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Article(id=1149789606666789482, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768563956826506, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2404734, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1719244800000, receivedDateStr=2024-06-25, revisedDate=1741276800000, revisedDateStr=2025-03-07, acceptedDate=null, acceptedDateStr=null, onlineDate=1752060804287, onlineDateStr=2025-07-09, pubDate=1749312000000, pubDateStr=2025-06-08, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752060804287, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752060804287, creator=13701087609, updateTime=1752060804287, updator=13701087609, issue=Issue{id=1149768563956826506, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='16', pageStart='6587', pageEnd='7021', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752055787314, creator=13701087609, updateTime=1768456850262, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1218559607937618069, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768563956826506, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1218559607937618070, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149768563956826506, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=6890, endPage=6897, ext={EN=ArticleExt(id=1149789606952002155, articleId=1149789606666789482, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Intelligent Detection Method and Device for Void of Composite Bridge Tower Based on Vibration and Acoustic Signals: Take the Composite Structure Bridge Tower of Zhangjinggao Yangtze River Bridge as an Example, columnId=1156262729917780302, journalTitle=Science Technology and Engineering, columnName=Papers·Architectural Science, runingTitle=null, highlight=null, articleAbstract=

To achieve rapid automatic detection and identification of void damage in high-rise composite structures, a bridge tower full-scale model was tested for damage using Zhangjinggao Yangtze River Bridge's composite structure tower. Through numerical simulation of sound field spatial distribution, time-frequency response characteristics comparison analysis, and convolutional neural network(CNN) model training and visualization. An automatic device for void detection of high-rise composite structures and a deep learning detection method based on acoustic signals were proposed. The results demonstrate that the acoustic signal analysis method based on automatic device acquisition can be used as a new approach for automatic detection and identification of void damage in high-rise composite structures. The constructed CNN model can achieve high-precision classification of structural void state, and the recognition accuracy is 96.8%. The automatic device and intelligent detection method enable automatic real-time detection and classification of high-rise composite structures, improving automation and reducing safety risks.

, correspAuthors=Hang YUAN, 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, authorCompany=null, fund=null, authors=null, authorsList=Hang YUAN, Xin-peng YOU, Feng-chao GUO), CN=ArticleExt(id=1149789629911621687, articleId=1149789606666789482, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=基于振动声信号的组合结构界面脱空智能检测方法及装置: 以张靖皋长江大桥等组合结构桥塔为例, columnId=1156262730517565784, journalTitle=科学技术与工程, columnName=论文·建筑科学, runingTitle=null, highlight=null, articleAbstract=

为实现对高耸组合结构界面脱空部位的快速自动化识别检测,以张靖皋长江大桥等组合结构桥塔为背景,开展桥塔足尺模型脱空检测试验,通过对声场空间分布数值模拟、时频响应特征分析及卷积神经网络(convolutional neural network,CNN)模型训练,提出一种适用高耸组合结构界面脱空检测的自动化装置及基于声信号的脱空损伤检测深度学习方法。结果表明:基于自动化装置采集的声信号分析方法可作为高耸组合结构脱空损伤自动化检测识别新方法,构建的卷积神经网络模型能够实现对结构损伤、健康及无效数据的高精度分类,分类识别准确率达96.8%,自动化检测装置可实现对高耸组合结构界面脱空的准确与快速检测,该装置及方法可提升超高组合索塔脱空检测的自动化程度降低检测安全风险。

, correspAuthors=袁航, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=gsICEYIK/PownRzDxKcI9A==, magXml=II81uS+EPTe8NWxbOeos3g==, pdfUrl=null, pdf=GeCUVkxiumwmdSrqgZMBVw==, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=xekp/QhhcN3F5gw2Wsw/hA==, mapNumber=null, authorCompany=null, fund=null, authors=

袁航(1992—),男,汉族,河南洛阳人,硕士,工程师。研究方向:大跨桥梁智能建造技术。E-mail:。

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袁航(1992—),男,汉族,河南洛阳人,硕士,工程师。研究方向:大跨桥梁智能建造技术。E-mail:。

