Article(id=1276896679697715544, tenantId=1146029695717560320, journalId=1276577754012160025, issueId=1276896661737701828, articleNumber=null, orderNo=null, doi=10.3724/j.gyjzG26022502, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1771948800000, receivedDateStr=2026-02-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1782365492033, onlineDateStr=2026-06-25, pubDate=1779206400000, pubDateStr=2026-05-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1782365492033, onlineIssueDateStr=2026-06-25, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1782365492033, creator=13701087609, updateTime=1782365492033, updator=13701087609, issue=Issue{id=1276896661737701828, tenantId=1146029695717560320, journalId=1276577754012160025, year='2026', volume='56', issue='5', pageStart='1', pageEnd='264', issueExtLink='null', onlineDate='null', pubDate='1779206400000', pubDateStr='2026-05-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1782365487751, creator='13701087609', updateTime=1782367237543, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1276904000968589318, tenantId=1146029695717560320, journalId=1276577754012160025, issueId=1276896661737701828, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1276904000968589319, tenantId=1146029695717560320, journalId=1276577754012160025, issueId=1276896661737701828, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=14, endPage=28, ext={EN=ArticleExt(id=1276896679970345306, articleId=1276896679697715544, tenantId=1146029695717560320, journalId=1276577754012160025, language=EN, title=Research Progress on Diagnosis and Treatment for Building External Thermal Insulation Composite System, columnId=null, journalTitle=Industrial Construction, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The external thermal insulation composite system (ETICS) is crucial for improving building energy efficiency and ensuring building functionality. In recent years, issues such as cracking, hollowing, peeling, and high-altitude falling have occurred frequently, posing a significant threat to public safety. A systematic review was conducted on domestic and international research and engineering practice regarding diagnosis and treatment methods for detection, evaluation, and repair of building ETICS. In terms of detection, non-destructive testing techniques were categorized into four types based on their energy forms and physical mechanisms, namely optical, thermal, electromagnetic, and acoustic. The research progress of various non-destructive testing techniques and commonly used destructive testing techniques was systematically reviewed. A comparative analysis was conducted on the technical points, advantages and disadvantages, and applicable scenarios of various detection techniques. In terms of evaluation, the characteristics and progress of existing evaluation methods were summarized from three aspects: qualitative evaluation, quantitative evaluation, and comprehensive evaluation. In terms of repair, the current development status of existing repair methods was introduced from the perspectives of repair technology, repair materials, and repair strategies. Finally, the deficiencies in the research and engineering practice regarding diagnosis and treatment methods for building ETICS were analyzed, and future research directions were discussed.

, authors=null, authorsList=Youlu HUANG, Qingfeng XU, Zhuolin WANG, authorCompany=null, correspAuthors=null, 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=1276896683711664485, articleId=1276896679697715544, tenantId=1146029695717560320, journalId=1276577754012160025, language=CN, title=建筑外墙外保温系统诊治研究进展, columnId=1276896662844998087, journalTitle=工业建筑, columnName=工程诊治与运维理论方法, runingTitle=null, highlight=null, articleAbstract=

建筑外墙外保温系统是提升建筑节能和保障建筑功能的关键,近年来常出现开裂、空鼓、剥落和高空坠落等问题,严重影响公众安全。系统梳理了国内外建筑外墙外保温系统诊治在检测、评估与修复方面的研究与工程实践进展:在检测方面,基于能量形式和物理作用机制,将无损检测技术分为光学、热学、电磁学和声学分析四类,系统梳理了各类无损检测技术以及常用破损检测方法的研究进展,对比分析了各类检测技术的技术要点、优缺点和适用场景;在评估方面,从定性评估、定量评估和综合评估三方面总结了现有评估方法的特点与进展;在修复方面,从修复技术、修复材料和修复方案等方面介绍了现有修复方法的发展现状。最后,对建筑外墙外保温系统诊治研究和工程实践存在的不足进行了分析,并对后续的研究方向进行了展望。

, authors=

黄有露,博士,主要从事既有建筑检测评估和非结构构件抗震研究,

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许清风,博士,教授级高级工程师,主要从事木竹结构、工程结构抗火和既有建筑维护研究,
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黄有露,博士,主要从事既有建筑检测评估和非结构构件抗震研究,

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Engineering Applications of Artificial Intelligence2023121: 105986., articleTitle=A novel fuzzy group decision-making approach based on CCSD method for thermal insulation board selection problem: a case study, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1276896684403724647, tenantId=1146029695717560320, journalId=1276577754012160025, articleId=1276896679697715544, xref=1, ext=[AuthorCompanyExt(id=1276896684407918952, tenantId=1146029695717560320, journalId=1276577754012160025, articleId=1276896679697715544, companyId=1276896684403724647, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Shanghai Key Laboratory of Engineering Structure Safety, Shanghai Research Institute of Building Sci. 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Comparative analysis of detection methods for building ETICS

, figureFileSmall=null, figureFileBig=null, tableContent=
类型名称优点缺点适用范围
光学可见光成像法非接触;支持大范围自动检测结果受环境因素影响大;仅能检测表面缺陷裂缝、剥落、霉变等表观缺陷检测
点云技术可获得毫米级精度的三维模型设备昂贵;对保温层内部缺陷不敏感外墙整体或局部变形测量
热学红外热像法非接触;支持大范围自动检测;能可视化隐蔽的空鼓及渗水区域结果受环境因素影响大;难以确定缺陷深度剥落、内部空鼓、渗水、保温层厚度不均等缺陷检测
电磁学探地雷达法可探测内部结构;能估算缺陷深度与范围设备昂贵;结果易受墙体内部金属构件影响内部空鼓、锚栓位置等缺陷检测
康普顿背散射法对保温层等低密度材料敏感设备昂贵;涉及放射性源,安全要求极高保温层密度均匀性、空鼓等检测
声学叩诊法成本低;支持大范围自动检测难以检测深层或小面积缺陷浅层空鼓检测
超声波法对内部小缺陷敏感超声波在非均质材料中衰减快内部细小裂缝、空洞等检测
冲击回波法结果比纯声学方法更客观需要传感器与表面良好耦合;表面粗糙度影响大内部空鼓检测
破损钻芯取样法结果直观可靠;芯样可用于测定抗压强度等性能指标事后需修复;效率低;抽样难以反映整体状况系统构造验证;材料性能检测
拉拔法能量化黏结强度;通过破坏模式可确定失效位置事后需修复;效率低;抽样难以反映整体状况黏结强度评估;失效机理诊断
局部剥离法结果直观可靠事后需修复;效率低;抽样难以反映整体状况有效黏结面积等施工工艺详查
), ArticleFig(id=1276896692528091533, tenantId=1146029695717560320, journalId=1276577754012160025, articleId=1276896679697715544, language=CN, label=表1, caption=

