Article(id=1217789889077236518, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1217789884081820362, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2406516, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1724947200000, receivedDateStr=2024-08-30, revisedDate=1744646400000, revisedDateStr=2025-04-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1768273334998, onlineDateStr=2026-01-13, pubDate=1753632000000, pubDateStr=2025-07-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1768273334998, onlineIssueDateStr=2026-01-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1768273334998, creator=13701087609, updateTime=1768273334998, updator=13701087609, issue=Issue{id=1217789884081820362, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='21', pageStart='8761', pageEnd='9209', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1768273333807, creator=13701087609, updateTime=1768273602927, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1217791012932604619, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1217789884081820362, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1217791012932604620, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1217789884081820362, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=9148, endPage=9157, ext={EN=ArticleExt(id=1217789889597330238, articleId=1217789889077236518, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Extracting Arch Rib Shape of Steel Arch Bridges Using Improved DBSCAN Algorithm Integrated with RANSAC, columnId=1156262728772735295, journalTitle=Science Technology and Engineering, columnName=Papers·Traffics and Transportations, runingTitle=null, highlight=null, articleAbstract=

The alignment monitoring of steel arch bridges constitutes an essential component of bridge health monitoring systems. Three-dimensional laser scanning technology was utilized, and the traditional density-based spatial clustering of applications with noise(DBSCAN) algorithm was improved by integrating the random sample consensus(RANSAC) algorithm to extract the alignment of steel arch bridge ribs. Three-dimensional laser point cloud data, characterized by its comprehensiveness and detailed representation, is capable of fully presenting the structural shape and deformation information of the bridge. The RANSAC-integrated improved DBSCAN algorithm, constrained by the structural features of the steel arch bridge, effectively achieves the removal of discrete points as well as point clouds from the bridge deck, cross bracing, lateral connections, and web members. Point clouds extracted using the RANSAC-integrated improved DBSCAN algorithm are fitted to identify key points, and a comparison is made with results obtained manually. The extraction errors for the key points of the arch ribs are all within the millimeter range, with the maximum error being 9.2 mm and the minimum error being 0.1 mm. This extraction method is demonstrated to more accurately and effectively accomplish the alignment extraction of steel arch bridges, achieving millimeter-level precision in alignment extraction. It significantly reduces labor and time costs, provides better robustness for the complex structures of steel arch bridges, and adapts well to practical production demands.

, correspAuthors=Xue-yan ZHAO, 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=Guo-bing PAN, Xue-yan ZHAO, Wei WU, Xiao-dong JIN, Hong-bing YU), CN=ArticleExt(id=1217789893003105239, articleId=1217789889077236518, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=融合RANSAC的改进DBSCAN算法提取钢拱桥拱肋线形, columnId=1156262730664366426, journalTitle=科学技术与工程, columnName=论文·交通运输, runingTitle=null, highlight=null, articleAbstract=

钢拱桥的线形监测是桥梁健康监测系统的重要组成部分。运用三维激光扫描技术,融合随机抽样一致(random sample consensus,RANSAC)算法对传统的具有噪声的基于密度的聚类方法(density-based spatial clustering of applications with noise,DBSCAN)算法进行改进,对钢拱桥拱肋线形进行提取。三维激光点云数据具有全面性和细节体现的优势,能够完整地呈现桥梁结构的形状和变形信息,融合RANSAC的改进DBSCAN算法根据钢拱桥结构特征对聚类结果进行约束,能够很好地实现删除离散点及桥面、横撑、横联和腹杆部分的点云这一目的。根据融合RANSAC的改进DBSCAN算法提取出的点云进行关键点拟合,与人工提取结果进行对比,拱肋关键点提取误差均在毫米级,最大误差为9.2 mm,最小误差为0.1 mm,此提取方法能够更加准确有效地完成钢拱桥线形提取,使线形提取精度达到毫米级,大大降低了人力成本和时间成本,对钢拱桥的复杂结构有更好的鲁棒性,能很好地适应实际生产需求。

, correspAuthors=赵雪妍, authorNote=null, correspAuthorsNote=
* 赵雪妍(2000—),女,汉族,内蒙古包头人,硕士研究生。研究方向:点云处理。E-mail:
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=J1wKxkwqkatIxj205AVlNw==, magXml=pyg++AMBAAlzmt6jVh1Iyg==, pdfUrl=null, pdf=78b5dlUpTBom3W81pw1IMQ==, pdfFileSize=10805192, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=t+U3cRiyQc2exPhydl33cg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=+MbFdiCD3QhcDLhpyVBYdg==, mapNumber=null, authorCompany=null, fund=null, authors=

潘国兵(1976—),男,汉族,四川眉山人,博士,教授。研究方向:智能测绘。E-mail:

, authorsList=潘国兵, 赵雪妍, 吴畏, 金晓东, 虞洪兵)}, authors=[Author(id=1217860108827153283, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=panguobing@126.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1217860109020091286, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, authorId=1217860108827153283, language=EN, stringName=Guo-bing PAN, firstName=Guo-bing, middleName=null, lastName=PAN, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Smart Cities Institute, Chongqing Jiaotong University, Chongqing 400074, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1217860109112365987, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, authorId=1217860108827153283, language=CN, stringName=潘国兵, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=重庆交通大学智慧城市学院, 重庆 400074, bio={"content":"

潘国兵(1976—),男,汉族,四川眉山人,博士,教授。研究方向:智能测绘。E-mail:

"}, bioImg=null, bioContent=

潘国兵(1976—),男,汉族,四川眉山人,博士,教授。研究方向:智能测绘。E-mail:

, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1217860108403528551, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, xref=null, ext=[AuthorCompanyExt(id=1217860108432888682, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, companyId=1217860108403528551, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Smart Cities Institute, Chongqing Jiaotong University, Chongqing 400074, China), AuthorCompanyExt(id=1217860108445471596, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, companyId=1217860108403528551, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=重庆交通大学智慧城市学院, 重庆 400074)])]), Author(id=1217860109263360950, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, orderNo=1, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=1611731466@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=1, authorType=1, ext={EN=AuthorExt(id=1217860109464687559, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, authorId=1217860109263360950, language=EN, stringName=Xue-yan ZHAO, firstName=Xue-yan, middleName=null, lastName=ZHAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=*, address=Smart Cities Institute, Chongqing Jiaotong University, Chongqing 400074, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1217860109611488215, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, authorId=1217860109263360950, language=CN, stringName=赵雪妍, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=*, address=重庆交通大学智慧城市学院, 重庆 400074, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1217860108403528551, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, xref=null, ext=[AuthorCompanyExt(id=1217860108432888682, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, companyId=1217860108403528551, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Smart Cities Institute, Chongqing Jiaotong University, Chongqing 400074, China), AuthorCompanyExt(id=1217860108445471596, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, companyId=1217860108403528551, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=重庆交通大学智慧城市学院, 重庆 400074)])]), Author(id=1217860109775066085, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, orderNo=2, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1217860109921866736, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, authorId=1217860109775066085, language=EN, stringName=Wei WU, firstName=Wei, middleName=null, lastName=WU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Smart Cities Institute, Chongqing Jiaotong University, Chongqing 400074, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1217860110152553470, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, authorId=1217860109775066085, language=CN, stringName=吴畏, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=重庆交通大学智慧城市学院, 重庆 400074, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1217860108403528551, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, xref=null, ext=[AuthorCompanyExt(id=1217860108432888682, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, companyId=1217860108403528551, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Smart Cities Institute, Chongqing Jiaotong University, Chongqing 400074, China), AuthorCompanyExt(id=1217860108445471596, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, companyId=1217860108403528551, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=重庆交通大学智慧城市学院, 重庆 400074)])]), Author(id=1217860111557644308, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, orderNo=3, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1217860111670890531, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, authorId=1217860111557644308, language=EN, stringName=Xiao-dong JIN, firstName=Xiao-dong, middleName=null, lastName=JIN, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Smart Cities Institute, Chongqing Jiaotong University, Chongqing 400074, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1217860111821885493, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, authorId=1217860111557644308, language=CN, stringName=金晓东, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=重庆交通大学智慧城市学院, 重庆 400074, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1217860108403528551, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, xref=null, ext=[AuthorCompanyExt(id=1217860108432888682, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, companyId=1217860108403528551, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Smart Cities Institute, Chongqing Jiaotong University, Chongqing 400074, China), AuthorCompanyExt(id=1217860108445471596, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, companyId=1217860108403528551, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=重庆交通大学智慧城市学院, 重庆 400074)])]), Author(id=1217860111935131714, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, orderNo=4, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1217860112203567185, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, authorId=1217860111935131714, language=EN, stringName=Hong-bing YU, firstName=Hong-bing, middleName=null, lastName=YU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Smart Cities Institute, Chongqing Jiaotong University, Chongqing 400074, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1217860112400699492, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, authorId=1217860111935131714, language=CN, stringName=虞洪兵, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=重庆交通大学智慧城市学院, 重庆 400074, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1217860108403528551, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, xref=null, ext=[AuthorCompanyExt(id=1217860108432888682, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, companyId=1217860108403528551, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Smart Cities Institute, Chongqing Jiaotong University, Chongqing 400074, China), AuthorCompanyExt(id=1217860108445471596, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, companyId=1217860108403528551, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=重庆交通大学智慧城市学院, 重庆 400074)])])], keywords=[Keyword(id=1217860112711078007, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, orderNo=1, keyword=3D laser), Keyword(id=1217860112807547010, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, orderNo=2, keyword=line shape monitoring), Keyword(id=1217860112979513484, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, orderNo=3, keyword=RANSAC algorithm), Keyword(id=1217860113105342620, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, orderNo=4, keyword=improved DBSCAN algorithm), Keyword(id=1217860113226977448, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, orderNo=1, keyword=三维激光), Keyword(id=1217860113306669233, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, orderNo=2, keyword=线形监测), Keyword(id=1217860113415721157, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, orderNo=3, keyword=RANSAC算法), Keyword(id=1217860113499607251, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, orderNo=4, keyword=改进DBSCAN算法)], refs=[Reference(id=1217860120231465651, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2023, volume=53, issue=5, pageStart=756, pageEnd=764, url=null, language=null, rfNumber=[1], rfOrder=0, authorNames=王浩琛, 冯东明, 吴刚, journalName=东南大学学报(自然科学版), refType=null, unstructuredReference=王浩琛, 冯东明, 吴刚, 等. 基于三维激光点云的公路曲线桥梁三维形貌与变形测量[J]. 东南大学学报(自然科学版), 2023, 53(5): 756-764., articleTitle=基于三维激光点云的公路曲线桥梁三维形貌与变形测量, refAbstract=null), Reference(id=1217860120441180870, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2023, volume=53, issue=5, pageStart=756, pageEnd=764, url=null, language=null, rfNumber=[1], rfOrder=1, authorNames=Wang Haochen, Feng Dongming, Wu Gang, journalName=Journal of Southeast University (Natural Science Edition), refType=null, unstructuredReference=Wang Haochen, Feng Dongming, Wu Gang, et al. Three-dimensional topography and deformation measurement of highway curved bridges based on 3D laser point clouds[J]. Journal of Southeast University (Natural Science Edition), 2023, 53(5): 756-764., articleTitle=Three-dimensional topography and deformation measurement of highway curved bridges based on 3D laser point clouds, refAbstract=null), Reference(id=1217860120592175823, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2021, volume=40, issue=8, pageStart=5, pageEnd=8, url=null, language=null, rfNumber=[2], rfOrder=2, authorNames=冯麟, 周志祥, 唐亮, journalName=实验室研究与探索, refType=null, unstructuredReference=冯麟, 周志祥, 唐亮, 等. 基于三维扫描点云数据的模型桥形变获取[J]. 实验室研究与探索, 2021, 40(8): 5-8, 18., articleTitle=基于三维扫描点云数据的模型桥形变获取, refAbstract=null), Reference(id=1217860120806085339, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2021, volume=40, issue=8, pageStart=5, pageEnd=8, url=null, language=null, rfNumber=[2], rfOrder=3, authorNames=Feng Lin, Zhou Zhixiang, Tang Liang, journalName=Laboratory Research and Exploration, refType=null, unstructuredReference=Feng Lin, Zhou Zhixiang, Tang Liang, et al. Deformation acquisition of model bridges based on 3D scanning point cloud data[J]. Laboratory Research and Exploration, 2021, 40(8): 5-8, 18., articleTitle=Deformation acquisition of model bridges based on 3D scanning point cloud data, refAbstract=null), Reference(id=1217860120957080297, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2022, volume=37, issue=12, pageStart=18, pageEnd=23, url=null, language=null, rfNumber=[3], rfOrder=4, authorNames=安路明, 赵健, 任延龙, journalName=钢结构(中英文), refType=null, unstructuredReference=安路明, 赵健, 任延龙, 等. 大跨度钢桁拱桥三维激光扫描线形监控技术研究[J]. 钢结构(中英文), 2022, 37(12): 18-23., articleTitle=大跨度钢桁拱桥三维激光扫描线形监控技术研究, refAbstract=null), Reference(id=1217860121099686642, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2022, volume=37, issue=12, pageStart=18, pageEnd=23, url=null, language=null, rfNumber=[3], rfOrder=5, authorNames=An Luming, Zhao Jian, Ren Yanlong, journalName=Steel Construction (Chinese-English), refType=null, unstructuredReference=An Luming, Zhao Jian, Ren Yanlong, et al. Research on 3D laser scanning shape monitoring technology for large-span steel truss arch bridges[J]. Steel Construction (Chinese-English), 2022, 37(12): 18-23., articleTitle=Research on 3D laser scanning shape monitoring technology for large-span steel truss arch bridges, refAbstract=null), Reference(id=1217860121204544250, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2024, volume=52, issue=7, pageStart=97, pageEnd=106, url=null, language=null, rfNumber=[4], rfOrder=6, authorNames=彭仪普, 李剑, 韩衍群, journalName=华南理工大学学报(自然科学版), refType=null, unstructuredReference=彭仪普, 李剑, 韩衍群, 等. 基于点云切片算法的铁路钢桁拱桥线形分析[J]. 华南理工大学学报(自然科学版), 2024, 52(7): 97-106., articleTitle=基于点云切片算法的铁路钢桁拱桥线形分析, refAbstract=null), Reference(id=1217860121342956297, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2024, volume=52, issue=7, pageStart=97, pageEnd=106, url=null, language=null, rfNumber=[4], rfOrder=7, authorNames=Peng Yipu, Li Jian, Han Yanqun, journalName=Journal of South China University of Technology (Natural Science Edition), refType=null, unstructuredReference=Peng Yipu, Li Jian, Han Yanqun, et al. Railway steel truss arch bridge shape analysis based on point cloud slicing algorithm[J]. Journal of South China University of Technology (Natural Science Edition), 2024, 52(7): 97-106., articleTitle=Railway steel truss arch bridge shape analysis based on point cloud slicing algorithm, refAbstract=null), Reference(id=1217860121540088597, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2022, volume=22, issue=36, pageStart=16211, pageEnd=16218, url=null, language=null, rfNumber=[5], rfOrder=8, authorNames=白祖应, 卯申殷, 胡开心, journalName=科学技术与工程, refType=null, unstructuredReference=白祖应, 卯申殷, 胡开心, 等. 基于点云的劲性骨架拱桥自动化线形监测方法[J]. 