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袁航(1992—),男,汉族,河南洛阳人,硕士,工程师。研究方向:大跨桥梁智能建造技术。E-mail:。

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Classification evaluation results of different features

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超参数 最佳值
优化器 Adam
初始学习率 0.000 3
迭代次数 120
卷积核大小 3×3
滤波器数量 32、64、128、128、128
步长 1
Dropout比例 0.2
), ArticleFig(id=1177977307722560103, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789606666789482, language=CN, label=表1, caption=

卷积神经网络超参数

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超参数 最佳值
优化器 Adam
初始学习率 0.000 3
迭代次数 120
卷积核大小 3×3
滤波器数量 32、64、128、128、128
步长 1
Dropout比例 0.2
), ArticleFig(id=1177977307781280360, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149789606666789482, language=EN, label=Table 2, caption=

Classification evaluation results of different features

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评测指标 类别 时域信号
统计特征		 频域特征
频幅曲线		 时频特征
小波时频谱 梅尔频谱
损伤 65.8 75.8 90.7 97.2
召回率/% 无效 57.5 68.2 95.1 97.6
健康 49.1 62.3 80.6 95.7
损伤 41.6 51.5 81.7 91.3
精确度/% 无效 56.9 57.3 92.9 98.1
健康 58.7 62.4 89.7 97.4
损伤 52.7 64.4 86.5 96.8
准确率/% 无效 52.7 64.4 86.5 96.8
健康 52.7 64.4 86.5 96.8
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不同特征的分类评测结果

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评测指标 类别 时域信号
统计特征		 频域特征
频幅曲线		 时频特征
小波时频谱 梅尔频谱
损伤 65.8 75.8 90.7 97.2
召回率/% 无效 57.5 68.2 95.1 97.6
健康 49.1 62.3 80.6 95.7
损伤 41.6 51.5 81.7 91.3
精确度/% 无效 56.9 57.3 92.9 98.1
健康 58.7 62.4 89.7 97.4
损伤 52.7 64.4 86.5 96.8
准确率/% 无效 52.7 64.4 86.5 96.8
健康 52.7 64.4 86.5 96.8
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Comparative analysis of different detection methods

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检测方法 检测效率/(min·m-2) 检测精度/% 检测成本
人工敲击法 1 50.0 低
冲击回波法 5 78.5 中等
压电陶瓷法 10 88.3 高
机器人智能检测 2 96.8 低
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不同检测方法对比分析结果

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检测方法 检测效率/(min·m-2) 检测精度/% 检测成本
人工敲击法 1 50.0 低
冲击回波法 5 78.5 中等
压电陶瓷法 10 88.3 高
机器人智能检测 2 96.8 低
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基于振动声信号的组合结构界面脱空智能检测方法及装置: 以张靖皋长江大桥等组合结构桥塔为例
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袁航 1 , 游新鹏 1, 2 , 郭峰超 3
科学技术与工程 | 论文·建筑科学 2025,25(16): 6890-6897
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科学技术与工程 | 论文·建筑科学 2025, 25(16): 6890-6897
基于振动声信号的组合结构界面脱空智能检测方法及装置: 以张靖皋长江大桥等组合结构桥塔为例
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袁航1 , 游新鹏1, 2, 郭峰超3
作者信息
  • 1 中交第二航务工程局有限公司, 武汉 430040
  • 2 同济大学土木工程学院, 上海 200125
  • 3 广东湾区交通建设投资有限公司, 广州 510000