建筑外墙外保温系统检测方法对比分析

, figureFileSmall=null, figureFileBig=null, tableContent=
类型名称优点缺点适用范围
光学可见光成像法非接触;支持大范围自动检测结果受环境因素影响大;仅能检测表面缺陷裂缝、剥落、霉变等表观缺陷检测
点云技术可获得毫米级精度的三维模型设备昂贵;对保温层内部缺陷不敏感外墙整体或局部变形测量
热学红外热像法非接触;支持大范围自动检测;能可视化隐蔽的空鼓及渗水区域结果受环境因素影响大;难以确定缺陷深度剥落、内部空鼓、渗水、保温层厚度不均等缺陷检测
电磁学探地雷达法可探测内部结构;能估算缺陷深度与范围设备昂贵;结果易受墙体内部金属构件影响内部空鼓、锚栓位置等缺陷检测
康普顿背散射法对保温层等低密度材料敏感设备昂贵;涉及放射性源,安全要求极高保温层密度均匀性、空鼓等检测
声学叩诊法成本低;支持大范围自动检测难以检测深层或小面积缺陷浅层空鼓检测
超声波法对内部小缺陷敏感超声波在非均质材料中衰减快内部细小裂缝、空洞等检测
冲击回波法结果比纯声学方法更客观需要传感器与表面良好耦合;表面粗糙度影响大内部空鼓检测
破损钻芯取样法结果直观可靠;芯样可用于测定抗压强度等性能指标事后需修复;效率低;抽样难以反映整体状况系统构造验证;材料性能检测
拉拔法能量化黏结强度;通过破坏模式可确定失效位置事后需修复;效率低;抽样难以反映整体状况黏结强度评估;失效机理诊断
局部剥离法结果直观可靠事后需修复;效率低;抽样难以反映整体状况有效黏结面积等施工工艺详查
), ArticleFig(id=1276896692595200398, tenantId=1146029695717560320, journalId=1276577754012160025, articleId=1276896679697715544, language=EN, label=Table 2, caption=

Classification of performance degradation levels for ETICS

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Sw取值范围/%等级描述
Sw≤1A未改变
1<Sw≤10B良好
10<Sw≤30C轻微退化
30<Sw≤50D中等退化
Sw>50E严重退化
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外保温系统性能退化等级划分

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Sw取值范围/%等级描述
Sw≤1A未改变
1<Sw≤10B良好
10<Sw≤30C轻微退化
30<Sw≤50D中等退化
Sw>50E严重退化
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建筑外墙外保温系统诊治研究进展
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黄有露 , 许清风 , 王卓琳
工业建筑 | 工程诊治与运维理论方法 2026,56(5): 14-28
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工业建筑 |工程诊治与运维理论方法 2026 , 56 (5) : 14 -28
建筑外墙外保温系统诊治研究进展
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黄有露 , 许清风 , 王卓琳
作者信息
  • 1上海市建筑科学研究院有限公司,上海市工程结构安全重点实验室,上海200032
通讯作者:
许清风,博士,教授级高级工程师,主要从事木竹结构、工程结构抗火和既有建筑维护研究,
Research Progress on Diagnosis and Treatment for Building External Thermal Insulation Composite System
Youlu HUANG , Qingfeng XU , Zhuolin WANG
Affiliations
  • 1Shanghai Key Laboratory of Engineering Structure Safety, Shanghai Research Institute of Building Sci. Co., Ltd., Shanghai200032, China
出版时间: 2026-05-20 doi: 10.3724/j.gyjzG26022502
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建筑外墙外保温系统是提升建筑节能和保障建筑功能的关键,近年来常出现开裂、空鼓、剥落和高空坠落等问题,严重影响公众安全。系统梳理了国内外建筑外墙外保温系统诊治在检测、评估与修复方面的研究与工程实践进展:在检测方面,基于能量形式和物理作用机制,将无损检测技术分为光学、热学、电磁学和声学分析四类,系统梳理了各类无损检测技术以及常用破损检测方法的研究进展,对比分析了各类检测技术的技术要点、优缺点和适用场景;在评估方面,从定性评估、定量评估和综合评估三方面总结了现有评估方法的特点与进展;在修复方面,从修复技术、修复材料和修复方案等方面介绍了现有修复方法的发展现状。最后,对建筑外墙外保温系统诊治研究和工程实践存在的不足进行了分析,并对后续的研究方向进行了展望。