科学技术与工程, 2022, 22(36): 16211-16218., articleTitle=基于点云的劲性骨架拱桥自动化线形监测方法, refAbstract=null), Reference(id=1217860121674306339, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2022, volume=22, issue=36, pageStart=16211, pageEnd=16218, url=null, language=null, rfNumber=[5], rfOrder=9, authorNames=Bai Zuying, Mao Shenyin, Hu Kaixin, journalName=Science Technology and Engineering, refType=null, unstructuredReference=Bai Zuying, Mao Shenyin, Hu Kaixin, et al. Automated shape monitoring method for stiffened skeleton arch bridges based on point cloud[J]. Science Technology and Engineering, 2022, 22(36): 16211-16218., articleTitle=Automated shape monitoring method for stiffened skeleton arch bridges based on point cloud, refAbstract=null), Reference(id=1217860121821106995, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2019, volume=38, issue=1, pageStart=167, pageEnd=196, url=null, language=null, rfNumber=[6], rfOrder=10, authorNames=Kaiser A, Ybanez Z J A, Boubekeur T, journalName=Computer Graphics Forum, refType=null, unstructuredReference=Kaiser A, Ybanez Z J A, Boubekeur T. A survey of simple geometric primitives detection methods for captured 3D data[J]. Computer Graphics Forum, 2019, 38(1): 167-196., articleTitle=A survey of simple geometric primitives detection methods for captured 3D data, refAbstract=null), Reference(id=1217860121955324737, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2007, volume=26, issue=2, pageStart=214, pageEnd=226, url=null, language=null, rfNumber=[7], rfOrder=11, authorNames=Schnabel R, Wahl R, Klein R, journalName=Computer Graphics Forum, refType=null, unstructuredReference=Schnabel R, Wahl R, Klein R. Efficient RANSAC for point-cloud shape detection[J]. Computer Graphics Forum, 2007, 26(2): 214-226., articleTitle=Efficient RANSAC for point-cloud shape detection, refAbstract=null), Reference(id=1217860122110513998, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2024, volume=24, issue=30, pageStart=12843, pageEnd=12852, url=null, language=null, rfNumber=[8], rfOrder=12, authorNames=朱旺煌, 刘荣, 龚循强, journalName=科学技术与工程, refType=null, unstructuredReference=朱旺煌, 刘荣, 龚循强, 等. 基于倾斜摄影测量点云的建筑物单体提取[J]. 科学技术与工程, 2024, 24(30): 12843-12852., articleTitle=基于倾斜摄影测量点云的建筑物单体提取, refAbstract=null), Reference(id=1217860122328617815, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2024, volume=24, issue=30, pageStart=12843, pageEnd=12852, url=null, language=null, rfNumber=[8], rfOrder=13, authorNames=Zhu Wanghuang, Liu Rong, Gong Xunqiang, journalName=Science Technology and Engineering, refType=null, unstructuredReference=Zhu Wanghuang, Liu Rong, Gong Xunqiang, et al. Extraction of individual buildings based on point clouds from oblique photogrammetry[J]. Science Technology and Engineering, 2024, 24 (30): 12843-12852., articleTitle=Extraction of individual buildings based on point clouds from oblique photogrammetry, refAbstract=null), Reference(id=1217860122571887462, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2024, volume=24, issue=13, pageStart=5427, pageEnd=5435, url=null, language=null, rfNumber=[9], rfOrder=14, authorNames=申燚, 赵泽钰, 袁明新, journalName=科学技术与工程, refType=null, unstructuredReference=申燚, 赵泽钰, 袁明新, 等. 基于四叉树扇形层值聚类的无人船障碍物检测[J]. 科学技术与工程, 2024, 24 (13): 5427-5435., articleTitle=基于四叉树扇形层值聚类的无人船障碍物检测, refAbstract=null), Reference(id=1217860122718688116, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2024, volume=24, issue=13, pageStart=5427, pageEnd=5435, url=null, language=null, rfNumber=[9], rfOrder=15, authorNames=Shen Yi, Zhao Zeyu, Yuan Mingxin, journalName=Science Technology and Engineering, refType=null, unstructuredReference=Shen Yi, Zhao Zeyu, Yuan Mingxin, et al. Obstacle detection for unmanned ship based on quadtree sector layer value clustering[J]. Science Technology and Engineering, 2024, 24 (13): 5427-5435., articleTitle=Obstacle detection for unmanned ship based on quadtree sector layer value clustering, refAbstract=null), Reference(id=1217860122840322945, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2020, volume=null, issue=null, pageStart=949, pageEnd=953, url=null, language=null, rfNumber=[10], rfOrder=16, authorNames=Deng D, journalName=Proceedings of the 2020 7th International Forum on Electrical Engineering and Automation (IFEEA), refType=null, unstructuredReference=Deng D. DBSCAN clustering algorithm based on density[C]// Proceedings of the 2020 7th International Forum on Electrical Engineering and Automation (IFEEA). Hefei: IEEE, 2020: 949-953., articleTitle=DBSCAN clustering algorithm based on density, refAbstract=null), Reference(id=1217860122936791945, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2020, volume=163, issue=null, pageStart=18, pageEnd=35, url=null, language=null, rfNumber=[11], rfOrder=17, authorNames=Ssanchez J, Denis F, Coeurjolly D, journalName=ISPRS Journal of Photogrammetry and Remote Sensing, refType=null, unstructuredReference=Ssanchez J, Denis F, Coeurjolly D, et al. Robust normal vector estimation in 3D point clouds through iterative principal component analysis[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163: 18-35., articleTitle=Robust normal vector estimation in 3D point clouds through iterative principal component analysis, refAbstract=null), Reference(id=1217860123045843857, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, doi=null, pmid=null, pmcid=null, year=2019, volume=233, issue=8, pageStart=2879, pageEnd=2893, url=null, language=null, rfNumber=[12], rfOrder=18, authorNames=Zhang H, Xu C, Xiao D, journalName=Part C: Journal of Mechanical Engineering Science, refType=null, unstructuredReference=Zhang H, Xu C, Xiao D. Offline correction of tool path deviations for robot-assisted ultrasonic nondestructive testing[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2019, 233(8): 2879-2893., articleTitle=Offline correction of tool path deviations for robot-assisted ultrasonic nondestructive testing, refAbstract=null)], funds=[Fund(id=1217860119094809242, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, awardId=42074004, language=CN, fundingSource=国家自然科学基金(42074004), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1217860108403528551, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, xref=null, ext=[AuthorCompanyExt(id=1217860108432888682, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, companyId=1217860108403528551, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Smart Cities Institute, Chongqing Jiaotong University, Chongqing 400074, China), AuthorCompanyExt(id=1217860108445471596, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, companyId=1217860108403528551, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=重庆交通大学智慧城市学院, 重庆 400074)])], figs=[ArticleFig(id=1217860113822568688, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Fig.1, caption=Comparison of clustering effects between the improved DBSCAN algorithm integrated with RANSAC and the original DBSCAN algorithm, figureFileSmall=IJb+KOZ12kbPvm6WUjTObA==, figureFileBig=qdw+oOBzqNf1fHjwBFrnjw==, tableContent=null), ArticleFig(id=1217860113927426305, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=图1, caption=融合RANSAC的改进DBSCAN算法与DBSCAN算法聚类对比效果, figureFileSmall=IJb+KOZ12kbPvm6WUjTObA==, figureFileBig=qdw+oOBzqNf1fHjwBFrnjw==, tableContent=null), ArticleFig(id=1217860114179084577, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Fig.2, caption=Raw point cloud data of the steel arch bridge, figureFileSmall=Qm4O+HIinaseoaCqqltfqw==, figureFileBig=YDnoS8VI2aGhBNaJFysFdQ==, tableContent=null), ArticleFig(id=1217860114405577011, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=图2, caption=钢拱桥原始点云数据, figureFileSmall=Qm4O+HIinaseoaCqqltfqw==, figureFileBig=YDnoS8VI2aGhBNaJFysFdQ==, tableContent=null), ArticleFig(id=1217860114552377672, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Fig.3, caption=Preprocessed raw point cloud data of the steel arch bridge, figureFileSmall=Udq0j69c8s46cjHyX0of7g==, figureFileBig=8mhzKJdYZJ/gWwbZ+9eSVg==, tableContent=null), ArticleFig(id=1217860114678206807, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=图3, caption=初步处理后的钢拱桥原始点云数据, figureFileSmall=Udq0j69c8s46cjHyX0of7g==, figureFileBig=8mhzKJdYZJ/gWwbZ+9eSVg==, tableContent=null), ArticleFig(id=1217860115995218282, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Fig.4, caption=Preprocessed point cloud data of key parts of the arch rib, figureFileSmall=/zSUBWTni3nagKlnNYo0qw==, figureFileBig=RirHnawzV7ZPBbdlnHPpEg==, tableContent=null), ArticleFig(id=1217860116121047413, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=图4, caption=初步处理后的拱肋关键部分点云, figureFileSmall=/zSUBWTni3nagKlnNYo0qw==, figureFileBig=RirHnawzV7ZPBbdlnHPpEg==, tableContent=null), ArticleFig(id=1217860116234293630, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Fig.5, caption=Clustering of the point cloud on both sides of the arch rib using the improved DBSCAN algorithm integrated with RANSAC, figureFileSmall=cquNCsGDH5Q9xDLwh5KNsQ==, figureFileBig=xaalmfzH6EDCK5XGu+ZFUA==, tableContent=null), ArticleFig(id=1217860116452397456, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=图5, caption=对拱肋两侧点云进行融合RANSAC的DBSCAN聚类, figureFileSmall=cquNCsGDH5Q9xDLwh5KNsQ==, figureFileBig=xaalmfzH6EDCK5XGu+ZFUA==, tableContent=null), ArticleFig(id=1217860116586615197, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Fig.6, caption=Clustering of the point cloud at the bottom of the arch rib using the improved DBSCAN algorithm integrated with RANSAC, figureFileSmall=Ujyrf9QRZwxtmxHD9Rd07g==, figureFileBig=dnFnUb4buUlHeUzmM+C0ww==, tableContent=null), ArticleFig(id=1217860116712444329, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=图6, caption=对拱肋底部点云进行融合RANSAC的DBSCAN聚类, figureFileSmall=Ujyrf9QRZwxtmxHD9Rd07g==, figureFileBig=dnFnUb4buUlHeUzmM+C0ww==, tableContent=null), ArticleFig(id=1217860116859244987, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Fig.7, caption=Comparison of DBSCAN clustering and RANSAC-integrated DBSCAN clustering results for point clouds on both sides of the arch rib, figureFileSmall=h+mW/iVjREmOLfAeIk27Pg==, figureFileBig=JYguyHt+tCm7uWYpB4dSng==, tableContent=null), ArticleFig(id=1217860117027017167, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=图7, caption=拱肋两侧点云的DBSCAN聚类与融合RANSAC的DBSCAN聚类结果对比, figureFileSmall=h+mW/iVjREmOLfAeIk27Pg==, figureFileBig=JYguyHt+tCm7uWYpB4dSng==, tableContent=null), ArticleFig(id=1217860117157040607, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Fig.8, caption=Comparison of DBSCAN clustering and RANSAC-integrated DBSCAN clustering results for point clouds at the bottom of the arch rib, figureFileSmall=pLaoR9JNgUhGli1yFEp8WA==, figureFileBig=ngrpOK+GxlM8JwKc1AVooQ==, tableContent=null), ArticleFig(id=1217860117303841259, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=图8, caption=拱肋底部点云的DBSCAN聚类与融合RANSAC的DBSCAN聚类结果对比, figureFileSmall=pLaoR9JNgUhGli1yFEp8WA==, figureFileBig=ngrpOK+GxlM8JwKc1AVooQ==, tableContent=null), ArticleFig(id=1217860117412893177, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Fig.9, caption=Filtered point cloud results of the arch rib section, figureFileSmall=trYCTlkOhgFl86JfW7YucA==, figureFileBig=/vW5qXDSSx/Pe2QWvhQIRg==, tableContent=null), ArticleFig(id=1217860117563888139, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=图9, caption=对桥梁拱肋部分点云筛选结果, figureFileSmall=trYCTlkOhgFl86JfW7YucA==, figureFileBig=/vW5qXDSSx/Pe2QWvhQIRg==, tableContent=null), ArticleFig(id=1217860117693911576, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Fig.10, caption=Distribution of differences in key points of the arch rib, figureFileSmall=Pb61Neh8YXdcU5See2PWuw==, figureFileBig=NqfBc10E1eKFPIAZnd2zYA==, tableContent=null), ArticleFig(id=1217860117807157801, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=图10, caption=拱肋关键点差值分布, figureFileSmall=Pb61Neh8YXdcU5See2PWuw==, figureFileBig=NqfBc10E1eKFPIAZnd2zYA==, tableContent=null), ArticleFig(id=1217860117916209717, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Table 1, caption=

