|Table of Contents|

Digital pre-assembly method for steel box segments based on point cloud data(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2024年6期
Page:
47-58
Research Field:
桥梁与隧道工程
Publishing date:

Info

Title:
Digital pre-assembly method for steel box segments based on point cloud data
Author(s):
ZHU Jin-song1; 2; 3;  WANG Duo-wu2;  YANG Rui-peng2
(1. State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University,Tianjin 300350, China; 2. School of Civil Engineering, Tianjin University, Tianjin 300350, China; 3. Key Laboratory of Coast Civil Structure Safety of Ministry of Education,Tianjin University, Tianjin 300350, China)
Keywords:
bridge engineering;  intelligent construction;  digital pre-assembly;  laser point cloud;  quality inspection
PACS:
U445.4
DOI:
10.19721/j.cnki.1671-8879.2024.06.005
Abstract:
In order to complete manufacturing error inspection and pre-assembly swiftly for prefabricated bridge steel box segments, a novel digital pre-assembly method utilizing 3D laser scanning point cloud models was introduced. At first, 3D laser scanning measurement technology was used to create the steel box segments' foot-scale point cloud models, which were then improved using pre-processing techniques to reduce noise and maintain integrity. Subsequently, a rapid boundary extraction algorithm and a planar point cloud projection algorithm was developed, enabling automatic boundary delineation of the 3D foot-scale point cloud models and facilitating dimension reduction of preassembled cross-sectional data. Additionally, an automatic feature corner point extraction algorithm for boundary point clouds was presented, as well as a method for aligning preassembled cross-sections of steel box segments using these feature points. This approach also included new evaluation indices to assess the pre-assembly's efficacy. Finally, the digital pre-assembly method's feasibility and accuracy were demonstrated, through method comparison and accuracy verification, using simulated point clouds from steel box segments of arch ribs in a specific tied arch bridge, and on-site scanning experiments validated the method's practicality. The results show that this method achieves efficient and high-precision virtual pre-assembly of steel box segments with a maximum manufacturing dimension error of 0.02 mm, outperforming traditional methods under simulated conditions. Pre-assembly accuracy from on-site scanning point cloud data can reach up to 1 cm, with a scanning distance of 10 m and a minimum component size of 8 mm. This method provides a substantial reference and algorithmic support for the digital pre-assembly of prefabricated bridge segments.7 tabs, 12 figs, 22 refs.

References:

[1] 齐宏拓,刘界鹏,程国忠,等.基于点云数据的大型复杂钢结构智能化施工方法[J].土木工程学报,2024,57(1):65-75.
QI Hong-tuo,LIU Jie-peng,CHENG Guo-zhong,et al.Intelligent construction of large and complex steel structure based on point cloud data[J].China Civil Engineering Journal,2024,57(1):65-75.
[2]梁 栋,赵 恺,马印怀,等.基于3D激光扫描的钢桥塔节段虚拟装配方法[J].桥梁建设,2021,51(3):62-71.
LIANG Dong,ZHAO Kai,MA Yin-huai,et al.Virtual steel pylon segments assembly method based on 3D laser scanning[J].Bridge Construction,2021,51(3):62-71.
[3]KIM D,KWAK Y,SOHN H.Accelerated cable-stayed bridge construction using terrestrial laser scanning[J].Automation in Construction,2020,117:103269.
[4]闵 岚,汪 泱,冯 骏.钢结构计算机模拟预拼装技术的应用[J].工业建筑,2013,43(增1):722-723,713.
MIN Lan,WANG Yang,FENG Jun.Application of computer simulation pre-assembly technology of steel structure[J].Industrial Construction,2013,43(S1):722-723,713.
[5] 刘晓光,潘永杰.虚拟预拼装技术在钢桁梁中的应用研究[J].铁道建筑,2020,60(1):1-6.
LIU Xiao-guang,PAN Yong-jie.Application of virtual pre-assembly technology for steel truss girder[J].Railway Engineering,2020,60(1):1-6.
[6]吴文清,刘泓佚,王新雅,等.基于点云疏密度分类的混凝土梁逆向建模方法[J].长安大学学报(自然科学版),2022,42(6):23-32.
WU Wen-qing,LIU Hong-yi,WANG Xin-ya,et al.Reverse modeling method for concrete beam based on classifications of density of point cloud[J].Journal of Chang'an University(Natural Science Edition),2022,42(6):23-32.
[7]HAO Z.Virtual assembly technology on steel bridge members of bolt connection[C]//GCMCE.Proceedings of the 2017 Global Conference on Mechanics and Civil Engineering.Paris:Atlantis Press,2017:270-274.
[8]周绪红,刘界鹏,程国忠,等.基于点云数据的大型复杂钢拱桥智能虚拟预拼装方法[J].中国公路学报,2021,34(11):1-9.
ZHOU Xu-hong,LIU Jie-peng,CHENG Guo-zhong,et al.Intelligent virtual trial assembly of large and complex steel arch bridges based on point cloud data[J].China Journal of Highway and Transport,2021,34(11):1-9.
[9]毛伟琦,李小珍,王熊珏.基于特征点的钢桁梁桥桁段虚拟拼装方法研究[J].桥梁建设,2023,53(6):26-33.
MAO Wei-qi,LI Xiao-zhen,WANG Xiong-jue.Research on feature-based virtual assembly algorithm for truss segments of steel truss bridge[J].Bridge Construction,2023,53(6):26-33.
[10]朱爱珠,王佳盟,潘文铭,等.基于PCL的钢箱节段虚拟预拼装技术[J].重庆交通大学学报(自然科学版),2024,43(1):10-17.
ZHU Ai-zhu,WANG Jia-meng,PAN Wen-ming,et al.Virtual preassembly technology of steel box segment based on PCL[J].Journal of Chongqing Jiaotong University(Natural Science),2024,43(1):10-17.
[11]左 勇,任 阳,杜志华,等.基于LP-RANSAC算法的路面点云快速移除算法[J].激光与光电子学进展,2023,60(14):384-389.
ZUO Yong,REN Yang,DU Zhi-hua,et al.Rapid removal algorithm of road surface point cloud based on LP-RANSAC algorithm[J].Laser & Optoelectronics Progress,2023,60(14):384-389.
[12]袁亚通.基于地面激光扫描的劲性骨架拱桥线形控制方法研究[D].重庆:重庆交通大学,2021.
YUAN Ya-tong.Research on linear control method of rigid skeleton arch bridge based on ground laser scanning[D].Chongqing:Chongqing Jiaotong University,2021.
[13]MELLADO N,AIGER D,MITRA N J.Super 4PCS fast global point cloud registration via smart indexing[J].Computer Graphics Forum,2014,33(5):205-215.
[14]LI D,LIU J,FENG L,et al.Automatic modeling of prefabricated components with laser-scanned data for virtual trial assembly[J].Computer-Aided Civil and Infrastructure Engineering,2021,36(4):453-471.
[15]赵梦娜,花向红,冯绍权,等.基于点云切片的建筑物门窗信息提取[J].中国激光,2020,47(6):183-192.
ZHAO Meng-na,HUA Xiang-hong,FENG Shao-quan,et al.Information extraction of buildings,doors,and windows based on point cloud slices[J].Chinese Journal of Lasers,2020,47(6):183-192.
[16]马振国.利用kdtree索引实现曲率自适应点云简化算法[J].测绘科学,2010,35(6):67-69.
MA Zhen-guo.A point cloud simplification algorithm based on kdtree and curvature sampling[J].Science of Surveying and Mapping,2010,35(6):67-69.
[17]朱宗玖,裴善强,周之琪,等.三维数据降维处理的实现方法[J].光电子·激光,2020,31(1):89-95.
ZHU Zong-jiu,PEI Shan-qiang,ZHOU Zhi-qi,et al.Realization method of dimensionality reduction processing of 3D data[J].Journal of Optoelectronics·Laser,2020,31(1):89-95.
[18]王艺楠.基于特征降维与模糊聚类的自适应点云压缩研究[D].上海:东华大学,2017.
WANG Yi-nan.A simplification method for point cloud based on feature dimensionality reduction and fuzzy cluster[D].Shanghai:Donghua University,2017.
[19]熊风光,霍 旺,韩 燮,等.三维点云中关键点误匹配剔除方法[J].光学学报,2018,38(2):128-138.
XIONG Feng-guang,HUO Wang,HAN Xie,et al.Removal method of mismatching keypoints in 3D point cloud[J].Acta Optica Sinica,2018,38(2):128-138.
[20]赵明富,黄 铮,宋 涛,等.融合采样一致性和迭代最近点算法的点云配准方法[J].激光杂志,2019,40(10):45-50.
ZHAO Ming-fu,HUANG Zheng,SONG Tao,et al.Point cloud registration method based on sample consensus initial alignment and iterative closest point algorithm[J].Laser Journal,2019,40(10):45-50.
[21]王 珊,王春阳.基于特征点匹配的三维点云配准算法[J].国外电子测量技术,2020,39(12):23-28.
WANG Shan,WANG Chun-yang.Three-dimensional point cloud registration algorithm based on feature point matching[J].Foreign Electronic Measurement Technology,2020,39(12):23-28.
[22]SMITH A,SARLO R.Automated extraction of structural beam lines and connections from point clouds of steel buildings[J].Computer-Aided Civil and Infrastructure Engineering,2022,37(1):110-125.

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Last Update: 2024-12-30