外墙外保温系统  /  检测  /  评估  /  修复  /  研究进展

The external thermal insulation composite system (ETICS) is crucial for improving building energy efficiency and ensuring building functionality. In recent years, issues such as cracking, hollowing, peeling, and high-altitude falling have occurred frequently, posing a significant threat to public safety. A systematic review was conducted on domestic and international research and engineering practice regarding diagnosis and treatment methods for detection, evaluation, and repair of building ETICS. In terms of detection, non-destructive testing techniques were categorized into four types based on their energy forms and physical mechanisms, namely optical, thermal, electromagnetic, and acoustic. The research progress of various non-destructive testing techniques and commonly used destructive testing techniques was systematically reviewed. A comparative analysis was conducted on the technical points, advantages and disadvantages, and applicable scenarios of various detection techniques. In terms of evaluation, the characteristics and progress of existing evaluation methods were summarized from three aspects: qualitative evaluation, quantitative evaluation, and comprehensive evaluation. In terms of repair, the current development status of existing repair methods was introduced from the perspectives of repair technology, repair materials, and repair strategies. Finally, the deficiencies in the research and engineering practice regarding diagnosis and treatment methods for building ETICS were analyzed, and future research directions were discussed.

external thermal insulation composite system  /  detection  /  evaluation  /  repair  /  research progress
黄有露, 许清风, 王卓琳. 建筑外墙外保温系统诊治研究进展. 工业建筑, 2026 , 56 (5) : 14 -28 . DOI: 10.3724/j.gyjzG26022502
Youlu HUANG, Qingfeng XU, Zhuolin WANG. Research Progress on Diagnosis and Treatment for Building External Thermal Insulation Composite System[J]. Industrial Construction, 2026 , 56 (5) : 14 -28 . DOI: 10.3724/j.gyjzG26022502
随着我国城镇化进程的不断加快,房地产市场已由传统的增量时代迈入存量时代,保证既有建筑安全运营已逐渐成为行业发展趋势。作为提升建筑节能和保障建筑功能的关键,建筑外墙外保温系统广泛应用于各类建筑1。外墙外保温系统是由保温层、防护层和固定材料构成,并固定在外墙外表面的非承重保温构造的总称,简称外保温系统2。由于建造质量瑕疵或建筑材料老化,外保温系统的开裂、空鼓、剥落和高空坠落等问题日益严重3。这些缺陷不仅破坏了结构的耐久性和适用性,建筑外保温系统的高空坠落已严重危害城市的公共安全4
对外保温系统进行定期检测对于及时发现潜在缺陷和有效的健康管理至关重要5-6。由于传统破损检测方法会对建筑产生不利影响,无损检测技术因其非破坏性日益受到重视7。随着各种新型无损检测技术与人工智能技术的深度融合,检测的效率和精度得到了显著提高8-9。此外,外保温系统缺陷诊治在检测、评估和修复方面的标准相继出台和更新,为其风险治理提供了制度化保障10。为了适应多元化检测需求,相关检测技术、评估方法和修复技术不断发展,为城市更新提供了体系化的技术支持。