Comparison of key points in the point cloud at the bottom of the arch rib

, figureFileSmall=null, figureFileBig=null, tableContent=
里程
桩号/m
下游下侧拱肋下弦杆最低点 下游上侧拱肋下弦杆最低点 上游下侧拱肋下弦杆最低点 上游上侧拱肋下弦杆最低点
人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm
K0+49.000 209.008 9 209.005 8 3.1 208.995 8 209.001 9 -6.1 209.006 7 209.013 6 -6.9 208.987 0 208.987 5 -0.5
K0+57.000 213.271 3 213.276 8 -5.5 213.264 1 213.271 5 -7.4 213.269 2 213.277 6 -8.4 213.254 3 213.260 7 -6.4
K0+65.000 217.009 3 217.004 7 4.6 217.007 2 217.007 3 -0.1 217.006 4 217.009 3 -2.9 217.002 6 217.003 7 -1.1
K0+73.000 220.271 7 220.275 4 -3.7 220.270 0 220.269 8 0.2 220.270 8 220.272 1 -1.3 220.269 1 220.277 6 -8.5
K0+81.000 223.071 1 223.072 3 -1.2 223.065 4 223.072 3 -6.9 223.058 5 223.061 0 -2.5 223.047 9 223.049 5 -1.6
K0+89.000 225.420 0 225.420 3 -0.3 225.414 3 225.422 0 -7.7 225.404 3 225.409 4 -5.1 225.393 3 225.394 8 -1.5
K0+97.000 227.305 9 227.308 8 -2.8 227.300 8 227.305 8 -5.0 227.304 1 227.311 0 -6.9 227.292 8 227.295 4 -2.6
K0+105.000 228.790 1 228.793 9 -3.8 228.784 3 228.786 9 -2.6 228.784 9 228.787 6 -2.7 228.769 7 228.771 6 -1.9
K0+113.000 229.826 2 229.832 1 -5.9 229.810 8 229.818 2 -7.4 229.821 0 229.822 5 -1.5 229.815 6 229.818 2 -2.6
K0+121.000 230.478 7 230.476 1 2.6 230.468 6 230.473 7 -5.1 230.451 3 230.453 9 -2.6 230.465 3 230.466 4 -1.1
K0+129.000 230.683 9 230.688 5 -4.6 230.676 1 230.676 8 -0.7 230.662 1 230.662 4 -0.3 230.672 0 230.673 6 -1.6
K0+137.000 230.467 3 230.466 2 1.1 230.465 9 230.466 8 -0.9 230.447 3 230.451 5 -4.2 230.447 0 230.454 0 -7.0
K0+145.000 229.812 5 229.820 8 -8.3 229.813 5 229.816 5 -3.0 229.810 9 229.811 0 -0.1 229.813 2 229.814 4 -1.2
K0+153.000 228.782 5 228.778 4 4.1 228.779 5 228.788 4 -8.9 228.788 2 228.790 5 -2.3 228.777 5 228.770 7 6.8
K0+161.000 227.306 7 227.299 8 6.9 227.306 1 227.314 4 -8.3 227.316 1 227.317 5 -1.4 227.313 9 227.317 1 -3.2
K0+169.000 225.416 0 225.416 8 -0.8 225.409 0 225.408 3 0.7 225.424 2 225.425 1 -0.9 225.423 1 225.420 4 2.7
K0+177.000 223.065 2 223.059 9 5.3 223.064 4 223.067 5 -3.1 223.071 7 223.075 5 -3.8 223.070 9 223.068 9 2.0
K0+185.000 220.275 4 220.267 7 7.7 220.267 4 220.260 9 6.5 220.275 6 220.282 4 -6.8 220.281 4 220.282 7 -1.3
K0+193.000 217.011 1 217.011 6 -0.5 217.003 1 217.001 6 1.5 217.004 7 217.007 3 -2.6 217.017 5 217.015 8 1.7
K0+201.000 213.261 4 213.270 4 -9.0 213.259 7 213.259 1 0.6 213.253 0 213.251 1 1.9 213.266 0 213.258 9 7.1
K0+209.000 208.991 3 208.989 0 2.3 208.990 8 208.995 3 -4.5 208.981 7 208.981 0 0.8 208.991 4 208.989 6 1.8
K0+217.000 204.200 6 204.197 5 3.1 204.197 2 204.192 5 4.7 204.192 8 204.183 6 9.2 204.202 8 204.205 3 -2.5
), ArticleFig(id=1217860118113342013, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=表1, caption=

