基于点云数据的网架结构提升变形智能监测方法

金钦明; 程国忠; 李东声; 王聪; 陈莎莎; 王瑞荣; 毕静刚

doi:10.13204/j.gyjzG21061811

基于点云数据的网架结构提升变形智能监测方法

doi: 10.13204/j.gyjzG21061811

1. 中建铁路投资建设集团有限公司, 北京 102601;
2. 重庆大学土木工程学院, 重庆 400045;
3. 深圳大学土木与交通工程学院, 广东深圳 518060

基金项目:

国家自然科学基金项目（52008055）。

详细信息

作者简介:
金钦明,男,1989年出生,工程师。

通讯作者:
程国忠,chengguozhong@cqu.edu.cn。

计量
- 文章访问数: 127
- HTML全文浏览量: 15
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2021-06-18
- 网络出版日期: 2023-03-22

Intelligent Deformation Monitoring for Lifting Space Frames Based on Point Cloud Data

1. China State Railway Investment and Construction Group Co., Ltd., Beijing 102601, China;
2. School of Civil Engineering, Chongqing University, Chongqing 400045, China;
3. College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China

摘要

摘要: 变形监测是保障网架结构提升施工安全的重要手段之一，目前传统方法仅能实现对局部点的变形监测，难以实现对整体结构的变形监测。三维激光扫描技术可全覆盖地得到已完成结构的精准点云数据，这为解决上述难题提供了新思路。为此，以泸州高铁站为工程背景，开展基于点云数据的网架结构提升变形智能监测研究，包括点云数据预处理、点云数据非刚性配准以及提升变形可视化三个方面。针对网架结构提升前后的点云数据非刚性配准问题，基于聚类算法、随机采样一致性算法、图结构方法以及正交普氏分析等提出了集球心智能定位、球心粗匹配、球心非刚性配准于一体的非刚性配准算法。研究结果表明，基于点云数据的网架结构提升变形智能监测方法高效、全面且实用。
- 网架结构 /
- 变形监测 /
- 三维激光扫描 /
- 点云数据 /
- 非刚性配准
Abstract: Deformation monitoring is one of the most essential means of ensuring construction safety for lifting space frames. Current traditional methods only enable deformation monitoring at local points, but not for the whole structure. Three-dimensional (3D) laser scanning technology can capture accurate point clouds of as-built structures through full-coverage scanning, thus providing a innovative solution to the above issue. To this end, the research on intelligent deformation monitoring for lifting space frames based on point cloud data, including point cloud data preprocessing, non-rigid matching of point clouds and lifting deformation visualization, was carried out based on a practical engineering project, namely Luzhou Railway Station. Based on clustering algorithms, random sample consensus, graph structural methods and orthogonal procrustes analysis, a non-rigid matching algorithm integrating intelligent sphere positioning, coarse sphere matching, non-rigid sphere matching was proposed for the non-rigid matching of point clouds of space frames before and after lifting. The results showed that the proposed intelligent deformation monitoring approach based on point cloud data was efficient, comprehensive and practical.
- space frame /
- deformation monitoring /
- 3D laser scanning /
- point cloud data /
- non-ridge matching

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参考文献(19)

[1]	董石麟. 我国大跨度空间钢结构的发展与展望[J]. 空间结构, 2000, 6(2):3-13.
[2]	翁凯. 大跨度钢管桁架结构的施工技术研究[D]. 天津:天津大学, 2012.
[3]	陈晋中, 郝林山. 高层与大跨结构施工技术[M]. 北京:机械工业出版社, 2004.
[4]	蔡俊. 大跨度钢网架结构整体提升施工关键技术应用研究[D]. 广州:华南理工大学, 2016.
[5]	季克建. 基于BIM技术的网架液压提升施工技术[J]. 施工技术, 2019, 48(10):92-95.
[6]	雷素素, 刘宇飞, 段先军, 等. 复杂大跨空间钢结构施工过程综合监测技术研究[J]. 工程力学, 2018, 35(12):203-211.
[7]	MA Z, LIU S. A review of 3D reconstruction techniques in civil engineering and their applications[J]. Advanced Engineering Informatics, 2018, 37:163-174.
[8]	WANG Q, KIM M K. Applications of 3D point cloud data in the construction industry:a fifteen-year review from 2004 to 2018[J].Advanced Engineering Informatics, 2019, 39:306-319.
[9]	WU Z, ZENG Y, LI D, et al. High-volume point cloud data simplification based on decomposed graph filtering[J]. Automation in Construction, 2021, 129, 103815.
[10]	BROWN B J, RUSINKIEWICZ S. Global non-rigid alignment of 3-D scans[J]. ACM Transactions on Graphics, 2007, 26(3):21.
[11]	BENTLEY J L, FRIEDMAN J H. Data structures for range searching[J]. ACM Computing Surveys (CSUR), 1979, 11(4):397-409.
[12]	ALTMAN N S. An introduction to kernel and nearest-neighbor nonparametric regression[J]. The American Statistician, 1992, 46(3):175-185.
[13]	BLOMLEY R, WEINMANN M, LEITLOFF J, et al. Shape distribution features for point cloud analysis-a geometric histogram approach on multiple scales[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014, 2(3):9.
[14]	ESTER M, KRIEGEL H P, SANDER J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Knowledge Discovery and Data Mining. 1996, 96(34):226-231.
[15]	FISCHLER M A, BOLLES R C. Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography[J]. Commun. ACM, 1981, 24(6):381-395.
[16]	AIGER D, MITRA N J, COHEN-OR D. 4-points congruent sets for robust pairwise surface registration[J]. ACM Transactions on Graphics, 2008, 27(3):1-10.
[17]	LUXBURG ULRIKE V. A tutorial on spectral clustering[J]. Statistics and Computing, 2007, 17(4):395-416.
[18]	HAGBERG A, SWART P, S CHULT D. Exploring network structure, dynamics, and function using NetworkX[R]. Los Alamos, NM (United States):Los Alamos National Lab.(LANL), 2008.
[19]	WATTS D J, STROGATZ S H. Collective dynamics of ‘small-world’ networks[J]. Nature, 1998, 393(6684):440-442.