基于上述背景,本文系统梳理了建筑外墙外保温系统诊治在检测、评估与修复三方面的研究与工程实践进展,对各类方法的适用条件和存在不足进行分析和归纳,指出后续的主要研究方向,为减少建筑外墙外保温高空坠落风险和事故提供支撑。
根据检测设备与外保温系统间的接触关系及对系统完整性的影响程度,现有检测方法可分为无损检测与破损检测两大类。前者通过非破坏式手段实现宏观普查与快速定位,并日益发展出定量化的识别能力;后者通过局部破坏式手段,直接获取外保温系统的关键构造与力学性能数据。两类方法在检测尺度与信息维度上形成互补,共同构成了外保温系统定性判断与定量诊断相结合的技术体系。
根据检测方法的不同能量形式和物理作用机制,本文将外保温系统无损检测方法分为光学、热学、电磁学和声学检测方法。
基于光学原理的检测技术主要有可见光成像法和点云技术两种类型。
可见光成像法指的是基于二维可见光图像,直接识别外墙表观缺陷的检测方法11。该方法的核心在于替代传统人工目视观察,将检测人员从高空、高危作业中解放出来,并克服人眼观察主观性强、检测效率低、难以量化等局限。不少学者采用该方法对外墙的裂缝和剥落进行检测。夏子祺等12提出了结合深度学习和图像处理的外墙裂缝和剥落定位与损伤量化评估两阶段检测方法,裂缝识别过程如图1所示。Xiang等13为解决外墙裂缝分割易受不规则裂缝形状和图像背景干扰等问题,提出了融合变换器和卷积神经网络的双编码识别网络。Katsigiannis等14提出了基于传递学习算法的墙体裂缝识别方法,从而克服传统深度学习算法需要大量数据的难题。Chen等15为提高裂缝识别精度,提出了先判定是否有裂缝再对有裂缝图像进行分割的两阶段识别方法。
建筑外保温系统除了开裂和剥落外,还可能面临返碱、起皮和粉化等缺陷。为此,Zhou等16提出了基于人工智能(AI)的建筑外墙缺陷自动检测系统。Guo等17提出了基于层的分类规则并应用元学习方法为训练数据重新分配权重,以解决不同缺陷类之间数据大小不平衡的问题;提出了基于卷积神经网络的半监督学习缺陷分类方法,从而克服在小样本数据下的分类难题18;Guo等19进一步提出了基于行业定义标准的深度学习方法,实现对缺陷位置、类型和数量等状态的识别和评估。Cui等20提出可扩展分类器和对比学习方法,发现在较少的标记数据下仍可获得很高的缺陷分类准确度。Karimi等21采用基于YOLO模型的检测方法对葡萄牙历史建筑外墙瓷砖裂缝和脱落进行检测。Yu等22采用扩散模型对哈尔滨市多幅建筑图像的数据集进行训练,提出了改进建筑外墙的缺陷修复策略。为了提高实际项目维修策略制定的针对性,Cheng等23提出了基于文本描述匹配和定位的特定缺陷检测方法。综上所述,可见光成像法通常需结合不同类型的深度学习算法对缺陷进行识别,且主要用于建筑外立面裂缝和脱落等表层缺陷的识别。
点云技术是利用建筑表面海量三维坐标点构成的高密度点云,实现对建筑实景的精准三维数字化重建,在结构健康监测中得到了广泛应用24。Stałowska等25利用地面激光扫描仪获取的建筑点云对外墙裂缝进行识别,并分析了扫描仪光斑尺寸对检测结果的影响规律。Bolourian等26利用激光扫描仪建立了混凝土表面的点云,实现了不同类型缺陷的识别。Xu等27根据红外图像获取的点云建立了针对建筑表面缺陷的集成信息管理框架,能够对缺陷进行识别和分类。此外,通过将激光扫描仪获取的点云与可见光图像融合分析,可实现建筑表面裂缝、剥落和变形检测28。Chen等29利用点云数据实现了裂缝宽度的量化评估。唐寅30利用机器狗搭载三维激光扫描仪对文物建筑进行检测,结果表明该方法具有较高的检测效率。Choi等31提出了三维激光点云与建筑信息模型(BIM)技术相结合的方法,结果表明该方法可提高建筑缺陷检测的精度。扫描仪还可基于无人机搭载实现对建筑外保温系统进行快速检测。
红外热像法是近年来迅速发展且广泛应用于实际工程的无损检测技术,通过捕捉外保温系统外表面温度差异能够对保温层空鼓和脱落等缺陷进行识别32。Lourenço等33采用红外热像法对瓷砖脱粘缺陷进行检测。冯力强等34提出了红外热像法检测饰面砖饰面层内部缺陷大小的估算方法,并建立了缺陷面积的估算公式。Resende等35将该方法应用于历史建筑的外墙质量检测。臧科宇等36提出一种基于无人机搭载红外热像仪的外保温系统智能巡检技术。Lu等37提出了改进的Canny边缘检测算法。为了进一步提高缺陷的检测效率和精度,不少学者采用深度学习算法建立训练模型以实现缺陷的自动识别和量化评估38-39。虽然上述方法可显著提高缺陷识别效率,但需保证训练数据的数量和质量以提高模型的泛化能力。
相比于其他类型的无损检测数据,红外图像的质量会明显受到环境的影响。Huang等40-41发现阴影和眩光会导致红外图像出现不均匀的温度分布特征,而图像分割技术是解决该问题的有效途径;采用主成分分析法对红外图像进行降维处理并结合图像分割技术对建筑外立面缺陷进行了识别42。Chen等43基于现场测试和数值模拟深入分析了太阳辐射、风速和环境温度对外墙缺陷检测精度的影响。也有学者发现外墙装饰材料特性如陶瓷面板孔隙率会影响红外热像缺陷检测结果44。为了适应建筑外保温系统缺陷的检测需求,无人机常作为红外热像仪的搭载工具,但缺陷识别精度会受到无人机与外墙距离、拍摄角度以及日光照射等因素的影响45-46。将红外热像图与可见光图像相融合,可以提高缺陷定位识别的精度,识别流程如图2所示47
基于电磁原理的检测技术主要包括探地雷达法和康普顿背散射法。
探地雷达法通过发射高频电磁波脉冲并接收介质的反射信号,依据波在介电常数突变界面的反射特征来探测内部结构,能够用于墙体内部空洞、钢筋位置和材料厚度的检测48。目前,该方法在历史建筑综合检测中得到了广泛应用49。为了对建筑质量进行全面检查并提高缺陷的检测精度,不少学者将探地雷达法与地面激光扫描法和红外热像法50、超声波法51-52、地震层析成像法53等相结合。Johnston等54探究了雷达天线频率对识别墙体内部空洞的影响,结果表明不同深度的缺陷所适用的天线频率存在较大差异,在实际使用过程中应根据检测需求选择最合适的天线频率。
