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: 199
- HTML全文浏览量: 22
- 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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