒谱系数（MFCC）特征，并进行帧级融合，形成MFCC+MSC特征集；最后，利用多数投票集成学习模型分类，进行高精度的空鼓检测。结果表明：文中方法的分类准确率达99.31%，显著优于传统方法，验证了自动化装置与优化信号处理技术结合在墙面空鼓检测中的可行性与有效性。

, authors=周尹辉¹, 丁勇副教授^{**, 1}, 李登华高级工程师^{2, 3}, authorsList=周尹辉, 丁勇副教授, 李登华高级工程师, authorCompany=null, correspAuthors=丁勇副教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=CxKbFgfgNHoOV3hZRLBr+g==, magXml=E8lGCzm6Jh0z+sttuTVOiw==, pdfUrl=null, pdf=DS7ZKwSK11Lkyd5sGUUTLA==, pdfFileSize=5389710, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=cwkNY45jyy706QHthKs+xQ==, mapNumber=null, fund=null)}, authors=[Author(id=1281627200793317417, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627199727964188, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=1311485190@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281627201074335787, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627199727964188, authorId=1281627200793317417, language=EN, stringName=Yinhui ZHOU, firstName=Yinhui, middleName=null, lastName=ZHOU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Safety Science and Engineering, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281627201166610476, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627199727964188, authorId=1281627200793317417, language=CN, stringName=周尹辉, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹南京理工大学 安全科学与工程学院，江苏 南京，210094, bio={"content":"

周尹辉（2000—），男，河南郑州人，硕士研究生，主要研究方向为建筑无损检测、智能检测、人工智能、信号处理。E-mail: 1311485190@qq.com。

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周尹辉（2000—），男，河南郑州人，硕士研究生，主要研究方向为建筑无损检测、智能检测、人工智能、信号处理。E-mail: 1311485190@qq.com。

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