由于该方法对墙体深层缺陷具有良好的检测能力,不少学者尝试将该技术应用于外保温系统内部缺陷的检测。Zhang等55采用探地雷达对无机保温砂浆外保温系统进行二维扫描成像,结果表明该技术可识别保温层与饰面层以及保温层与基层间的缺陷深度和高度。Li等56采用探地雷达对保温层的黏结面积进行识别,识别过程如图3所示。通过对三维扫描切片进行语义分割,缺陷面积估算的相对误差为0.7%。Cheng等57为了提高传统探地雷达的检测精度,研发了一款新型的图像雷达,试验结果表明其可用于保温层黏结面积的精确测量。上述研究结果表明,探地雷达法可用于建筑外保温系统缺陷面积的识别,但在实际检测中需注意墙体内部钢筋等金属物体对检测结果的干扰。
康普顿背散射技术利用高能射线与材料相互作用产生的背向散射信号对物体内部结构进行检测,通常采用X射线作为辐射源。Kolkoori等58采用背散射技术对航天飞机外部保温材料的缺陷进行检测。Lu等59发现康普顿背散射技术对墙体内部材料缺陷的面积和位置识别误差可分别控制在4%和2%以内。Qin等60利用蒙特卡洛法设计了基于康普顿背散射技术的墙体缺陷检测装置,试验结果显示该装置能够检测到墙体内不同位置和不同尺寸的缺陷。由于该方法需要对被检物体发射电离辐射,因此在检测过程中需要做好严格的防护措施。
声学检测方法主要包括叩诊法(敲击法)、超声波法和冲击回波法。
叩诊法指通过敲击让结构发生受迫振动并采集声信号,然后根据声信号的变化特征判断其是否存在异常,常用于判断外保温系统的空鼓缺陷。Luk等61-63、Jiang等64、Tong等65对基于声信号的外立面瓷砖空鼓识别进行了系统研究,开发了多锤头敲击方法和基于爬墙机器人的检测技术,并提出了基于支持向量机的缺陷自动识别方法。Lin等66-67开发了融合声信号识别系统的仿生爬墙机器人和智能检测棒。周尹辉等68提出基于贝叶斯算法优化-支持向量机的多特征融合空鼓检测方法。Ito等69采用深度学习算法对声信号进行处理,试验结果显示该方法可准确识别出瓷砖与墙体间的黏结率。
以上研究对空鼓缺陷进行识别时均侧重于声音特征的提取方法和识别算法,虽然该方法可有效提升缺陷识别的效率和精度,但在数据样本有限的情况下可能会导致模型的泛化能力和鲁棒性较低。为了解决该问题,Ji等70提出了基于有限元-边界元耦合技术的瓷砖空鼓声音模拟方法,分析结果显示:瓷砖空鼓声的主峰频率与空鼓面积呈正相关关系;并基于三维弹性理论对上述模型进行修正,从而建立了更加准确的建筑外立面瓷砖空鼓声振弱耦合分析模型71
超声波技术通过发射高频声波穿透材料并分析反射信号,依据声波在缺陷处的衰减及传播时间变化实现缺陷的定位和识别。目前,该方法已在混凝土缺陷检测中得到广泛应用72-73,识别过程如图4所示。Chen等74将时间反转法与lamb波相结合对瓷砖脱粘程度进行量化评估,并利用缺陷概率检测重构算法对损伤概率分布进行成像。虽然该方法能有效检测缺陷的位置和大小,但检测精度对传感器的布置位置较为敏感。为了克服上述缺陷,进一步提出基于数据驱动的深度学习方法,通过创建二维损伤指数图实现缺陷大小和位置的精准识别75,后续还有待在实际工程中验证该方法的有效性和实用性。超声波技术在历史建筑墙面空鼓检测中应用也较为广泛51-52。Qian等76利用超声波法对石质文物中的空洞损伤进行检测,并利用Grasshopper软件实现了缺陷的位置、大小和形状的可视化。
冲击回波法是通过机械冲击在结构表面产生应力波,并利用传感器接收反射信号的检测技术。采集的振动信号包括加速度、位移和速度。根据振动信号的特征,可识别出结构的裂缝、空鼓等缺陷77以及结构的损伤状态78。Wang等79通过试验研究了墙体空鼓面积、激振位置、拾振位置、黏结强度和激振力对振动加速度幅值增长率的影响规律。Hou等80对采集的外保温系统加速度信号进行频域分析,发现缺陷检测精度不会受信号采集点与缺陷区域相对距离的影响。为了提高瓷砖脱粘尺寸的量化识别能力,Shen等81-82采用卷积神经网络对瓷砖位移数据进行分类识别。为避免在被测物体表面安装加速度和位移传感器可能引起的信号扰动,Zhao等83提出采用激光多普勒测振仪远距离测量振动速度信号。目前振动信号特征值的选取有频率、幅值以及幅值增长率等,每种特征值与缺陷大小、缺陷深度、缺陷形状的相关性有待继续研究。
对于建筑外保温系统,构造检查和黏结强度测试是进行质量检测的关键内容,所采用的检测方法主要有钻芯取样法、拉拔法和局部剥离法等破损检测方法。
外保温系统的构造做法常采用钻芯取样法进行检测,从而验证墙体保温材料的种类、构造做法以及保温层厚度等是否符合设计和施工要求84。该方法是检测和评估建筑外保温系统质量的重要手段,可用于判断缺陷产生的原因。
保温层的黏结性能或锚栓的锚固力不足是引发外保温系统开裂、空鼓甚至脱落的重要成因。拉拔法是检测保温层与基层间拉伸黏结强度和锚栓锚固力的重要方法85。当对锚栓锚固力进行拉拔法测试时,行业标准JG/T 366—2012《外墙保温用锚栓》86规定了敲击式和旋入式两种锚栓类型的试验方法。保温锚固射钉具有施工速度快和工序简单等优势,上海市工程建设规范DG/TJ 08-2126—2023《外墙外保温系统应用技术标准》87增加了保温锚固射钉的性能指标和试验方法。
局部剥离法是检测建筑外保温系统黏结面积比的重要方式88。相比于钻芯取样法和拉拔法,该方法对墙体造成的破坏更大。由于锚栓埋入在墙体内部,对锚栓进行拉拔试验或检查其数量时需先找到锚栓的位置。虽然钢筋检测仪或红外热像仪等无损检测方法常用于探测锚栓的位置,但检测结果易受墙体内部钢筋、边界条件和锚栓材质等因素影响。因此,检测人员常采用局部剥离法寻找锚栓分布位置,未来可进一步开发便捷式锚栓智能探测仪以提高锚栓位置的检测效率和精度,减少检测对外保温系统的损坏。
