Article(id=1308453912084771414, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453882472984927, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.03.015, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1756224000000, receivedDateStr=2025-08-27, revisedDate=1764864000000, revisedDateStr=2025-12-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889322538, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889322538, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889322538, creator=13701087609, updateTime=1789889322538, updator=13701087609, issue=Issue{id=1308453882472984927, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='3', pageStart='1', pageEnd='150', issueExtLink='null', onlineDate='null', pubDate='1781020800000', pubDateStr='2026-06-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789889315479, creator='13701087609', updateTime=1789889814158, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308455974147224139, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453882472984927, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308455974151418444, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453882472984927, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=117, endPage=125, ext={EN=ArticleExt(id=1308453912269320791, articleId=1308453912084771414, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=High-Precision Measurement Method for Shield Segment Misalignment Movement Based on Laser Scanning, columnId=1152669335454658940, journalTitle=Urban Rapid Rail Transit, columnName=Academic Discussion, runingTitle=null, highlight=null, articleAbstract=

Targeting the problems of low efficiency, poor coverage, and insufficient accuracy in shield tunnel segment misalignment measurement, an automatic measurement algorithm integrating laser point cloud grayscale conversion with deep learning is proposed. This method can accurately measure segment misalignment, effectively reduce the risk of water leakage caused by misalignment, and maintain the service performance of tunnel structures. First, the collected three-dimensional laser point cloud is converted into grayscale images with enhanced edge features. Then, the lightweight EfficientNet network is utilized to efficiently identify the bounding boxes of inter-ring and intra-ring joints of segments. The bounding box coordinates are mapped to the three-dimensional point-cloud space to achieve precise segmentation and extraction of point clouds on both sides of segment joints. Finally, the average radial distances from the point clouds on both sides of joints to the center of the fitted ellipse of the tunnel segment structure are calculated respectively, with their difference representing the segment misalignment, enabling non-contact, high-precision measurement of shield tunnel segment misalignment. A tunnel structure inspection vehicle equipped with a three-dimensional laser scanner was developed, with the above algorithm integrated and supporting data acquisition and processing software developed. An engineering pilot application was conducted in a shield tunnel section of Hangzhou Metro Line 1, with laser scanning at a moving speed of 20 km/h. Comparative analysis between the inspection vehicle measurement results and manual measurement results shows that the two measurement results are close, with a maximum absolute deviation of 1.6 mm. This method can maintain high measurement accuracy even under high-speed movement conditions, meeting the industry demand for rapid, comprehensive detection of shield tunnel segment misalignment.

, authors=Liyan JU¹, Gaozhao PANG^{2, 3}, Hongbo LIU¹, Xuezeng LIU^{4, 5}, Xingen LIU^{2, 3}, authorsList=Liyan JU, Gaozhao PANG, Hongbo LIU, Xuezeng LIU, Xingen LIU, 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=1308453912512590424, articleId=1308453912084771414, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=基于激光扫描的盾构管片错台移动高精度测量方法, columnId=1152669335668568445, journalTitle=都市快轨交通, columnName=学术探讨, runingTitle=null, highlight=null, articleAbstract=

针对盾构隧道管片错台量测量效率低、覆盖性差、精度不足的问题，提出一种融合激光点云灰度化与深度学习的自动测量算法。该方法可精准测量管片错台，有效降低因错台引发的渗漏风险，保障隧道结构服役性能。首先将采集的三维激光点云转换为灰度图像，并增强其边缘特征；然后利用轻量化EfficientNet网络高效识别管片环间、环内接缝的边界框，将边界框坐标映射至点云三维空间，实现管片接缝两侧点云精准分割与提取；最后分别计算接缝两侧点云至隧道管片结构拟合椭圆圆心的平均径向距离，其差值为管片错台量，可实现盾构隧道管片错台非接触、高精度测量。研制搭载三维激光扫描仪的隧道结构检测车，集成上述算法，开发配套的数据采集与数据处理软件。在杭州地铁1号线盾构区间隧道进行工程试点应用，结果表明，检测车测量结果与人工测量结果接近，绝对最大偏差1.6 mm。本方法可确保在高速移动条件下，仍然具有较高的测量精度，满足盾构隧道管片错台量快速、覆盖式检测行业需求。

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鞠丽艳，女，博士，高级工程师，从事轨道交通土建结构技术研究及管理工作，angel5666@126.com

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鞠丽艳，女，博士，高级工程师，从事轨道交通土建结构技术研究及管理工作，angel5666@126.com

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