    • 袁航(1992—),男,汉族,河南洛阳人,硕士,工程师。研究方向:大跨桥梁智能建造技术。E-mail:。

Intelligent Detection Method and Device for Void of Composite Bridge Tower Based on Vibration and Acoustic Signals: Take the Composite Structure Bridge Tower of Zhangjinggao Yangtze River Bridge as an Example
Hang YUAN1 , Xin-peng YOU1, 2, Feng-chao GUO3
Affiliations
  • 1 CCCC Second Harbour Engineering Co. , Ltd. , Wuhan 430040, China
  • 2 College of Civil Engineering, Tongji University, Shanghai 200215, China
  • 3 Guangdong Bay Area Transportation Construction Investment Co. , Ltd. , Guangzhou 510000, China
出版时间: 2025-06-08 doi: 10.12404/j.issn.1671-1815.2404734
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为实现对高耸组合结构界面脱空部位的快速自动化识别检测,以张靖皋长江大桥等组合结构桥塔为背景,开展桥塔足尺模型脱空检测试验,通过对声场空间分布数值模拟、时频响应特征分析及卷积神经网络(convolutional neural network,CNN)模型训练,提出一种适用高耸组合结构界面脱空检测的自动化装置及基于声信号的脱空损伤检测深度学习方法。结果表明:基于自动化装置采集的声信号分析方法可作为高耸组合结构脱空损伤自动化检测识别新方法,构建的卷积神经网络模型能够实现对结构损伤、健康及无效数据的高精度分类,分类识别准确率达96.8%,自动化检测装置可实现对高耸组合结构界面脱空的准确与快速检测,该装置及方法可提升超高组合索塔脱空检测的自动化程度降低检测安全风险。

组合结构  /  桥塔  /  脱空检测  /  声信号  /  卷积神经网络(CNN)

To achieve rapid automatic detection and identification of void damage in high-rise composite structures, a bridge tower full-scale model was tested for damage using Zhangjinggao Yangtze River Bridge's composite structure tower. Through numerical simulation of sound field spatial distribution, time-frequency response characteristics comparison analysis, and convolutional neural network(CNN) model training and visualization. An automatic device for void detection of high-rise composite structures and a deep learning detection method based on acoustic signals were proposed. The results demonstrate that the acoustic signal analysis method based on automatic device acquisition can be used as a new approach for automatic detection and identification of void damage in high-rise composite structures. The constructed CNN model can achieve high-precision classification of structural void state, and the recognition accuracy is 96.8%. The automatic device and intelligent detection method enable automatic real-time detection and classification of high-rise composite structures, improving automation and reducing safety risks.