无损检测方法因其技术优势在实际工程中的应用越来越多,尤其是在历史保护建筑的检测维护中得到广泛应用89。对于外墙黏结性能的检测,破损检测方法能够更加直接准确地获取力学性能参数。对每种检测方法的特点、适用范围和优缺点进行总结,如表1所示。可以看出,不同检测方法均具有自身特点和优缺点,共同构成了建筑外保温系统大面积普查与局部精细检测、定性判断与定量评估相结合的检测技术体系。
随着检测技术的不断发展以及检测需求的多样化趋势,外保温系统的检测技术不断朝以下三个方向发展:
1)多源数据融合。通过将不同检测方法获取的多源数据进行融合,可提高在复杂环境或数据质量不佳的情况下检测结果的准确性和效率。如将红外图像与可见光图像融合4790、红外热像法与叩诊法结果相互验证41、可见光图像与BIM融合91。未来应进一步开发更加高效的多源数据配准、融合与解译算法,并构建集成BIM的智能诊断平台。
2)智能搭载设备研发。将检测仪器搭载在无人智能设备或直接由具身机器人操作能够实现外保温系统快速高效检测92-93。如红外热像仪或敲击设备集成于无人机平台46、三维激光扫描仪与机器狗结合30。未来可研究空-地协同的无人设备检测集群,并结合5G通信与路径自动规划技术,实现区域建筑群的自动化快速普查与智能监测。
3)智能识别算法应用。当面对海量、高噪的现场检测数据时,需借助智能算法提升缺陷的识别效率和精度。当前,传统机器学习6494和深度学习1216等算法已在建筑外保温系统裂缝、空鼓的自动识别与量化评估中展现出优势。为进一步提升算法的工程实用性与鲁棒性,未来可开发轻量化模型以适应移动设备的边缘计算、利用迁移学习提升模型的泛化能力、探索小样本学习以解决不平衡数据难题。
在建筑外墙外保温系统检测基础上,应采用合适的方法对其性能等级进行准确评估以指导后续维护策略的制定。根据评估过程中对数据信息的处理深度、量化程度以及决策的复杂性,现行评估方法可分为定性评估、定量评估和综合评估三类。
定性评估方法是对外保温系统检测结果的初步评估,其核心在于识别缺陷现象并做出性质判断。该类方法主要依赖目视观察与经验判断或结合红外图像中的异常表征,对空鼓、剥离、渗漏等病害进行定位和分类。姜常玖等95结合目视观察、红外热像法和叩诊法对外墙异常区域进行排查,并将外墙损伤程度等级划分为3个等级,但未对损伤程度进行定量化描述。
Silva等96-97提出了适用于混凝土面层质量的检测与评估系统。该系统包含各类型墙面异常(开裂、脱落等)分类方法、导致各类型异常原因、适用检测方法和对应修复方法,建立了各项目之间的相关性矩阵。通过对葡萄牙53栋建筑进行现场检测,验证了该系统的有效性,但在实际使用过程中仍依赖于检查人员的主观判定。为了避免主观判定带来的不利影响,进一步进行了建筑检测评估软件需求的问卷调查,从而判断其在实际检测项目中的有效性和实用性98
相比于定性评估方法,定量评估方法旨在通过精准量测获取表征系统性能的关键参数,为外保温系统的安全性和使用性判定提供客观和可复验的数据基础。该方法主要依据现场原位量测或力学试验获得各类型异常的量化指标,从而对异常的严重程度进行量化分级99
中国工程建设标准化协会标准T/CECS 1675—2024《既有外墙外保温工程检测与评价标准》10明确了外保温系统各单个评估项的评价依据和等级划分标准;T/CECS 1029—2022《建筑外墙外保温系统质量诊断与评估技术规程》100在各单项评估等级基础上进一步对外保温系统评估等级进行划分,但各单项之间的评估相互独立且缺乏量化的综合评估模型。T/CECS 1675—202410和T/CECS 1029—2022100在判定外保温系统的空鼓等级时,前者采用空鼓总面积比指标划分为3个等级,而后者采用连续空鼓面积比指标划分为4个等级。未来应结合不同类型外保温系统特点提出统一的评估方法。
综合评估方法旨在突破单一缺陷或性能指标的局限,通过集成多源、异构的定性与定量数据,实现对外保温系统整体性能与退化风险的系统性诊断与决策排序101。该方法通常依托于层次分析法或模糊综合评价模型,将离散的缺陷类型、严重程度、实测性能指标以及环境与使用条件等多维因素进行加权与耦合分析。该方法不仅能评估系统当前状态,更能量化风险等级、优化维护优先级,从而为制定科学、经济且高效的维护加固策略提供决策支持。
Ximenes等102根据外保温系统出现的开裂、空鼓和脱落等缺陷,提出了可反映其性能退化程度的损伤指标Sw,计算公式如式(1)所示。该方法最初应用于水泥抹灰的性能评估103,目前已在瓷砖外墙104、石材外墙105以及外墙涂料106的性能评价中得到广泛应用。
Sw=An×KnA×K
式中:An为缺陷n对应的面积,共考虑了外保温系统表面的完整性、颜色或纹理变化、黏结性能和构件连接处缺陷共4种缺陷类型;Kn为缺陷n对应的退化系数,取值为0~4之间的整数;A为外墙面的总面积;K为最高退化条件水平对应的退化系数(Kn的最大值)。
基于计算出的损伤指标Sw,Ximenes等102将外保温系统的退化水平分为5个等级,详细信息如表2所示。根据大量实测数据绘制的Sw与外保温系统服役年限的对应关系,可知外保温系统服役年限为17年时最大退化程度达到30%。因此建议在外保温系统服役年限达到10年后进行定期检测。Marques等107在该方法基础上提出了基于因子分析法的外保温系统服役寿命预测方法。Silva等108提出了基于人工神经网络的外墙使用寿命预测模型预测方法,并建立了外保温系统使用寿命预测数据库109
不少学者根据所分析对象的退化特征对式(1)进行了改进。Serralheiro等110为了评估混凝土表面的退化特性共考虑了3种缺陷类型,包括表观缺陷(如返碱、粉化)、力学缺陷(如裂缝、脱落)以及建造缺陷(如对拉螺栓孔灌浆),并为每种缺陷进一步指定权重系数,计算式如式(2)所示。
Sw=AeKnKa,n+AmKnKa,n+AcKnKa,nA×Kmax
式中:AeAmAc分别为受美学、机械和构造异常影响的面层面积;Kn为退化系数,取值为0~4之间的整数;Ka,n为各异常的相对加权系数,默认取值为1;A为面层总面积;∑Kmax为所有退化系数最大取值之和。