拱肋底部点云关键点对比

, figureFileSmall=null, figureFileBig=null, tableContent=
里程
桩号/m
下游下侧拱肋下弦杆最低点 下游上侧拱肋下弦杆最低点 上游下侧拱肋下弦杆最低点 上游上侧拱肋下弦杆最低点
人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm
K0+49.000 209.008 9 209.005 8 3.1 208.995 8 209.001 9 -6.1 209.006 7 209.013 6 -6.9 208.987 0 208.987 5 -0.5
K0+57.000 213.271 3 213.276 8 -5.5 213.264 1 213.271 5 -7.4 213.269 2 213.277 6 -8.4 213.254 3 213.260 7 -6.4
K0+65.000 217.009 3 217.004 7 4.6 217.007 2 217.007 3 -0.1 217.006 4 217.009 3 -2.9 217.002 6 217.003 7 -1.1
K0+73.000 220.271 7 220.275 4 -3.7 220.270 0 220.269 8 0.2 220.270 8 220.272 1 -1.3 220.269 1 220.277 6 -8.5
K0+81.000 223.071 1 223.072 3 -1.2 223.065 4 223.072 3 -6.9 223.058 5 223.061 0 -2.5 223.047 9 223.049 5 -1.6
K0+89.000 225.420 0 225.420 3 -0.3 225.414 3 225.422 0 -7.7 225.404 3 225.409 4 -5.1 225.393 3 225.394 8 -1.5
K0+97.000 227.305 9 227.308 8 -2.8 227.300 8 227.305 8 -5.0 227.304 1 227.311 0 -6.9 227.292 8 227.295 4 -2.6
K0+105.000 228.790 1 228.793 9 -3.8 228.784 3 228.786 9 -2.6 228.784 9 228.787 6 -2.7 228.769 7 228.771 6 -1.9
K0+113.000 229.826 2 229.832 1 -5.9 229.810 8 229.818 2 -7.4 229.821 0 229.822 5 -1.5 229.815 6 229.818 2 -2.6
K0+121.000 230.478 7 230.476 1 2.6 230.468 6 230.473 7 -5.1 230.451 3 230.453 9 -2.6 230.465 3 230.466 4 -1.1
K0+129.000 230.683 9 230.688 5 -4.6 230.676 1 230.676 8 -0.7 230.662 1 230.662 4 -0.3 230.672 0 230.673 6 -1.6
K0+137.000 230.467 3 230.466 2 1.1 230.465 9 230.466 8 -0.9 230.447 3 230.451 5 -4.2 230.447 0 230.454 0 -7.0
K0+145.000 229.812 5 229.820 8 -8.3 229.813 5 229.816 5 -3.0 229.810 9 229.811 0 -0.1 229.813 2 229.814 4 -1.2
K0+153.000 228.782 5 228.778 4 4.1 228.779 5 228.788 4 -8.9 228.788 2 228.790 5 -2.3 228.777 5 228.770 7 6.8
K0+161.000 227.306 7 227.299 8 6.9 227.306 1 227.314 4 -8.3 227.316 1 227.317 5 -1.4 227.313 9 227.317 1 -3.2
K0+169.000 225.416 0 225.416 8 -0.8 225.409 0 225.408 3 0.7 225.424 2 225.425 1 -0.9 225.423 1 225.420 4 2.7
K0+177.000 223.065 2 223.059 9 5.3 223.064 4 223.067 5 -3.1 223.071 7 223.075 5 -3.8 223.070 9 223.068 9 2.0
K0+185.000 220.275 4 220.267 7 7.7 220.267 4 220.260 9 6.5 220.275 6 220.282 4 -6.8 220.281 4 220.282 7 -1.3
K0+193.000 217.011 1 217.011 6 -0.5 217.003 1 217.001 6 1.5 217.004 7 217.007 3 -2.6 217.017 5 217.015 8 1.7
K0+201.000 213.261 4 213.270 4 -9.0 213.259 7 213.259 1 0.6 213.253 0 213.251 1 1.9 213.266 0 213.258 9 7.1
K0+209.000 208.991 3 208.989 0 2.3 208.990 8 208.995 3 -4.5 208.981 7 208.981 0 0.8 208.991 4 208.989 6 1.8
K0+217.000 204.200 6 204.197 5 3.1 204.197 2 204.192 5 4.7 204.192 8 204.183 6 9.2 204.202 8 204.205 3 -2.5
), ArticleFig(id=1217860118369194570, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Table 2, caption=

Comparison of key points in the point cloud on both sides of the downstream arch rib

, figureFileSmall=null, figureFileBig=null, tableContent=
里程桩号/m 下游下侧拱肋下弦杆下侧边线 下游下侧拱肋下弦杆上侧边线 下游上侧拱肋下弦杆下侧边线 下游上侧拱肋下弦杆上侧边线
人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm
K0+24.000 -8.365 2 -8.363 5 -1.7 -7.378 3 -7.379 9 1.6 -6.465 9 -6.463 4 -2.5 -5.510 4 -5.511 6 1.2
K0+34.000 -8.373 5 -8.370 0 -3.5 -7.388 2 -7.390 0 1.8 -6.459 7 -6.458 3 -1.4 -5.498 3 -5.501 3 3.0
K0+44.000 -8.348 2 -8.347 0 -1.3 -7.393 4 -7.395 7 2.3 -6.458 7 -6.455 8 -2.9 -5.508 1 -5.510 3 2.2
K0+54.000 -8.316 3 -8.319 3 3.0 -7.404 5 -7.406 6 2.1 -6.463 6 -6.461 3 -2.3 -5.519 4 -5.521 7 2.3
K0+64.000 -8.323 6 -8.326 2 2.6 -7.414 3 -7.415 9 1.6 -6.469 1 -6.468 0 -1.1 -5.529 9 -5.531 5 1.6
K0+74.000 -8.335 4 -8.336 2 0.7 -7.433 9 -7.439 8 5.9 -6.469 0 -6.470 0 1.0 -5.541 1 -5.537 0 -4.1
K0+84.000 -8.328 5 -8.330 3 1.8 -7.445 9 -7.442 4 -3.5 -6.469 2 -6.470 5 1.3 -5.553 4 -5.549 6 -3.8
K0+94.000 -8.319 1 -8.322 3 3.2 -7.446 0 -7.445 8 -0.2 -6.469 7 -6.471 5 1.8 -5.565 0 -5.563 7 -1.3
K0+104.000 -8.311 3 -8.316 2 4.9 -7.440 9 -7.442 0 1.1 -6.469 0 -6.471 2 2.2 -5.575 9 -5.574 5 -1.4
K0+114.000 -8.303 2 -8.306 4 3.2 -7.436 1 -7.436 8 0.7 -6.464 3 -6.466 6 2.3 -5.566 7 -5.565 6 -1.1
K0+124.000 -8.313 3 -8.315 6 2.3 -7.417 7 -7.416 4 -1.3 -6.448 4 -6.451 2 2.8 -5.554 7 -5.550 0 -4.7
K0+129.000 -8.319 1 -8.322 2 3.1 -7.408 5 -7.405 5 -3.0 -6.447 3 -6.450 3 3.0 -5.548 4 -5.544 1 -4.4
K0+134.000 -8.325 6 -8.327 8 2.2 -7.411 3 -7.405 5 -5.8 -6.453 3 -6.456 5 3.2 -5.543 2 -5.538 0 -5.2
K0+144.000 -8.313 0 -8.315 1 2.1 -7.448 8 -7.442 6 -6.2 -6.464 1 -6.466 1 2.0 -5.540 0 -5.539 9 -0.1
K0+154.000 -8.304 0 -8.307 2 3.2 -7.446 2 -7.444 6 -1.6 -6.475 4 -6.479 0 3.6 -5.546 1 -5.541 4 -4.7
K0+164.000 -8.322 2 -8.323 8 1.6 -7.437 7 -7.443 8 6.1 -6.486 6 -6.488 1 1.5 -5.551 8 -5.546 6 -5.2
K0+174.000 -8.338 4 -8.339 6 1.2 -7.448 1 -7.451 5 3.4 -6.495 2 -6.497 6 2.4 -5.558 6 -5.559 7 1.1
K0+184.000 -8.344 1 -8.345 4 1.3 -7.455 3 -7.457 2 1.9 -6.500 8 -6.499 6 -1.2 -5.563 1 -5.569 9 6.7
K0+194.000 -8.351 7 -8.353 8 2.1 -7.455 0 -7.455 7 0.7 -6.503 9 -6.502 3 -1.6 -5.568 3 -5.572 0 3.7
K0+204.000 -8.361 4 -8.360 6 -0.8 -7.455 3 -7.456 4 1.1 -6.498 0 -6.497 2 -0.8 -5.573 8 -5.571 7 -2.1
K0+214.000 -8.371 5 -8.370 5 -1.0 -7.455 4 -7.457 2 1.8 -6.491 6 -6.489 8 -1.8 -5.566 3 -5.567 3 1.0
K0+224.000 -8.357 3 -8.361 2 3.9 -7.456 9 -7.458 3 1.4 -6.485 6 -6.483 7 -1.9 -5.541 9 -5.544 8 2.9
K0+234.000 -8.335 5 -8.339 6 4.1 -7.484 5 -7.489 5 5.0 -6.474 5 -6.471 9 -2.6 -5.518 8 -5.521 0 2.2
), ArticleFig(id=1217860118541161053, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=表2, caption=

下游拱肋两侧点云关键点对比

, figureFileSmall=null, figureFileBig=null, tableContent=
里程桩号/m 下游下侧拱肋下弦杆下侧边线 下游下侧拱肋下弦杆上侧边线 下游上侧拱肋下弦杆下侧边线 下游上侧拱肋下弦杆上侧边线
人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm
K0+24.000 -8.365 2 -8.363 5 -1.7 -7.378 3 -7.379 9 1.6 -6.465 9 -6.463 4 -2.5 -5.510 4 -5.511 6 1.2
K0+34.000 -8.373 5 -8.370 0 -3.5 -7.388 2 -7.390 0 1.8 -6.459 7 -6.458 3 -1.4 -5.498 3 -5.501 3 3.0
K0+44.000 -8.348 2 -8.347 0 -1.3 -7.393 4 -7.395 7 2.3 -6.458 7 -6.455 8 -2.9 -5.508 1 -5.510 3 2.2
K0+54.000 -8.316 3 -8.319 3 3.0 -7.404 5 -7.406 6 2.1 -6.463 6 -6.461 3 -2.3 -5.519 4 -5.521 7 2.3
K0+64.000 -8.323 6 -8.326 2 2.6 -7.414 3 -7.415 9 1.6 -6.469 1 -6.468 0 -1.1 -5.529 9 -5.531 5 1.6
K0+74.000 -8.335 4 -8.336 2 0.7 -7.433 9 -7.439 8 5.9 -6.469 0 -6.470 0 1.0 -5.541 1 -5.537 0 -4.1
K0+84.000 -8.328 5 -8.330 3 1.8 -7.445 9 -7.442 4 -3.5 -6.469 2 -6.470 5 1.3 -5.553 4 -5.549 6 -3.8
K0+94.000 -8.319 1 -8.322 3 3.2 -7.446 0 -7.445 8 -0.2 -6.469 7 -6.471 5 1.8 -5.565 0 -5.563 7 -1.3
K0+104.000 -8.311 3 -8.316 2 4.9 -7.440 9 -7.442 0 1.1 -6.469 0 -6.471 2 2.2 -5.575 9 -5.574 5 -1.4
K0+114.000 -8.303 2 -8.306 4 3.2 -7.436 1 -7.436 8 0.7 -6.464 3 -6.466 6 2.3 -5.566 7 -5.565 6 -1.1
K0+124.000 -8.313 3 -8.315 6 2.3 -7.417 7 -7.416 4 -1.3 -6.448 4 -6.451 2 2.8 -5.554 7 -5.550 0 -4.7
K0+129.000 -8.319 1 -8.322 2 3.1 -7.408 5 -7.405 5 -3.0 -6.447 3 -6.450 3 3.0 -5.548 4 -5.544 1 -4.4
K0+134.000 -8.325 6 -8.327 8 2.2 -7.411 3 -7.405 5 -5.8 -6.453 3 -6.456 5 3.2 -5.543 2 -5.538 0 -5.2
K0+144.000 -8.313 0 -8.315 1 2.1 -7.448 8 -7.442 6 -6.2 -6.464 1 -6.466 1 2.0 -5.540 0 -5.539 9 -0.1
K0+154.000 -8.304 0 -8.307 2 3.2 -7.446 2 -7.444 6 -1.6 -6.475 4 -6.479 0 3.6 -5.546 1 -5.541 4 -4.7
K0+164.000 -8.322 2 -8.323 8 1.6 -7.437 7 -7.443 8 6.1 -6.486 6 -6.488 1 1.5 -5.551 8 -5.546 6 -5.2
K0+174.000 -8.338 4 -8.339 6 1.2 -7.448 1 -7.451 5 3.4 -6.495 2 -6.497 6 2.4 -5.558 6 -5.559 7 1.1
K0+184.000 -8.344 1 -8.345 4 1.3 -7.455 3 -7.457 2 1.9 -6.500 8 -6.499 6 -1.2 -5.563 1 -5.569 9 6.7
K0+194.000 -8.351 7 -8.353 8 2.1 -7.455 0 -7.455 7 0.7 -6.503 9 -6.502 3 -1.6 -5.568 3 -5.572 0 3.7
K0+204.000 -8.361 4 -8.360 6 -0.8 -7.455 3 -7.456 4 1.1 -6.498 0 -6.497 2 -0.8 -5.573 8 -5.571 7 -2.1
K0+214.000 -8.371 5 -8.370 5 -1.0 -7.455 4 -7.457 2 1.8 -6.491 6 -6.489 8 -1.8 -5.566 3 -5.567 3 1.0
K0+224.000 -8.357 3 -8.361 2 3.9 -7.456 9 -7.458 3 1.4 -6.485 6 -6.483 7 -1.9 -5.541 9 -5.544 8 2.9
K0+234.000 -8.335 5 -8.339 6 4.1 -7.484 5 -7.489 5 5.0 -6.474 5 -6.471 9 -2.6 -5.518 8 -5.521 0 2.2
), ArticleFig(id=1217860118683767402, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=EN, label=Table 3, caption=