composite structure  /  bridge tower  /  void detection  /  acoustic signal  /  convolutional neural network (CNN)
袁航, 游新鹏, 郭峰超. 基于振动声信号的组合结构界面脱空智能检测方法及装置: 以张靖皋长江大桥等组合结构桥塔为例. 科学技术与工程, 2025 , 25 (16) : 6890 -6897 . DOI: 10.12404/j.issn.1671-1815.2404734
Hang YUAN, Xin-peng YOU, Feng-chao GUO. Intelligent Detection Method and Device for Void of Composite Bridge Tower Based on Vibration and Acoustic Signals: Take the Composite Structure Bridge Tower of Zhangjinggao Yangtze River Bridge as an Example[J]. Science Technology and Engineering, 2025 , 25 (16) : 6890 -6897 . DOI: 10.12404/j.issn.1671-1815.2404734
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随着超大跨径桥梁的建设,桥塔逐渐向着400 m级的高度发展,对于超高桥塔采用钢-混凝土组合结构所呈现的承载力高、延性好、结构自重轻、施工快速等优点逐渐显露。但由于混凝土收缩徐变、施工工艺及恶劣的服役条件等因素,导致混凝土脱空缺陷在大型钢-混凝土结构中容易产生,钢管混凝土界面脱空缺陷可能会引发结构损伤与破坏进而影响结构的承载力,因此脱空损伤检测成为组合结构安装及施工完成后必不可少的步骤。目前针对钢混组合结构脱空检测,《钢管混凝土混合结构技术标准》(GB/T 51446—2021)[1]建议采用人工小锤敲击的方法配合超声波进行检测,通过人员听取敲击声音确定是否脱空异常,再通过超声波进行确认并修补,但该方法在噪声环境下适应性差、受敲击力度及人为主观判断因素影响大,检测效率与准确率较低。
学者们开展了大量脱空损伤检测技术的研究,提出检测新方法。骆勇鹏等[2]提出了基于激励振动的时间序列分析理论,通过分析建立时序自回归模型,构建以马氏距离及科氏距离的混凝土脱空缺陷识别指标。张戎令等[3]提出依据超声波的传播规律,根据首波声时法推导出钢管砼中心缺陷定量计算公式,给出对称探测法和扇形检测法检测钢管混凝土不同缺陷适用性的建议。李军等[4]利用冲击回波法对钢板衬砌混凝土叠合结构表面脱空质量缺陷进行检测并提出频率幅值密度比作为定量检测识别参数。陈劲等[5]将红外热成像法与超声波法相结合实现了混凝土缺陷的量化识别。柯钧豪等[6]提出了基于外贴式压电陶瓷的应力波测量小波包分析的钢管混凝土构件界面剥离检测的方法。张长亮等[7]基于冲击映像法提出了钢壳混凝土脱空质量评价指标分析了深中通道钢壳混凝土界面脱空。目前对于组合结构界面脱空检测均主要以激励产生的振动信号为媒介,采用耦合传感器等对激励产生的加速度时序序列进行时域特征及频域特征的统计分析,这些方法主要以人工主动激励并结合耦合传感器获取信号进行人为分析判断,而对于超高组合索塔等高耸临空危险作业检测,人员需在高耸位置进行激励及耦合信合接收作业,操作难度大、危险性极高。
近年来,随着机器学习等人工智能技术的发展,深度学习算法在对于数据流的固有属性及规律的深度挖掘表现的更加快速与准确,尤其在特征分类与检测方面[8-14]。针对以上问题,提出一种自动化采集装置及基于声信号的钢管混凝土脱空损伤检测深度学习方法。首先建立流-固耦合的钢-混组合结构有限元模型,对外部激励下损伤及非损伤模型中的声信号特征进行分析,验证方法的可能性。其次,建立足尺模型试验采集不同尺度脱空损伤声信号,构建基于声信号时频特征的深度学习网络,并研发磁吸爬壁式自动化检测机器人,从而实现组合结构脱空损伤状态的快速自动化智能检测与识别。
1 声场空间分布数值模拟
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1.1 有限元模型建立
