Article(id=1319222806655652476, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222668956643677, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220573, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1657641600000, receivedDateStr=2022-07-13, revisedDate=1664294400000, revisedDateStr=2022-09-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1792456827081, onlineDateStr=2026-10-20, pubDate=1684512000000, pubDateStr=2023-05-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792456827081, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792456827081, creator=13701087609, updateTime=1792456827081, updator=13701087609, issue=Issue{id=1319222668956643677, tenantId=1146029695717560320, journalId=1263187385517883426, year='2023', volume='51', issue='5', pageStart='1091', pageEnd='1372', issueExtLink='null', onlineDate='null', pubDate='1684512000000', pubDateStr='2023-05-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1792456794251, creator='13701087609', updateTime=1792456976356, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1319223432827490920, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222668956643677, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1319223432827490921, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1319222668956643677, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1323, endPage=1331, ext={EN=ArticleExt(id=1319222806844396157, articleId=1319222806655652476, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Crack Inspection and Feature Quantitative Identification of Hybrid-Fibers Engineered Cementitious Composites Based on Deep Learning, columnId=1263922769897599102, journalTitle=Journal of the Chinese Ceramic Society, columnName=Research Articles, runingTitle=null, highlight=null, articleAbstract=

It is important for the accurate identification of cracks of engineered cementitious composites (ECC) to investigate the mechanical properties and durability of ECC. To solve the problems like the large number and density of ECC cracks and heavy noise interference, this study adopted the U-NET model suitable for biological image recognition, and optimized part of ResNet network layer structure based on the deep learning method. This study also used the neural network model and combined with the created data suitable for the ECC, and performed the semantic segmentation to obtain the crack pixels. For crack parameter extraction, this study used the bone extraction method and combined with digital image processing process, and used the CLAHE filter and half-peak full-width concept to obtain the crack width. The crack identification and parameter extraction method was applied to detect the cracks on hybrid-fiber ECC dog bone specimen and ECC link slab. The results show that the error range between the ECC crack identification and intelligent detection established by the deep learning method and the actual manual measurement is within 0.6 mm. The results of this study can provide an accurate, effective and high-throughput analysis for the ECC crack inspection and feature quantitative identification.

, authors=Xiaodan TENG^{1, 2, 3}, Jianming GUO⁴, Huihuang SUN¹, authorsList=Xiaodan TENG, Jianming GUO, Huihuang SUN, authorCompany=null, correspAuthors=Jianming GUO, authorNote=, correspAuthorsNote=, 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=1319222807075082878, articleId=1319222806655652476, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=基于深度学习的高延性水泥基复合材料单轴拉伸裂缝检查与特征定量化识别, columnId=1263922770220560512, journalTitle=硅酸盐学报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

对高延性水泥基材料（ECC）的裂缝特征进行准确识别和测量是研究ECC力学性能与耐久性的重要手段。针对ECC裂缝数量多且细密，纤维噪声干扰重等问题，基于深度学习方法，采用适合于小样本数量生物图像识别的U-Net模型，加上部分ResNet网络层结构进行优化，结合新增制作的适用于ECC的数据集，训练神经网络模型，进行语义分割获取裂缝像素。针对裂纹参数提取问题，使用骨骼提取方法，结合数字图像处理流程，运用CLAHE滤镜和半峰全宽概念获取裂缝宽度，实现了混杂纤维ECC狗骨试件和ECC连接板的裂缝识别与参数提取。结果表明：采用深度学习方法建立的ECC裂缝识别与智能检测方法与实际手工测量误差范围在0.6 mm以内。研究成果可为ECC裂缝检查与特征定量化识别提供准确有效和高通量的分析方法。

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