Comparison of key points in the point cloud on both sides of the upstream arch rib

, figureFileSmall=null, figureFileBig=null, tableContent=
里程桩号/m 上游下侧拱肋上弦杆下侧边线 上游下侧拱肋上弦杆上侧边线 上游上侧拱肋上弦杆下侧边线 上游上侧拱肋上弦杆上侧边线
人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm
K0+24.000 5.530 9 5.525 8 5.1 6.505 7 6.503 0 2.7 7.384 2 7.386 7 -2.5 8.376 0 8.373 8 2.2
K0+34.000 5.523 6 5.518 8 4.9 6.499 6 6.496 5 3.1 7.392 4 7.394 5 -2.1 8.390 7 8.388 4 2.3
K0+44.000 5.516 9 5.516 0 0.9 6.494 5 6.491 8 2.7 7.399 1 7.401 9 -2.8 8.402 7 8.400 0 2.7
K0+54.000 5.519 3 5.522 4 -3.1 6.503 1 6.495 5 7.6 7.406 4 7.408 6 -2.2 8.402 5 8.401 4 1.1
K0+64.000 5.524 7 5.521 3 3.5 6.504 6 6.502 4 2.2 7.406 4 7.408 8 -2.4 8.402 9 8.401 7 1.2
K0+74.000 5.531 5 5.533 5 -2.0 6.498 8 6.496 8 2.0 7.406 1 7.408 6 -2.5 8.405 7 8.401 9 3.8
K0+84.000 5.535 3 5.536 8 -1.5 6.491 8 6.487 1 4.7 7.406 5 7.409 5 -3.0 8.412 7 8.411 6 1.1
K0+94.000 5.535 1 5.536 2 -1.1 6.488 9 6.487 8 1.1 7.406 3 7.408 2 -1.9 8.419 2 8.417 1 2.1
K0+104.000 5.534 9 5.536 1 -1.2 6.482 4 6.474 4 8.0 7.407 3 7.409 2 -1.9 8.422 9 8.420 4 2.5
K0+114.000 5.535 5 5.537 6 -2.1 6.488 8 6.486 5 2.3 7.412 4 7.415 4 -3.0 8.422 6 8.420 7 1.9
K0+124.000 5.535 5 5.537 4 -1.9 6.501 9 6.498 4 3.5 7.422 0 7.425 2 -3.2 8.421 2 8.418 4 2.8
K0+129.000 5.535 4 5.538 5 -3.1 6.507 2 6.503 4 3.8 7.427 1 7.430 2 -3.1 8.421 5 8.420 1 1.4
K0+134.000 5.535 4 5.538 2 -2.8 6.513 2 6.510 0 3.2 7.431 3 7.433 9 -2.6 8.421 9 8.420 5 1.4
K0+144.000 5.535 2 5.536 4 -1.1 6.526 6 6.523 1 3.5 7.402 5 7.404 6 -2.1 8.421 4 8.419 3 2.1
K0+154.000 5.526 1 5.528 2 -2.1 6.539 1 6.537 0 2.1 7.372 9 7.376 5 -3.6 8.421 2 8.417 3 3.9
K0+164.000 5.514 3 5.515 6 -1.3 6.552 9 6.549 5 3.4 7.364 7 7.369 0 -4.3 8.428 5 8.425 0 3.5
K0+174.000 5.503 8 5.506 0 -2.2 6.566 6 6.562 9 3.7 7.374 3 7.378 2 -3.9 8.435 7 8.432 5 3.2
K0+184.000 5.493 4 5.497 2 -3.8 6.579 9 6.576 9 3.0 7.381 1 7.385 3 -4.2 8.443 8 8.441 1 2.7
K0+194.000 5.487 9 5.493 2 -5.3 6.589 7 6.585 7 4.0 7.372 3 7.376 6 -4.3 8.449 6 8.447 7 1.9
K0+204.000 5.480 1 5.484 9 -4.8 6.591 5 6.589 0 2.5 7.363 4 7.368 2 -4.8 8.456 6 8.453 8 2.8
K0+214.000 5.483 6 5.487 7 -4.1 6.594 8 6.592 6 2.2 7.373 1 7.377 2 -4.1 8.462 9 8.460 4 2.5
K0+224.000 5.496 1 5.499 5 -3.4 6.597 0 6.595 2 1.8 7.407 0 7.411 1 -4.1 8.468 7 8.465 0 3.7
K0+234.000 5.508 3 5.511 8 -3.5 6.599 7 6.598 1 1.6 7.441 7 7.444 5 -2.8 8.474 5 8.472 0 2.5
), ArticleFig(id=1217860118834762366, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1217789889077236518, language=CN, label=表3, caption=