根据相关声学理论可知,当对处在于流-固耦合空间内的结构表面施加脉冲激励后,将在固体内部产生首波、横波及纵波,而在流体内部产生直接声波及瑞雷波,同时在流-固耦合表面处产生界面波[15]。为验证基于声信号的脱空损伤检验分析方法的有效性,建立实体结构有限元模型开展空气-钢板-混凝土流固耦合有限元分析模拟。结合张靖皋大桥桥塔实体结构,建立几何尺寸为Φ1 000 mm×600 mm有限元模型,内部采用C60混凝土实体、外壳采用30 mm厚的钢板,脱空损伤位于混凝土与钢壳的接触面,脱空厚度为10 mm,平面尺寸为10 cm×10 cm,外围设置1.5 m(长)×1.5 m(宽)×1 m(高)的场外环境作为空气流体如图1所示,用以分析在激励振动产生声音辐射后不同损伤模型形成的声压分布与传播规律。
1.2 声场模拟分析
通过结构振动及损伤理论分析可知,损伤位置处结构刚度相对减弱,在外部激励作用下结构的共振频率较健康位置处将显著降低,另外振动的持续时间也将随之变长,声音强度将变强[16]。结合实际现场及试验情况下人员检测对组合结构的激励,基于流固耦合有限元模型建立同强度下的脉冲激励,构建质量源激励分别作用于脱空损伤区域及同条件的健康结构位置,分析不同位置情况下空气流体中的声压及声强分布规律。根据流固耦合模型分析结果如图2可知,在同条件情况下脱空损伤位置处较非脱空位置处的声波逸散更慢影响范围更广、声压强度更高,另根据模态分析可知损伤模型较健康模型的基频更低,与结构振动与损伤理论分析结果一致。通过理论与有限元模型分析可知采用激励损伤模型声信号的分析方法可明显区分损伤与非损伤结构,该方法可有效用于组合结构的脱空损伤识别。
2 组合结构脱空自动化检测装置及试验
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2.1 自动化脱空检测装置
为能实现高耸组合结构的全过程脱空自动化检测,提出基于磁吸机器人的自行走激励、声信号采集与分析的检测装置如图3所示。信号激振主体为设置在机器人前端的钢制球体,通过机器人控制系统电磁激发实现对钢壁板力度均一化的激励振动,激励尺度与机器人行走协同。信号采集采用带隔音装置的高频单向麦克风,在力锤激励的同时进行声信号采集,基基于单向麦克风的声音采集技术可有效提取声源并抑制其他噪声。声信号的非接触式传感器在实体工程检测中,可随自动化机器人高效移动且无需耦合,相对传统耦合振动传感器可提高信号的采集准确率及速度。机器人内置计算单元,通过部署训练完成的深度学习算法对采集声音信号进行计算分析并对损伤状态进行识别。通过对磁吸式爬壁机器人的检测、控制及状态识别模块的部署,自动化机器人按照规划路径进行爬壁行走,行走速度达1 m/s,行走过程以一定间距为基准进行力锤激励与信号采集,并针对所在位置处的信号进行实时识别与判定。
2.2 脱空检测试验及声信号数据采集与处理
为研究钢混组合结构在脱空损伤状态下的受到激励后的信号响应特征,依托张靖皋长江大桥钢箱-钢管组合结构及狮子洋大桥钢板组合结构桥塔开展足尺模型测试。其中,张靖皋长江大桥索塔选定内部单根钢管建立桥塔足尺模型,钢管直径3.6 m,壁厚 30 mm,高度6 m,内部灌注C60自密实混凝土,钢管壁上预先粘贴厚度为7、10 mm,尺寸为100~150 mm多尺寸规格的泡沫片来模拟实际情况下组合桥塔结构可能会产生的界面脱空损伤。狮子洋大桥索塔选定为T28塔柱节段,节段尺寸10.3 m×14.3 m,壁厚18 cm,高度3 m,内部灌注C80混凝土,在钢壳内侧张贴厚度分别为10、20 mm,规格尺寸分别为50~80 mm预设缺陷。具体缺陷布置如图4、图5所示。
脱空检测试验在两个大桥的钢混组合桥塔足尺模型上完成,模型位于室外施工现场与常规检测环境保持一致,具备相同的施工噪声等干扰因素。利用机器人在足尺模型外侧钢壁板上进行声信号自动采集,控制机器人分别爬行至预设缺陷区域及非脱空区域,由机器人控制系统触发敲击激励并在同时触发信号采集系统进行声信号采集。此外,预先考虑机器人在信号触发或接收过程的不稳定性,在试验检测过程采用人工干预等方法获取无效声信号。声信信号采集装置频率设置为51.2 kHz,采样时间设置为0.2 s,每组声信号序列数为10 240。基于两种模型共采集样本1 278组,其中脱空信号512组,健康数据575组,无效声信号191组。
