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Article(id=1282036080803287237, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282036038860255373, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.11.0233, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1746806400000, receivedDateStr=2025-05-10, revisedDate=1754755200000, revisedDateStr=2025-08-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1783590820741, onlineDateStr=2026-07-09, pubDate=1764259200000, pubDateStr=2025-11-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783590820741, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783590820741, creator=13701087609, updateTime=1783590820741, updator=13701087609, issue=Issue{id=1282036038860255373, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='11', pageStart='1', pageEnd='268', issueExtLink='null', onlineDate='null', pubDate='1764259200000', pubDateStr='2025-11-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783590810742, creator='13701087609', updateTime=1783593801313, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048582291276616, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282036038860255373, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048582295470921, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282036038860255373, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=131, endPage=138, ext={EN=ArticleExt(id=1282036081013002438, articleId=1282036080803287237, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Research on multi-classification detection method of wall hollow drum based on Bayesian algorithm optimization and feature fusion, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

To achieve high-precision recognition of wall hollowing sound signals and improve multi-category detection accuracy, a multi-feature fusion method for wall hollowing detection based on BO-SVM was proposed. First, the collected knocking sound signals from different wall types were preprocessed by pre-emphasis, framing, and windowing, and both MFCC and MSC were extracted. The two acoustic features were concatenated at the frame level and normalized to construct a fused feature dataset. Then, a BO-SVM classification model was developed, and the kernel function penalty and parameters were optimized using five-fold cross-validation to establish the MFCC+MSC-BO-SVM model. Finally, classification experiments were conducted using hollow and non-hollow from multiple wall types, including cement, coating, marble, and ceramic tile walls. The results show that the fused features outperform single features in terms of accuracy, recall, and F1-score. The MFCC+MSC-BO-SVM model achieves an overall recognition accuracy of 96.36%, representing improvements of 6.61%, 9.58%, 15.27%, 13.90%, and 5.02% compared with standard SVM, Random Forest, K-Nearest Neighbor, Grid Search-optimized SVM, and Chaos Particle Swarm Optimization SVM respectively. Furthermore, the BO method can obtain the optimal parameter combination with fewer iterations, showing superior convergence and classification stability.

, authors=Yinhui ZHOU1, Yong DING**, 1, Yulong WU2, Denghua LI3, 4, Dalong GE1, authorsList=Yinhui ZHOU, Yong DING, Yulong WU, Denghua LI, Dalong GE, 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=1282036081294020807, articleId=1282036080803287237, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于特征融合与贝叶斯算法优化SVM的墙面空鼓检测, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为高精度识别墙面空鼓声信号，提高多类别墙体空鼓检测准确率，提出一种基于贝叶斯算法优化-支持向量机（BO-SVM）的多特征融合空鼓检测方法。首先，对采集的不同墙体敲击声音信号进行预加重、分帧、加窗等预处理，并分别提取梅尔倒谱系数特征（MFCC）和梅尔频谱系数特征（MSC）；其次，将2种声学特征进行帧级串联融合，并归一化处理融合特征，形成融合特征数据集；然后，构建BO-SVM分类模型，并利用五折交叉验证方法优化核函数惩罚系数和参数，建立MFCC+MSC-BO-SVM模型；最后，以水泥墙、涂料墙、大理石墙和瓷砖墙等多类墙体的空鼓和非空鼓样本为对象开展分类试验。结果表明：融合特征在分类准确率、召回率和F1值等指标上均优于单一特征；MFCC+MSC-BO-SVM模型整体识别准确率为96.36%，相较于标准SVM、随机森林、K近邻算法、网格搜索优化SVM模型和混沌粒子群优化SVM模型，准确率分别提高6.61%、9.58%、15.27%、13.90%和5.02%；BO法能够在较少迭代次数内获得最优参数组合，并表现出较好的模型收敛性与分类稳定性。

, authors=周尹辉1, 丁勇副教授**, 1, 吴玉龙2, 李登华高级工程师3, 4, 葛大龙1, authorsList=周尹辉, 丁勇副教授, 吴玉龙, 李登华高级工程师, 葛大龙, authorCompany=null, correspAuthors=丁勇副教授, authorNote=

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。Email: 。

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** 丁勇（1977—），男，江苏南京人，博士，副教授，主要从事结构健康检测、智能检测的研究。E-mail: 。
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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。Email: 。

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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。Email: 。

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基于特征融合与贝叶斯算法优化SVM的墙面空鼓检测
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周尹辉 1 , 丁勇副教授 **, 1 , 吴玉龙 2 , 李登华高级工程师 3, 4 , 葛大龙 1
中国安全科学学报 | 安全工程技术 2025,35(11): 131-138
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中国安全科学学报 |安全工程技术 2025 , 35 (11) : 131 -138
基于特征融合与贝叶斯算法优化SVM的墙面空鼓检测
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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。Email: 。

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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。Email: 。

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周尹辉1 , 丁勇副教授**, 1 , 吴玉龙2, 李登华高级工程师3, 4, 葛大龙1
作者信息
  • 1南京理工大学 安全科学与工程学院，江苏 南京 210094
  • 2昆山市质量检测中心，江苏 昆山 215332
  • 3南京水利科学研究院，江苏 南京 210029
  • 4水利部水库大坝安全重点实验室，江苏 南京 210024
通讯作者:
** 丁勇（1977—），男，江苏南京人，博士，副教授，主要从事结构健康检测、智能检测的研究。E-mail: 。
作者简介:

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。Email: 。

Research on multi-classification detection method of wall hollow drum based on Bayesian algorithm optimization and feature fusion
Yinhui ZHOU1 , Yong DING**, 1 , Yulong WU2, Denghua LI3, 4, Dalong GE1
Affiliations
  • 1School of Safety Science and Engineering, Nanjing University of Science and Technology, Nanjing Jiangsu 210094, China
  • 2Kunshan Quality Inspection Center, Kunshan Jiangsu, 215332, China
  • 3Nanjing Hydraulic Research Institute, Nanjing Jiangsu 210029, China
  • 4Key Laboratory of Reservoir and Dam Safety, Ministry of Water Resources, Nanjing Jiangsu 210024, China
出版时间: 2025-11-28 doi: 10.16265/j.cnki.issn1003-3033.2025.11.0233
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为高精度识别墙面空鼓声信号，提高多类别墙体空鼓检测准确率，提出一种基于贝叶斯算法优化-支持向量机（BO-SVM）的多特征融合空鼓检测方法。首先，对采集的不同墙体敲击声音信号进行预加重、分帧、加窗等预处理，并分别提取梅尔倒谱系数特征（MFCC）和梅尔频谱系数特征（MSC）；其次，将2种声学特征进行帧级串联融合，并归一化处理融合特征，形成融合特征数据集；然后，构建BO-SVM分类模型，并利用五折交叉验证方法优化核函数惩罚系数和参数，建立MFCC+MSC-BO-SVM模型；最后，以水泥墙、涂料墙、大理石墙和瓷砖墙等多类墙体的空鼓和非空鼓样本为对象开展分类试验。结果表明：融合特征在分类准确率、召回率和F1值等指标上均优于单一特征；MFCC+MSC-BO-SVM模型整体识别准确率为96.36%，相较于标准SVM、随机森林、K近邻算法、网格搜索优化SVM模型和混沌粒子群优化SVM模型，准确率分别提高6.61%、9.58%、15.27%、13.90%和5.02%；BO法能够在较少迭代次数内获得最优参数组合，并表现出较好的模型收敛性与分类稳定性。

特征融合  /  贝叶斯优化（BO）  /  支持向量机（SVM）  /  墙面空鼓检测  /  梅尔倒谱系数特征（MFCC）  /  梅尔频谱系数特征（MSC）

To achieve high-precision recognition of wall hollowing sound signals and improve multi-category detection accuracy, a multi-feature fusion method for wall hollowing detection based on BO-SVM was proposed. First, the collected knocking sound signals from different wall types were preprocessed by pre-emphasis, framing, and windowing, and both MFCC and MSC were extracted. The two acoustic features were concatenated at the frame level and normalized to construct a fused feature dataset. Then, a BO-SVM classification model was developed, and the kernel function penalty and parameters were optimized using five-fold cross-validation to establish the MFCC+MSC-BO-SVM model. Finally, classification experiments were conducted using hollow and non-hollow from multiple wall types, including cement, coating, marble, and ceramic tile walls. The results show that the fused features outperform single features in terms of accuracy, recall, and F1-score. The MFCC+MSC-BO-SVM model achieves an overall recognition accuracy of 96.36%, representing improvements of 6.61%, 9.58%, 15.27%, 13.90%, and 5.02% compared with standard SVM, Random Forest, K-Nearest Neighbor, Grid Search-optimized SVM, and Chaos Particle Swarm Optimization SVM respectively. Furthermore, the BO method can obtain the optimal parameter combination with fewer iterations, showing superior convergence and classification stability.

feature fusion  /  Bayesian optimization (BO)  /  support vector machine (SVM)  /  wall hollow drum detection  /  Mel-frequency cepstral coefficients (MFCC)  /  Mel-spectral coefficients (MSC)
周尹辉, 丁勇副教授, 吴玉龙, 李登华高级工程师, 葛大龙. 基于特征融合与贝叶斯算法优化SVM的墙面空鼓检测. 中国安全科学学报, 2025 , 35 (11) : 131 -138 . DOI: 10.16265/j.cnki.issn1003-3033.2025.11.0233
Yinhui ZHOU, Yong DING, Yulong WU, Denghua LI, Dalong GE. Research on multi-classification detection method of wall hollow drum based on Bayesian algorithm optimization and feature fusion[J]. China Safety Science Journal, 2025 , 35 (11) : 131 -138 . DOI: 10.16265/j.cnki.issn1003-3033.2025.11.0233
基金
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  • 国家重点研发计划资助项目(2024YFC3210703)
  • 国家自然科学基金资助(U2240221)
  • 中央级公益性科研院所基本科研业务费专项资金资助项目(Y724011)
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doi: 10.16265/j.cnki.issn1003-3033.2025.11.0233
  • 接收时间：2025-05-10
  • 首发时间：2026-07-09
  • 出版时间：2025-11-28
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  • 收稿日期：2025-05-10
  • 修回日期：2025-08-10
基金
国家重点研发计划资助项目(2024YFC3210703)
国家自然科学基金资助(U2240221)
中央级公益性科研院所基本科研业务费专项资金资助项目(Y724011)
作者信息
    1南京理工大学 安全科学与工程学院，江苏 南京 210094
    2昆山市质量检测中心，江苏 昆山 215332
    3南京水利科学研究院，江苏 南京 210029
    4水利部水库大坝安全重点实验室，江苏 南京 210024

通讯作者:

** 丁勇（1977—），男，江苏南京人，博士，副教授，主要从事结构健康检测、智能检测的研究。E-mail: 。
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https://castjournals.cast.org.cn/joweb/zgaqkxxb/CN/10.16265/j.cnki.issn1003-3033.2025.11.0233
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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
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