上游拱肋两侧点云关键点对比

, figureFileSmall=null, figureFileBig=null, tableContent=
里程桩号/m 上游下侧拱肋上弦杆下侧边线 上游下侧拱肋上弦杆上侧边线 上游上侧拱肋上弦杆下侧边线 上游上侧拱肋上弦杆上侧边线
人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm 人工提
取值/m
算法提
取值/m
差值/mm
K0+24.000 5.530 9 5.525 8 5.1 6.505 7 6.503 0 2.7 7.384 2 7.386 7 -2.5 8.376 0 8.373 8 2.2
K0+34.000 5.523 6 5.518 8 4.9 6.499 6 6.496 5 3.1 7.392 4 7.394 5 -2.1 8.390 7 8.388 4 2.3
K0+44.000 5.516 9 5.516 0 0.9 6.494 5 6.491 8 2.7 7.399 1 7.401 9 -2.8 8.402 7 8.400 0 2.7
K0+54.000 5.519 3 5.522 4 -3.1 6.503 1 6.495 5 7.6 7.406 4 7.408 6 -2.2 8.402 5 8.401 4 1.1
K0+64.000 5.524 7 5.521 3 3.5 6.504 6 6.502 4 2.2 7.406 4 7.408 8 -2.4 8.402 9 8.401 7 1.2
K0+74.000 5.531 5 5.533 5 -2.0 6.498 8 6.496 8 2.0 7.406 1 7.408 6 -2.5 8.405 7 8.401 9 3.8
K0+84.000 5.535 3 5.536 8 -1.5 6.491 8 6.487 1 4.7 7.406 5 7.409 5 -3.0 8.412 7 8.411 6 1.1
K0+94.000 5.535 1 5.536 2 -1.1 6.488 9 6.487 8 1.1 7.406 3 7.408 2 -1.9 8.419 2 8.417 1 2.1
K0+104.000 5.534 9 5.536 1 -1.2 6.482 4 6.474 4 8.0 7.407 3 7.409 2 -1.9 8.422 9 8.420 4 2.5
K0+114.000 5.535 5 5.537 6 -2.1 6.488 8 6.486 5 2.3 7.412 4 7.415 4 -3.0 8.422 6 8.420 7 1.9
K0+124.000 5.535 5 5.537 4 -1.9 6.501 9 6.498 4 3.5 7.422 0 7.425 2 -3.2 8.421 2 8.418 4 2.8
K0+129.000 5.535 4 5.538 5 -3.1 6.507 2 6.503 4 3.8 7.427 1 7.430 2 -3.1 8.421 5 8.420 1 1.4
K0+134.000 5.535 4 5.538 2 -2.8 6.513 2 6.510 0 3.2 7.431 3 7.433 9 -2.6 8.421 9 8.420 5 1.4
K0+144.000 5.535 2 5.536 4 -1.1 6.526 6 6.523 1 3.5 7.402 5 7.404 6 -2.1 8.421 4 8.419 3 2.1
K0+154.000 5.526 1 5.528 2 -2.1 6.539 1 6.537 0 2.1 7.372 9 7.376 5 -3.6 8.421 2 8.417 3 3.9
K0+164.000 5.514 3 5.515 6 -1.3 6.552 9 6.549 5 3.4 7.364 7 7.369 0 -4.3 8.428 5 8.425 0 3.5
K0+174.000 5.503 8 5.506 0 -2.2 6.566 6 6.562 9 3.7 7.374 3 7.378 2 -3.9 8.435 7 8.432 5 3.2
K0+184.000 5.493 4 5.497 2 -3.8 6.579 9 6.576 9 3.0 7.381 1 7.385 3 -4.2 8.443 8 8.441 1 2.7
K0+194.000 5.487 9 5.493 2 -5.3 6.589 7 6.585 7 4.0 7.372 3 7.376 6 -4.3 8.449 6 8.447 7 1.9
K0+204.000 5.480 1 5.484 9 -4.8 6.591 5 6.589 0 2.5 7.363 4 7.368 2 -4.8 8.456 6 8.453 8 2.8
K0+214.000 5.483 6 5.487 7 -4.1 6.594 8 6.592 6 2.2 7.373 1 7.377 2 -4.1 8.462 9 8.460 4 2.5
K0+224.000 5.496 1 5.499 5 -3.4 6.597 0 6.595 2 1.8 7.407 0 7.411 1 -4.1 8.468 7 8.465 0 3.7
K0+234.000 5.508 3 5.511 8 -3.5 6.599 7 6.598 1 1.6 7.441 7 7.444 5 -2.8 8.474 5 8.472 0 2.5
)], attaches=null, journal=Journal(id=1146119176004939786, delFlag=0, nameCn=科学技术与工程, nameEn=Science Technology and Engineering, nameHistory1=null, nameHistory2=null, issn=1671-1815, eissn=, cn=11-4688/T, coden=null, periodic=4, language=CN, oaType=是, ccby=null, superviseOffice=null, ownerOffice=null, pubOffice=null, editorOffice=null, officeType=null, aims=null, clcCode=null, officeProv=null, officeCity=null, officeAddr=null, officeZip=null, officeEmail=null, officePhone=null, editDirector=null, officeDirector=null, officeDirectorPhone=null, officeStaffNum=null, officeEmpNum=null, coverPicUrl=UKU/O7GSka5polgCTkbIIw==, journalPrice=null, startedYear=null, abbrevIsoEn=Sci Technol Eng, journalRemark=null, publicationField=null, createdTime=null, updatedTime=1754445529766, createdBy=null, updatedBy=13701087609, firstLetterCn=S, firstLetterEn=S, subjectCode=Natural Sciences, subjectName=自然科学, subjectCodeEn=Natural Sciences, subjectNameEn=null, picCn=UKU/O7GSka5polgCTkbIIw==, picEn=5hwlULoNwcbj3xUmVi9MAQ==, jcr=null, cjcr=null, exts=[JournalExt(id=1159791870395564357, language=CN, name=科学技术与工程, nameHistory1=null, nameHistory2=null, managedBy=, sponsoredBy=, publishedBy=, editorOffice=, officeProv=null, officeCity=null, officeAddr=, officeZip=, editDirector=null, officeDirector=null, officePhone=null, coverPicUrl=null, journalRemark=, submitArticleUrl=null, websiteUrl=http://www.stae.com.cn/jsygc/home, createdTime=1754445529793, updatedTime=1754445529793, createdBy=13701087609, updatedBy=13701087609, submissionGuidelinesUrl=http://www.stae.com.cn/jsygc/site/menus/20090429150146001, submissionAuthorUrl=http://www.stae.com.cn/jsygc/author/login, submissionEditorUrl=http://www.stae.com.cn/jsygc/editor/login, submissionReviewUrl=http://www.stae.com.cn/jsygc/reviewer/login, submissionCeEditorUrl=, submissionAeEditorUrl=, option={"copyright":""}), JournalExt(id=1159791870441701702, language=EN, name=Science Technology and Engineering, nameHistory1=null, nameHistory2=null, managedBy=, sponsoredBy=, publishedBy=, editorOffice=, officeProv=null, officeCity=null, officeAddr=, officeZip=, editDirector=null, officeDirector=null, officePhone=null, coverPicUrl=null, journalRemark=, submitArticleUrl=null, websiteUrl=http://www.stae.com.cn/jsygc/home, createdTime=1754445529804, updatedTime=1754445529804, createdBy=13701087609, updatedBy=13701087609, submissionGuidelinesUrl=, submissionAuthorUrl=http://www.stae.com.cn/jsygc/author/login, submissionEditorUrl=http://www.stae.com.cn/jsygc/editor/login, submissionReviewUrl=http://www.stae.com.cn/jsygc/reviewer/login, submissionCeEditorUrl=, submissionAeEditorUrl=, option={"copyright":""})], databaseList=null, tenantJournalId=1146123166801305609, websiteList=[Website(id=1148243202391400884, webName=null, webTitle=null, webDomain=null, webCopyrigh=null, webIpcNo=null, seoTitle=null, seoKeywords=null, seoDescription=null, tenantJournalId=null, journalId=1146123166801305609, journalNameCn=null, journalNameEn=null, grayFlag=null, tenantId=1146029695717560320, platformId=null, journalGroupId=null, journalGroupNameCn=null, journalGroupNameEn=null, type=1, domain=https://castjournals.cast.org.cn/joweb/kxjsygc/CN, language=CN, createTime=1751692112777, createBy=18614031015, updateTime=1753520965431, updateBy=18614031015, name=科学技术与工程-中文站点, tplId=1146099689490845704, title=科学技术与工程, delFlag=0, indexPage=/home, props=[WebsiteProps(id=1148622798802673703, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1148243202391400884, code=articleTextType, value=kx, createTime=1751782615614, updateTime=1751782615614, creator=18614031015, updator=18614031015), WebsiteProps(id=1148622798781702180, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1148243202391400884, code=banner, value=null, createTime=1751782615609, updateTime=1751782615609, creator=18614031015, updator=18614031015), WebsiteProps(id=1148622798769119267, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1148243202391400884, code=logo, value=https://castjournals.cast.org.cn/joweb/kjdb/CN/file/pic?fileId=j86gbwi+p0Idkyl5SzIlmQ==, createTime=1751782615606, updateTime=1751782615606, creator=18614031015, updator=18614031015), WebsiteProps(id=1148622798794285094, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1148243202391400884, code=picServerUrl, value=https://castjournals.cast.org.cn/joweb/kjdb/CN/file/pic, createTime=1751782615612, updateTime=1751782615612, creator=18614031015, updator=18614031015), WebsiteProps(id=1148622798790090789, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1148243202391400884, code=staticResourcePath, value=https://castjournals.cast.org.cn/joweb/cast_kjdb_cn_619/, createTime=1751782615611, updateTime=1751782615611, creator=18614031015, updator=18614031015)]), Website(id=1155914124811976731, webName=null, webTitle=null, webDomain=null, webCopyrigh=null, webIpcNo=null, seoTitle=null, seoKeywords=null, seoDescription=null, tenantJournalId=null, journalId=1146123166801305609, journalNameCn=null, journalNameEn=null, grayFlag=null, tenantId=1146029695717560320, platformId=null, journalGroupId=null, journalGroupNameCn=null, journalGroupNameEn=null, type=1, domain=https://castjournals.cast.org.cn/joweb/kxjsygc/EN, language=EN, createTime=1753521003206, createBy=18614031015, updateTime=1753521003206, updateBy=18614031015, name=科学技术与工程-英文站点, tplId=1146101810881728533, title=Science Technology and Engineering, delFlag=0, indexPage=/home, props=[WebsiteProps(id=1155914371227308235, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1155914124811976731, code=articleTextType, value=kx, createTime=1753521061952, updateTime=1753521061952, creator=18614031015, updator=18614031015), WebsiteProps(id=1155914371210531016, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1155914124811976731, code=banner, value=null, createTime=1753521061947, updateTime=1753521061947, creator=18614031015, updator=18614031015), WebsiteProps(id=1155914371202142407, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1155914124811976731, code=logo, value=https://castjournals.cast.org.cn/joweb/kjdb/CN/file/pic?fileId=j86gbwi+p0Idkyl5SzIlmQ==, createTime=1753521061945, updateTime=1753521061945, creator=18614031015, updator=18614031015), WebsiteProps(id=1155914371223113930, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1155914124811976731, code=picServerUrl, value=https://castjournals.cast.org.cn/joweb/kjdb/CN/file/pic, createTime=1753521061950, updateTime=1753521061950, creator=18614031015, updator=18614031015), WebsiteProps(id=1155914371218919625, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1155914124811976731, code=staticResourcePath, value=https://castjournals.cast.org.cn/joweb/cast_kjdb_cn_619/, createTime=1753521061949, updateTime=1753521061949, creator=18614031015, updator=18614031015)])], journalTitle=科学技术与工程, weixinUrl=null, journalUrl=null, iacademicId=null, status=0, seqNo=null, journalTitleEn=Science Technology and Engineering, journalPhotoCn=UKU/O7GSka5polgCTkbIIw==, journalPhotoEn=5hwlULoNwcbj3xUmVi9MAQ==, journalFirstLetter=S, journalRecommend=null, journalNew=null, journalCollection=null, jcrJf=null, cjcrJf=null, jcrJfStr=null, cjcrJfStr=null, submissionFirstDecision=null, sciSubjectClassification=null, casSubjectClassification=null, citeScore=null, totalCitationFrequency=null, icpCode=null, psCode=null, advertisingLicenseCode=null, copyrightInformation=null, country=null, option=null, provinceCode=null, provinceName=null, collectFlag=false), detailUrlCn=https://castjournals.cast.org.cn/joweb/kxjsygc/CN/10.12404/j.issn.1671-1815.2406516, detailUrlEn=https://castjournals.cast.org.cn/joweb/kxjsygc/EN/10.12404/j.issn.1671-1815.2406516, pdfUrlCn=https://castjournals.cast.org.cn/joweb/kxjsygc/CN/PDF/10.12404/j.issn.1671-1815.2406516, pdfUrlEn=https://castjournals.cast.org.cn/joweb/kxjsygc/EN/PDF/10.12404/j.issn.1671-1815.2406516, aliStartDate=null, aliEndDate=null, collectionFlag=false, citedCount=null, citedUrl=null, reference=null)
收藏切换
融合RANSAC的改进DBSCAN算法提取钢拱桥拱肋线形
收藏切换
PDF下载
潘国兵 , 赵雪妍 * , 吴畏 , 金晓东 , 虞洪兵
科学技术与工程 | 论文·交通运输 2025,25(21): 9148-9157
收起
收藏切换
科学技术与工程 | 论文·交通运输 2025, 25(21): 9148-9157
融合RANSAC的改进DBSCAN算法提取钢拱桥拱肋线形
全屏
潘国兵 , 赵雪妍* , 吴畏, 金晓东, 虞洪兵
作者信息
  • 重庆交通大学智慧城市学院, 重庆 400074
  • 潘国兵(1976—),男,汉族,四川眉山人,博士,教授。研究方向:智能测绘。E-mail:

通讯作者:

* 赵雪妍(2000—),女,汉族,内蒙古包头人,硕士研究生。研究方向:点云处理。E-mail:
Extracting Arch Rib Shape of Steel Arch Bridges Using Improved DBSCAN Algorithm Integrated with RANSAC
Guo-bing PAN , Xue-yan ZHAO* , Wei WU, Xiao-dong JIN, Hong-bing YU
Affiliations
  • Smart Cities Institute, Chongqing Jiaotong University, Chongqing 400074, China
出版时间: 2025-07-28 doi: 10.12404/j.issn.1671-1815.2406516
文章导航
收藏切换

钢拱桥的线形监测是桥梁健康监测系统的重要组成部分。运用三维激光扫描技术,融合随机抽样一致(random sample consensus,RANSAC)算法对传统的具有噪声的基于密度的聚类方法(density-based spatial clustering of applications with noise,DBSCAN)算法进行改进,对钢拱桥拱肋线形进行提取。三维激光点云数据具有全面性和细节体现的优势,能够完整地呈现桥梁结构的形状和变形信息,融合RANSAC的改进DBSCAN算法根据钢拱桥结构特征对聚类结果进行约束,能够很好地实现删除离散点及桥面、横撑、横联和腹杆部分的点云这一目的。根据融合RANSAC的改进DBSCAN算法提取出的点云进行关键点拟合,与人工提取结果进行对比,拱肋关键点提取误差均在毫米级,最大误差为9.2 mm,最小误差为0.1 mm,此提取方法能够更加准确有效地完成钢拱桥线形提取,使线形提取精度达到毫米级,大大降低了人力成本和时间成本,对钢拱桥的复杂结构有更好的鲁棒性,能很好地适应实际生产需求。

三维激光  /  线形监测  /  RANSAC算法  /  改进DBSCAN算法

The alignment monitoring of steel arch bridges constitutes an essential component of bridge health monitoring systems. Three-dimensional laser scanning technology was utilized, and the traditional density-based spatial clustering of applications with noise(DBSCAN) algorithm was improved by integrating the random sample consensus(RANSAC) algorithm to extract the alignment of steel arch bridge ribs. Three-dimensional laser point cloud data, characterized by its comprehensiveness and detailed representation, is capable of fully presenting the structural shape and deformation information of the bridge. The RANSAC-integrated improved DBSCAN algorithm, constrained by the structural features of the steel arch bridge, effectively achieves the removal of discrete points as well as point clouds from the bridge deck, cross bracing, lateral connections, and web members. Point clouds extracted using the RANSAC-integrated improved DBSCAN algorithm are fitted to identify key points, and a comparison is made with results obtained manually. The extraction errors for the key points of the arch ribs are all within the millimeter range, with the maximum error being 9.2 mm and the minimum error being 0.1 mm. This extraction method is demonstrated to more accurately and effectively accomplish the alignment extraction of steel arch bridges, achieving millimeter-level precision in alignment extraction. It significantly reduces labor and time costs, provides better robustness for the complex structures of steel arch bridges, and adapts well to practical production demands.