在进行性能等级划分时,在表2基础上对Sw的取值范围进行了修正。Mousavi等111研究了上述改进方法对非锚固式石材外墙性能退化评估的有效性。Prieto等112发现该方法计算出的Sw与建筑功能指数具有高度一致性,证明了该方法用于评估外墙退化性能的有效性。Souza等113-114采用类似方法研究了气候因素对巴西大量建筑外墙退化程度的影响,并进一步提出了外墙退化机制的判定方法。
为了确定建筑外立面维修的优先级,Madureira等115提出了一种维修优先级指标P,计算方法如式(3)所示。
P=EE+2AE+3NI+4NC34
式中:EE代表环境暴露水平,考虑农村、城市和沿海地区;AE代表各种缺陷类型的程度;NI代表非干预后果;NC代表外观质量、使用功能和安全性符合程度,并对每个因素赋予了相应的权重系数。该方法不仅考虑了外保温系统本身缺陷程度的影响,同时考虑了环境条件等因素。
上海市工程建设规范DG/TJ 08-2310—2019《外墙外保温系统修复技术标准》116给出了外保温系统质量技术状况指数(MQI)的计算方法,如式(4)所示。
MQI=ω1FQI+ω2PQI+ω3IQI+ω4BQI
式中:FQIPQIIQIBQI分别为饰面材料、护面材料、保温材料和黏结锚固材料的质量技术状况指数;ω1~ω4为权重系数。
该方法在计算各单项以及外保温系统的MQI时,均赋予了相应的权重系数(ω1~ω4)。目前各单项质量技术状况指数的计算相互独立,未来可进一步考虑各因素间相关性的影响。
此外,杨霞117建立了外墙砖饰面的安全风险评估模型,该模型将距离上一次全面维修的年限、裂缝数量和立面空鼓面积比作为评价指标。梁轶循118提出了外保温系统的综合评价体系并通过层次分析法确定各指标的权重,结果发现保温层的脱落程度对评价结果影响最大,但该方法对各指标层的划分依据仍有待进一步研究。
上述介绍的三种外保温系统评估方法形成了逐层深入的技术体系,未来外保温系统评估方法研究可从以下三个方面进行深入:
1)融入建筑整体质量状况评估框架,开展建筑整体风险分析。将建筑外保温系统评估纳入建筑整体状况评估,是提升建筑运维科学性的必然要求。未来可分析外保温失效对建筑能耗、结构安全、室内环境及外围护结构水密性的影响,通过建立跨系统的耦合评估框架综合评价建筑整体质量状况119-120
2)融合BIM技术,构建全生命期管理平台。BIM模型不仅集成了建筑设计和施工阶段的数据,还能根据定期检测数据对建筑信息进行持续更新,动态反映外保温系统的性能退化过程121-122。未来可建立集状态监控、性能评估和维修决策于一体的可视化管理平台,为外保温系统的实时监测和预防性维护提供支撑。
3)深化AI算法应用,实现检测评估的自动化与智能化。未来可开发基于深度学习的风险评估模型,通过分析海量历史案例数据,建立性能退化与多因素(环境、构造、材料等)的映射关系并考虑参数不确定性的影响,实现对外保温系统剩余寿命和失效风险的智能预警123-124
在明确外保温系统的性能等级与风险特征后,需选取适配的修复方法开展修复作业。基于修复方法的适配性、针对性与系统性,从修复技术、修复材料及修复方案三个维度展开分析。
针对外保温系统不同病害类型,国内外发展并应用了多种修复技术。目前,国内多个标准均对外保温系统修复技术进行了明确规定,但在修复分类和技术方法上存在差异。T/CECS 574—2019《既有建筑外墙外保温改造技术规程》125规定了置换法、嵌缝修补法、注浆法和加固法等修复技术;上海市工程建设规范DG/TJ 08-2310—2019116提出了置换修复和原位修复两大类共7小类修复技术,其中置换修复技术指的是对外保温系统进行清除并进行置换的技术,原位修复技术指的是不铲除或极少量铲除原系统、直接在原外保温系统上进行修复的技术。Pereira等126针对外墙常见缺陷类型制定了详细的维修技术清单,包括置换法和注浆法等。刘晨127提出基于粘锚法的既有建筑模塑聚苯板(EPS)外保温“微创”维修技术,研究了粘贴面积和粘贴厚度对加固效果的影响规律。
外保温系统缺陷修复材料的性能对修复效果具有显著影响。上海市工程建设规范DG/TJ 08-2310—2019116对置换修复和原位修复所采用的界面处理剂、聚合物注浆胶、黏结砂浆和抹灰砂浆、锚栓等材料的性能指标进行了明确规定。Lyu等128研究了不同类型聚合物对瓷砖界面剂黏结强度和抗冻融性的影响规律。Azeiteiro等129开发了可用于加固墙体抹灰层的石灰基灌浆料,并通过试验验证了该修复材料的有效性。
近年来,聚合物改性水泥基材料因其抗裂性和耐久性好、施工工艺简单、经济性好等优点,在外保温系统修复中备受关注130-131。目前该类材料所选用的聚合物改性组分类型丰富,包括自乳化水性环氧树脂132、活性金尾矿133、粉煤灰/高炉矿渣微粉134、乙烯醋酸乙烯酯135、再生聚乙烯136、丁苯橡胶137、氧化石墨烯138、阴离子水性聚氨酯139以及不同类型聚合物组合140等。各类改性组分通过与水泥基基材的协同作用,显著提升了材料的柔韧性与黏结强度。再生聚乙烯等工业废料的再利用有助于降低修复成本并促进低碳发展。
锚栓作为外保温系统常用的辅助固定手段,其力学性能对外保温系统的整体性能具有较大影响。李安起等141通过拉拔试验研究了基层材料、锚入深度以及有无砂浆抹面等因素对锚栓受力性能的影响。Ji等142采用数值模拟方法分析了锚栓对建筑外墙保温系统性能的影响。Zhang等143研究了锚栓加固数量和位置对锚栓本身和黏结层内力分布的影响规律。未来可将锚栓自身的力学性能及其对外墙整体性能的影响进行综合考虑,并优化锚栓布置的位置和数量。
外保温系统的性能随着使用时间的增加会逐渐退化,根据修复介入时机的差异可分为预测性修复(Predictive Maintenance)、预防性修复(Preventive Maintenance)和改进型修复(Improvement Maintenance)144,依次对应例行维护、精准干预和系统升级的不同维修策略。Hu等145对预测性修复方案在建筑领域的应用进行了总结与分析。