为了进一步过滤环境噪声影响,数据采集由激励锤击传感器触发,在激励动作发生的同时触发阵列麦克风获取声信号序列如图6所示。为提高采样数据的分析效率,采集完成后针对声信号进行预处理,通过在时序序列中增加窗口按照帧率进行快速傅里叶变换并获取该窗口内的频率方差,通过对方差的过滤,精准选取携带激励信息的时序序列,缩减序列中冗余无效数组,再通过高通及低通滤波消除环境噪声的影响。相比常规人工敲击自动化采集的声信号由电信号控制采用统一标准相同激励,减少对缺陷判断的阈值和基准特征的影响,通过数据预处理分别获取3种声音时序序列(脱空信号、健全信号、无效信号)如图7所示,脱空信号与健全信号在厚板的条件下不能看出直接显著差异,需持续从时域、频域及时频域中进行特征提取与分析,无效信号则相对有效信号具明显特异性区别。
为获取高效脱空检测模型,针对组合结构检测声信号进行特征提取,分别使用声信号的时域、频域以及时频域特征开展神经网络模型的训练,对比在监督学习中不同条件特征下模型训练的准确率,优选可适用于深度学习模型检测的信号特征。其中,在时域信号方面选取信号的平均值、最大值、标准差、方差、峰度、偏度、均方根作为模型输入特征值;在频域方面通过时域信号快速傅里叶变化获取幅值谱作为特征如图8所示;在时频域中采用梅尔尺度转换梅尔频谱图(图9)作为特征进行训练。
3 基于深度学习的脱空识别检测方法研究
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3.1 脱空智能识别检测模型建立与训练
建立基于卷积神经网络架构的深度学习模型进行组合结构脱空损伤识别如图10所示,形成脱空损伤、健康结构及无效数据的三分类识别模型,模型根据选定的特征值自动从输入数据中提取细化特征并输出分类。
卷积层是卷积神经网络的主要组成部分,通过生成的特征映射滤波器检测输入的声信号特征元素。每个卷积层的激活函数采用ReLU函数,将上一层神经元的输出特征值作为本神经元的输入值,并将神经元的输入映射到输出端;池化层是特征选择和信息过滤的过程,通过池化后减少特征图的尺寸维度并引入不变性,可减少学习参数的数量,降低计算量;批量归一化方法可加速学习与训练过程,减少初始化的影响,可将各层的输入保持在相同的数值范围内,归一化表达式为
y= x - μ σ 2 + εγ+β
式(1)中:x为训练轮次输入特征值;σ、μ分别为特征值的方差与均值;γ和β为可训练参数;ε为常数避免出现方差为0的状况。
最后一层是全连接层作为神经网络的输出层,用于实现对输入特征的全局特征提取和非线性变换,该特征被反馈到softmax激活函数用以输出分类预测,softmax函数定义为
Pi= e a i ∑ j = 1 n e a j
式(2)中:Pi为输出样本不同类别的概率值;ai为输入分类为i的元素;n为样本分类总数。
为了避免在模型训练过程过拟合,在神经网络全连接层中增加Dropout来随机删除神经网络中部分神经元之间的链接,以此来降低全连接层的复杂度同时防止训练时过度拟合,在本模型训练过程中Dropout设定为20%。基于梅尔频谱的CNN神经网络模型超参数如表1所示,为有效地确定最佳超参数组合,采用K折交叉验证和网格搜索的方法对网络结构中超参数进修正确定。
3.2 脱空检测模型分类评价
两类试验共计获取的1 278组声信号,其中健康数据575组、无效声信号191组、脱空信号512组。针对采集信号进行数据截取、降噪及特征提取等预处理,建立基于神经网络模型的训练集与测试集,选取80%数组作为训练集,20%数组作为测试集,开展模型性能的评估。针对输入声信号预处理的不同特征分别开展一维及二维卷积神经网络模型训练及对比,得到训练结果如表2所示。可以看出,采用时域信号统计值特征及频域信号的频谱曲线特征进行训练的模型得到的分列准确率均相对较低,不能作为获取结构损伤、健康及无效信号的分类特征,而采用时频特征的小波时频谱及梅尔频谱特征则均能保证一定的准确率,其中小波时频特征准确率可达86.5%,梅尔频谱可达96.8%。根据钢混组合结构表面波传播理论分析可知,由于桥塔作为大吨位的压弯构件,桥塔组合结构通常采用较厚的钢板,表面波的浸入混凝土较薄板而言深度显著减小,由脱空损伤所形成的常规特征如声强增加等现象等时域特征将不再显著。而能将信号在时间及频率上进行尺度划分的时频特征则可有效捕捉不同标签响应的时频信息并将该部分变量信息进行权重优化与传递,进而获取高准确率的标签分类。