3D laser  /  line shape monitoring  /  RANSAC algorithm  /  improved DBSCAN algorithm
潘国兵, 赵雪妍, 吴畏, 金晓东, 虞洪兵. 融合RANSAC的改进DBSCAN算法提取钢拱桥拱肋线形. 科学技术与工程, 2025 , 25 (21) : 9148 -9157 . DOI: 10.12404/j.issn.1671-1815.2406516
Guo-bing PAN, Xue-yan ZHAO, Wei WU, Xiao-dong JIN, Hong-bing YU. Extracting Arch Rib Shape of Steel Arch Bridges Using Improved DBSCAN Algorithm Integrated with RANSAC[J]. Science Technology and Engineering, 2025 , 25 (21) : 9148 -9157 . DOI: 10.12404/j.issn.1671-1815.2406516
钢拱桥作为一种结构形式,在桥梁建设中有着悠久的发展历史和广泛的应用。最早的钢拱桥出现于19世纪中期,这些桥梁以其优越的力学性能和较长的使用寿命迅速得到了广泛应用。20世纪中期,钢拱桥技术逐渐成熟,特别是在长跨度桥梁和复杂地形条件下的应用得到了极大的发展,大量现代化的钢拱桥在全球各地建成。21世纪,随着材料科学和施工技术的进步,钢拱桥在设计、施工和维护方面实现了更加优化和高效。
钢拱桥的线形监测是桥梁健康监测系统的重要组成部分,它可以帮助评估桥梁的结构状态、识别潜在的结构问题,提供灾害预警信息,指导桥梁的维修和保养工作,提供分析负载效应的变形数据,确保桥梁的安全性和耐久性。
目前钢拱桥线形监测常用的方法是全站仪测量结合北斗卫星导航系统(Beidou navigation system,BDS)监测,对桥梁的关键节点进行高精度的三维坐标测量,从而监测桥梁的线形变化,但全站仪测量人工依赖性强、视线要求高、覆盖范围有限。除此之外,还有摄影测量和激光扫描两种手段。摄影测量利用高分辨率相机从不同角度拍摄桥梁照片,重建桥梁的三维模型,从而监测桥梁的线形变化,适用于短期监测和复杂环境下的监测,但精度受摄影设备和处理技术的限制,数据处理复杂。
激光扫描技术可以快速获取桥梁表面的高密度点云数据,通过分析点云数据来监测桥梁的线形变化,适用于大范围和高精度的线形监测。同时三维激光点云数据具有全面性和细节体现的优势,能够完整地呈现桥梁结构的形状和变形信息,包括位移、形变、沉降等参数。
王浩琛等[1]针对公路曲线桥梁检测问题,提出了一种基于三维激光扫描技术的三维形貌与变形提取方法。冯麟等[2]利用三维激光扫描点云数据实时获取桥面形变值,实现了桥面所有点的形变测量,证明了三维激光点云在桥梁监测中的应用价值。安路明等[3]采用三维激光扫描技术开展了钢桁架拱桥架设线形的监测研究,证明了基于三维激光扫描的线形监测方法可满足工程应用要求;彭仪普等[4]提出点云等距切片与点云平面切片算法提取桥梁线形,但在完整桥梁线形提取方面仍有不足;白祖应等[5]提出拱桥自动化高精度线形监测算法,但是面对结构更为复杂的钢拱桥,在处理复杂结构和多样性方面存在一定的局限性。因此对于桥梁线形的提取,仍需要进一步的研究和改进。
现以具有复杂结构的大跨度钢拱桥为研究对象,针对钢拱结构复杂、干扰物众多的问题,使用三维激光扫描技术对钢拱桥进行扫描,利用钢拱桥的结构特点,使用PCA算法对钢拱桥关键结构点云进行初步提取,针对初步提取结果中桥面、横撑、横联和腹杆等干扰线形提取结果的结构,提出融合随机抽样一致(random sample consensus,RANSAC)的改进具有噪声的基于密度的聚类方法(density-based spatial clustering of applications with noise,DBSCAN)算法,来实现对钢拱桥拱肋的精确提取。
RANSAC算法[6-7]是一种用于估计数学模型参数的迭代算法,尤其适用于存在大量噪声数据的情况。它是一种鲁棒的参数估计算法,能够从一组包含大量异常值的数据中估计出模型参数。其基本原理如下。
给定一个包含N个点的二维数据集 { ( x i , y i ) } i = 1 n,从数据集中随机选择两个点,因为直线拟合最小的数据子集为两个点,这两个点定义一条候选直线。对于这两个随机选择的点(x1,y1)、(x2,y2),直线方程可以写成
y=mx+c
式(1)中:斜率m和截距c可以通过式(2)、式(3)计算。
$m=\frac{y_{2}-y_{1}}{x_{2}-x_{1}}$
$c=y_{1}-m x_{1}$
对每一个数据点(xi,yi)进行内点评估,计算其到拟合直线的垂直距离为
$d_{i}=\frac{\left|m x_{i}-y_{i}+c\right|}{\sqrt{m^{2}+1}}$
设定一个阈值dth,如果didth,则该点被认为是内点。然后对拟合模型进行评估,计算内点数量,如果内点数量超过预设的阈值,则认为找到了一条合理的直线。保留内点数量最多的直线模型。重复上述步骤k次,选择内点最多的直线模型作为最终的拟合结果。
DBSCAN算法[8-10]是一种基于密度的聚类算法,特别适用于发现具有任意形状的簇以及处理噪声数据。DBSCAN的基本思想是通过密度来定义簇,即在一个簇内,点的密度要大于某个阈值。其基本原理及算法流程如下。
给定一个点p和半径ε,p的∈-邻域包含所有与p的距离不超过ε的点。
Nε(p)={qD|dist(p,q)≤ε}
$\operatorname{dist}(p, q)=\sqrt{\sum_{i=1}^{n}\left(p_{i}-q_{i}\right)^{2}}$
式中:D为数据集;dist(p,q)为点p和点q之间的距离。
如果一个点p的∈-邻域包含至少MinPts个点(包括p自身),则称p为核心点;如果一个点p本身不是核心点,但它落在某个核心点的∈-邻域内,则称p为边界点;如果一个点p既不是核心点,也不是边界点,则称p为噪声点。核心点判定式为
Nε(p)>MinPts
选择任意一个未访问的点,如果该点是核心点,则以该点为起始点,基于∈-邻域找到所有直接密度可达的点,形成一个簇;如果该点是边界点或噪声点,则将其标记为噪声。从核心点开始,递归地将所有直接密度可达的点添加到当前簇中。如果点p是核心点,并且点qp的∈-邻域内,则qp直接密度可达。重复上述步骤,直到所有点都被访问。
提出融合RANSAC的改进DBSCAN算法的步骤如下。
步骤1 拟合并分割第一条直线。使用RANSAC在当前点云中拟合直线,找到直线的内点(即符合直线模型的点),提取这些内点并将它们从原点云中移除。直线表示为
L(A,B):y=A+Bx
步骤2 继续拟合剩余点云。在剩余的点云上再次应用RANSAC,拟合下一条直线。
步骤3 通过上述步骤重复这一过程。直到剩余点云中的点数少于min_num。
步骤4 判断点所属的直线。在每次RANSAC拟合中。通过计算每个点到拟合直线的距离来判断其是否属于当前直线(即是否为内点)。只有内点会被分割出来并从原点云中移除。
步骤5 将每条直线表示为一组特征向量。
$\boldsymbol{F}=\left(\begin{array}{cc} A_{1} & B_{1} \\ A_{2} & B_{2} \\ \vdots & \vdots \\ A_{n} & B_{n} \end{array}\right)$
步骤6 使用DBSCAN算法对这些直线的特征向量进行聚类,在给定的∈-邻域内,如果某个直线的邻居数大于或等于MinPts,则这条直线被认为是核心直线,核心直线以及其密度可达的直线构成一个簇。
步骤7 继续对剩余直线进行聚类,直到所有直线都完成分类。
提出的融合RANSAC的改进DBSCAN算法有两个优势。
(1)结构化分割:RANSAC能有效地拟合和分割出具有特定几何形状的点,从而在初步分割时提供结构上的约束。
(2)减少噪声影响:RANSAC通过迭代和模型验证,可以有效地减少对噪声点的敏感性。
融合RANSAC的改进DBSCAN算法与DBSCAN算法聚类对比效果如图1所示。
对于结构复杂但存在明显的直线结构的钢拱桥,RANSAC算法能够有效地提取这些结构,并对DBSCAN算法的聚类结果进行约束,通过DBSCAN对提取的直线进行进一步的分割和聚类,最终的聚类结果能够有效分离离散点及桥面、横撑、横联和腹杆部分,准确提取出钢拱桥拱肋关键部分的点云。
采用RiGel架站式扫描仪,对重庆市正在施工阶段的钢拱桥进行三维激光扫描,扫描时在桥两岸分别架设两站,桥上架设三站,原始点云数据合计178 946 265个点,如图2所示。
由于原始点云数据量过大且存在少量噪声点,因此首先对原始点云数据进行下采样和去噪。
使用Octree算法对点云进行下采样,Octree算法是一种常用于点云数据处理和分析的算法,主要用于减少点云数据的密度和复杂性,同时保持点云数据的主要特征和结构。Octree算法通过将三维空间递归地划分为8个子立方体(Octants)来有效地分割和组织点云数据。由于Octree是基于空间划分的,它能够保留点云数据的主要结构特征。这是因为每个Octant的重心都是由该区域内的所有点计算得出的,这样可以更好地保留原始数据的分布和结构特征。算法步骤如下。
构建Octree,将整个点云数据空间递归地划分为8个子立方体,直到每个子立方体内的点云数量小于预定的阈值或达到最大深度。
对于每个非空的Octant,计算其内部所有点的重心作为代表点。
$C=\frac{1}{n} \sum_{i=1}^{n} p_{i}$
式(10)中:pi为Octant内的点;n为Octant内点的数量。
进行下采样,将每个Octant的重心作为代表点,构建新的下采样后的点云。
考虑到桥梁的结构特性,选择对桥梁复杂结构细节适应性更好的双边滤波对下采样后的点云数据进行去噪。双边滤波是一种在点云处理中的非线性滤波技术,能够处理非线性变化的点云数据,对非线性噪声有更好的鲁棒性,由于它同时考虑了空间域和强度域的距离,滤波后的点云边缘和细节得以更好的保留,如图3所示。
使用PCA算法对点云法线进行估算,并对法向量进行方向一致化处理,PCA算法[11-12]视每个点的局部点云为一个光滑的曲面,然后用最小二乘算法在邻域内估计出一个最佳切平面,最后令此最佳切平面的法向量为采样点的法向量,算法步骤如下。
步骤1 对于包含n个三维点的点云数据集,其中每个点由3个坐标值(xi,yi,zi)表示,那么这些点可以表示为一个3×n的矩阵X,其列向量为点云中的所有点。首先计算点云数据集的均值向量 X ¯
$\overline{\boldsymbol{X}}=\frac{1}{n} \sum_{i=1}^{n} \boldsymbol{X}_{i}$
式(11)中:Xi为点云中的第i个点。
步骤2 将点云数据集去中心化。
$X^{\prime}=X-\bar{X} $
步骤3 计算协方差矩阵C
$\boldsymbol{C}=\frac{1}{n} \boldsymbol{X}^{\prime}\left(\boldsymbol{X}^{\prime}\right)^{\mathrm{T}}$
协方差矩阵C是一个对称矩阵,其特征向量即为法线方向。对于点云中的每个点,PCA算法通过求解协方差矩阵的特征值和特征向量来计算法线。特征向量对应的特征值表示了该方向上的方差,因此,对应于最大特征值的特征向量即为点云数据的主方向,即法线方向。求解协方差矩阵的特征值和特征向量。
Cv=λv
式(14)中:v为特征向量;λ为特征值。
选择对应于最大特征值的特征向量作为法线方向。通过上述步骤,PCA算法可以计算出点云数据集中每个点的法线方向。设置邻域点n=5,并对法向量进行方向一致化处理。
根据对点云数据的分析结果设定阈值,Z方向上的阈值设为0.85~1.000,X方向上的阈值设为-1.00~-0.99、0.99~1.00,分别提取出拱肋底部及两侧的点云,处理后点云数据为865 105个,如图4所示。
在前述数据预处理中,已基于点云的法线特征实现了对拱肋底部和两侧点云的初步提取,但是,由于钢拱结构复杂,提取结果中还存在很多离散点、拱桥桥面、横撑、横联和腹杆上的点以及挡墙上的点,这些点都会对拱桥线形提取造成影响。为了去除这些点对提取结果的干扰,使用RANSAC算法进行直线拟合分类并去除部分离散点,对拟合的直线进行DBSCAN聚类以去除拱桥桥面、横撑、横联和腹杆上的点以及挡墙上的点,并与直接对点云进行DBSCAN聚类的结果进行对比。融合RANSAC的DBSCAN算法不同参数的实验结果如图5图6所示。
选择融合RANSAC的DBSCAN算法的最佳提取结果,与直接对点云进行DBSCAN聚类的结果进行对比,结果如图7所示。
选择融合RANSAC的DBSCAN算法的最佳提取结果,与直接对点云进行DBSCAN聚类的结果进行对比,结果如图8所示。根据聚类结果对桥梁部分点云进行筛选,删除各类干扰点云,仅保留拱肋处点云,筛选结果如图9所示。
对筛选后的点云进行关键点坐标提取拟合,将算法提取结果与人工提取结果进行对比,根据对比结果,提出的方法提取精度能够达到毫米级,可以满足实际生产需求,详细对比结果如表1~表3所示。
根据图5图6,在不同参数的结果中,对于拱肋两侧的点云,RANSAC直线拟合最小拟合点数为25时,有大量有用数据点被滤除,RANSAC直线拟合最小拟合点数为15、20时,提取效果更好,都能够将拱肋部分点云分离出来;对于拱肋底部的点云,RANSAC直线拟合最小拟合点数为25时,几乎所有点云都被滤除掉,RANSAC直线拟合最小拟合点数为15、20时,提取效果更好,其中,RANSAC直线拟合最小拟合点数为20时,除横撑外,横联和腹杆也能够被滤除,效果最好。
根据图7~图9,对比DBSCAN聚类与融合RANSAC的改进DBSCAN算法的提取结果,融合RANSAC的改进DBSCAN算法有效解决了传统的DBSCAN聚类无法实现基于拱桥结构进行提取的问题。融合RANSAC的改进DBSCAN算法能够实现拱肋部分点云的提取,以及对于拱肋两侧的离散点、拱肋之间的连接构件的分割,并且能够更好地保留有效数据点,避免有用数据被错误滤除,很好地实现删除离散点及桥面、横杆部分的点云这一目的。
根据表1~表3图10,根据融合RANSAC的改进DBSCAN算法提取出的点云进行关键点拟合,与人工提取结果进行对比,拱肋底部及两侧点云关键点误差均在毫米级,其中拱肋底部提取点云最大差值为9.2 mm,最小差值为0.1 mm,差值绝对值在5.0 mm以下的占比为92.05%,在2.0 mm以下的占比为82.95%;拱肋两侧提取点云最大差值为8.0 mm,最小差值为0.1 mm,差值绝对值在5.0 mm以下的占比为96.74%,在2.0 mm以下的占比为56.82%。
受到原始点云数据质量的影响,由于拱肋两侧数据质量更高更完整,提取得到的关键点精度也更高,拱肋底部受到遮挡更多,数据质量及完整度略差于拱肋两侧数据,因此提取得到的关键点精度也略低于拱肋两侧。
根据最终的精度评价,本文方法能够精确快速地实现克服钢拱桥复杂结构影响的拱肋线形自动化提取,提取精度能够满足实际生产需求。
(1)以钢拱桥为对象,运用三维激光扫描技术及PCA算法得到点云法向数据,基于钢拱桥的法线特征及结构特征,根据钢拱桥不同位置法线不同的垂直特性对点云进行提取,得到了拱肋关键部位的特征点云。基于提取数据对拱肋关键点坐标进行提取,对比了传统的DBSCAN算法与融合RANSAC的改进DBSCAN算法的提取效果,DBSCAN聚类无法实现拱肋部分点云的提取;融合RANSAC的改进DBSCAN算法能够很好地实现删除离散点及桥面、横杆部分的点云这一目的。根据融合RANSAC的改进DBSCAN算法提取出的点云进行关键点拟合,与人工提取结果进行对比,拱肋底部及两侧点云关键点误差均在毫米级,提取点云最大差值为9.2 mm,最小差值为0.1 mm;拱肋两侧提取点云最大差值为8.0 mm,最小差值为0.1 mm。
(2)相比于传统的监测手段,三维激光点云数据具有全面性和细节体现的优势,能够完整地呈现桥梁结构的形状和变形信息。而本文方法能够更加准确有效地完成钢拱桥线形提取。凭借钢拱桥拱肋本身的法线特征和结构特征,利用三维激光点云,使线形提取精度达到毫米级,大大降低了人力成本和时间成本,流程更为简便,结果更加准确,对钢拱桥的复杂结构有更好的鲁棒性,能很好地适应实际生产需求。
(3)本文算法仍有一些不足之处,虽然在对拱桥的结构特征的适应性上有显著提升,但相比传统算法处理时间更长,同时,对于点云密度过低的数据处理效果也会大打折扣,下一步工作将针对这两个问题进行改进。
  • 国家自然科学基金(42074004)
参考文献 引证文献
排序方式:
[1]
王浩琛, 冯东明, 吴刚, 等. 基于三维激光点云的公路曲线桥梁三维形貌与变形测量[J]. 东南大学学报(自然科学版), 2023, 53(5): 756-764.
Wang Haochen, Feng Dongming, Wu Gang, et al. Three-dimensional topography and deformation measurement of highway curved bridges based on 3D laser point clouds[J]. Journal of Southeast University (Natural Science Edition), 2023, 53(5): 756-764.
[2]
冯麟, 周志祥, 唐亮, 等. 基于三维扫描点云数据的模型桥形变获取[J]. 实验室研究与探索, 2021, 40(8): 5-8, 18.
Feng Lin, Zhou Zhixiang, Tang Liang, et al. Deformation acquisition of model bridges based on 3D scanning point cloud data[J]. Laboratory Research and Exploration, 2021, 40(8): 5-8, 18.
[3]
安路明, 赵健, 任延龙, 等. 大跨度钢桁拱桥三维激光扫描线形监控技术研究[J]. 钢结构(中英文), 2022, 37(12): 18-23.
An Luming, Zhao Jian, Ren Yanlong, et al. Research on 3D laser scanning shape monitoring technology for large-span steel truss arch bridges[J]. Steel Construction (Chinese-English), 2022, 37(12): 18-23.
[4]
彭仪普, 李剑, 韩衍群, 等. 基于点云切片算法的铁路钢桁拱桥线形分析[J]. 华南理工大学学报(自然科学版), 2024, 52(7): 97-106.
Peng Yipu, Li Jian, Han Yanqun, et al. Railway steel truss arch bridge shape analysis based on point cloud slicing algorithm[J]. Journal of South China University of Technology (Natural Science Edition), 2024, 52(7): 97-106.
[5]
白祖应, 卯申殷, 胡开心, 等. 基于点云的劲性骨架拱桥自动化线形监测方法[J]. 科学技术与工程, 2022, 22(36): 16211-16218.
Bai Zuying, Mao Shenyin, Hu Kaixin, et al. Automated shape monitoring method for stiffened skeleton arch bridges based on point cloud[J]. Science Technology and Engineering, 2022, 22(36): 16211-16218.
[6]
Kaiser A, Ybanez Z J A, Boubekeur T. A survey of simple geometric primitives detection methods for captured 3D data[J]. Computer Graphics Forum, 2019, 38(1): 167-196.
[7]
Schnabel R, Wahl R, Klein R. Efficient RANSAC for point-cloud shape detection[J]. Computer Graphics Forum, 2007, 26(2): 214-226.
[8]
朱旺煌, 刘荣, 龚循强, 等. 基于倾斜摄影测量点云的建筑物单体提取[J]. 科学技术与工程, 2024, 24(30): 12843-12852.
Zhu Wanghuang, Liu Rong, Gong Xunqiang, et al. Extraction of individual buildings based on point clouds from oblique photogrammetry[J]. Science Technology and Engineering, 2024, 24 (30): 12843-12852.
[9]
申燚, 赵泽钰, 袁明新, 等. 基于四叉树扇形层值聚类的无人船障碍物检测[J]. 科学技术与工程, 2024, 24 (13): 5427-5435.
Shen Yi, Zhao Zeyu, Yuan Mingxin, et al. Obstacle detection for unmanned ship based on quadtree sector layer value clustering[J]. Science Technology and Engineering, 2024, 24 (13): 5427-5435.
[10]
Deng D. DBSCAN clustering algorithm based on density[C]// Proceedings of the 2020 7th International Forum on Electrical Engineering and Automation (IFEEA). Hefei: IEEE, 2020: 949-953.
[11]
Ssanchez J, Denis F, Coeurjolly D, et al. Robust normal vector estimation in 3D point clouds through iterative principal component analysis[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163: 18-35.
[12]
Zhang H, Xu C, Xiao D. Offline correction of tool path deviations for robot-assisted ultrasonic nondestructive testing[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2019, 233(8): 2879-2893.
2025年第25卷第21期
PDF下载
136
55
引用本文
BibTeX
文章信息
doi: 10.12404/j.issn.1671-1815.2406516
  • 接收时间:2024-08-30
  • 首发时间:2026-01-13
  • 出版时间:2025-07-28
补充材料
相关文章
文章信息
作者
出版历史
  • 收稿日期:2024-08-30
  • 修回日期:2025-04-15
基金
国家自然科学基金(42074004)
作者信息
    重庆交通大学智慧城市学院, 重庆 400074

通讯作者:

* 赵雪妍(2000—),女,汉族,内蒙古包头人,硕士研究生。研究方向:点云处理。E-mail:
参考文献
分享链接
https://castjournals.cast.org.cn/joweb/kxjsygc/CN/10.12404/j.issn.1671-1815.2406516
分享至
全文二维码

扫描看全文

引用本文
BibTeX
本文的引用情况
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
关闭全屏