决定建筑维修需求大小是制定合适维修方案的重要基础。Nowogońska146基于层次分析法提出了建筑维修需求的计算方法,考虑了构件质量状况和建筑重要性等因素。Ferreira等147建立了基于构件质量状况的维修随机性模型,该模型考虑了与退化过程以及维护行动相关的不确定性参数的影响,已在石材外墙148以及外保温系统149的维修方案制定中得到应用;同时还分析了维修方案对建筑外立面构件性能退化的影响150。在实际维修过程中,潮湿环境与离海洋距离等因素均会对维修质量产生显著影响151
在制定维修方案时,维修材料的选择对提升建筑性能和控制经济成本至关重要。Chai等106在选择维修方案时同时考虑了技术条件和经济成本。Nyers等152将建筑节能效果纳入保温材料厚度的确定方法中。Ferreira等153对修复方法进行了建筑全生命期成本分析。Zhang等154为了改善以往研究主要基于经济和环境因素考量的片面性,提出了结合BIM模型和多准则决策框架的保温材料优选方法,并在外保温系统修复项目中验证了该方法的有效性。
修复技术的选择是完成修复的重要工具,修复材料的选择是保证修复质量的重要基础,而修复方案的制定则是对修复工作的优化与完善。未来有关外保温系统修复方法的研究可从以下三个方面进行:
1)研发精准高效的新型修复技术。当前,针对空鼓区域的定点注浆技术和低扰动注浆技术显著提升了修复的精准度并减少了对居民生活的干扰。未来在保证维修质量的同时,可进一步结合智能化设备研发更加高效的修复技术,提升维修效率和精准度,并保证修复后外保温系统整体的安全性和有效性。
2)开发高性能的绿色修复材料。外墙外保温修复新材料的研发应聚焦于性能提升与绿色低碳的双重需求,重点研究固体废弃物的资源化利用155;可结合各类新型聚合物与水泥基基材的协同作用机理,研发可适配不同缺陷类型的专用修复材料,进一步提升材料的黏结性和耐久性。
3)制定综合性能优的维修方案。未来可综合考虑材料性能、损伤等级、生命期成本、环境影响等多目标,通过引入模糊综合评价、遗传算法等决策模型以及数字孪生模型145-156,实现对不同维修策略的量化比选与优化,从而制定出技术可行、经济合理的综合性能优的修复方案。
国内外针对建筑外保温系统诊治在检测、评估与修复方法的研究已取得了较多成果,研发了多种无损和破损检测方法,建立了评估体系,提出了修复方法,为保证建筑外保温系统的安全运维提供了有力支撑。但当前有关外保温系统检测、评估与修复方法还存在以下不足:
1)红外热成像、叩诊等检测方法虽能结合无人设备实现大面积自动检测,但在复杂检测环境下的应用仍受限制,且结果多为定性,难以量化缺陷程度。各类检测设备与数据之间彼此孤立,尚未形成统一、自动化的多源信息融合采集与分析流程,同时识别算法的泛化能力仍有待进一步提升。
2)现有评估方法多为基于单一指标的判定阶段,很少结合环境荷载与材料老化规律对系统性能退化和剩余寿命进行科学预测,缺乏模型驱动的动态分析能力。在实际检测过程中,各项评估指标的判定主观性强,未考虑不同参数不确定性的影响。同时,当前外保温系统评估未与建筑整体评估相融合,导致后续的修复策略缺乏前瞻性。
3)目前仍主要采用人工作业的维修模式,存在效率低、风险高、质量波动大等问题。修复技术与材料的选用多依赖经验,尚未形成基于评估结果的“量身定制”式材料设计与施工参数智能匹配。此外,修复方案的选择缺乏全生命期成本、性能与绿色低碳的多目标协同优化工具,使得维修决策往往偏向短期经济性而忽视了长期可靠性与可持续性。
基于上述不足,未来可关注以下研究方向:
1)基于智能无人设备和具身机器人的自动化检测技术研究。未来应深化智能无人巡检系统和具身机器人在外保温系统检测中的应用,实现大规模建筑外保温系统检测数据的高效、自动化、低成本采集。重点研发基于深度学习的全自动病害智能识别与精准量化算法,实现对裂缝、空鼓等缺陷的自动定位与尺寸量化。探索构建部署于建筑表层的分布式无线传感物联网,对关键性能参数进行全生命期的实时和无人化监测。
2)基于数据与模型驱动的动态风险评估体系研究。开发融合多源检测数据的自动化综合评估模型,实现从数据到风险等级的实时映射。利用机器学习算法和历史数据,构建外保温系统性能退化预测模型与数字孪生模型,实现对外保温系统长期性能演变与剩余寿命的虚拟仿真与预测。可结合BIM技术构建可视化评估与决策支持平台,为外保温系统的预防性维护提供支持。
3)基于智能装备与绿色材料的精准高效修复方法研究。着力研发适用于高空、复杂立面作业的多功能修复机器人,从而替代人工完成清理、加固等高风险施工作业。开发面向绿色修复材料的智能设计与自适应调配系统,使其能根据实时检测评估的缺陷类型与程度,动态优化材料配比,确保修复性能与环境效益。深度融合数字孪生与优化算法,获取多目标优化的修复方案。

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2026年第56卷第5期
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doi: 10.3724/j.gyjzG26022502
  • 接收时间:2026-02-25
  • 首发时间:2026-06-25
  • 出版时间:2026-05-20
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  • 收稿日期:2026-02-25
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    1上海市建筑科学研究院有限公司,上海市工程结构安全重点实验室,上海200032

通讯作者:

许清风,博士,教授级高级工程师,主要从事木竹结构、工程结构抗火和既有建筑维护研究,
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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