由于深度学习网络的学习与训练具有“黑箱子”特性,为了有效认知神经网络在特征的识别及权重自动分配的过程,采用类激活图将分类的输出的权重映射到原输入特征上,进而实现深度学习网络的特征可视化。选取模型具有良好训练效果的梅尔频谱特征进行可视化分析,得到类激活图如图11所示。可以看出,采用类激活图可视化特征对比原图可有效得到神经网络对梅尔频谱分类学习的特征及权重分布,权重分布及特征识别可明显作为脱空损伤、健康及无效数据的分类依据。从图11中也可以看出,具有脱空损伤的结构在脱空位置处冲击产生的声波在低频及高频均表现出明显峰值特征,而健康结构仅在高频附近具有峰值特征,这一现象也与四周约束声振理论相符,说明在神经网络模型训练过程中获取到可实现准确分类的准确特征,进一步说明了基于该架构的深度学习网络模型是相对准确且有效的。
依托组合桥塔足尺模型结合常规传统人工敲击法、冲击回波法、压电陶瓷法开展无损检测方法对比试验研究,分别从组合结构脱空缺陷的识别效果、检测效率、检测精度及检测成本进行对比分析,得到表3结果。可以看出,人工敲击法效率最高,但其缺乏理论依据常依赖于个人经验判断,检测精度较低,仅适用于大面积缺陷检测;冲击回波法与压电陶瓷法,相对检测精度有较大提升,但仍需人工手持仪器逐个检测,且压电陶瓷法需要张贴压电陶瓷片具有相对较高的检测成本;而搭载机器人的声信号智能检测方法可实现高效率、低成本及高精度的无人化检测。
脱空智能检测方案利用先进的自动化传感技术和数据分析算法,在张靖皋长江大桥及狮子洋长江大桥开展应用,该方案将检测精度提高至96%,降低了检测成本与人工作业强度,解决了高耸恶劣环境人员危险性高、人员检测效率低等难题。未来,随着该技术在桥梁结构上的应用,通过持续积累并丰富各类结构类型的声信号数据库,可实现组合结构多类型、多场景更加精准高效的检测。这也将有助于提升桥梁检测行业的智能化水平,拓展智能检测技术的应用领域,推动产业升级。
4 结论
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通过钢混组合结构桥塔足尺模型试验研究并结合深度学习模型与理论分析,得到以下结论。
(1)通过对流-固耦合的钢-混组合结构有限元模型模拟分析,外部激励下声信号特征可作为组合结构脱空检测识别的有效方法。
(2)基于桥塔足尺模型检测数据,梅尔频谱特征可提高对结构脱空损伤识别的准确性;构建的基于声信号时频特征的深度学习网络,可对组合结构界面脱空进行智能识别,识别精度达96.8%。
(3)研发的自动化脱空检测机器人装置及检测方法可高效解决高耸组合结构界面脱空的快速自动化检测与识别问题,自动化检测效率达2 min/m2。
基金
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  • 国家重点研发计划(2022YFC3802003)
  • 中交集团院士专项科研项目(YSZX-02-2022-01-B)
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2025年第25卷第16期
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doi: 10.12404/j.issn.1671-1815.2404734
  • 接收时间：2024-06-25
  • 首发时间：2025-07-09
  • 出版时间：2025-06-08
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  • 收稿日期：2024-06-25
  • 修回日期：2025-03-07
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国家重点研发计划(2022YFC3802003)
中交集团院士专项科研项目(YSZX-02-2022-01-B)
作者信息
    1 中交第二航务工程局有限公司, 武汉 430040
    2 同济大学土木工程学院, 上海 200125
    3 广东湾区交通建设投资有限公司, 广州 510000
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2种不同金属材料的力学参数
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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
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