中文核心期刊
CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊
重庆交通大学学报（自然科学版）

重庆交通大学学报（自然科学版） ›› 2024, Vol. 43 ›› Issue (6): 1-7.DOI: 10.3969/j.issn.1674-0696.2024.06.01

• 交通基础设施工程 •    

基于无人机点云与BIM模型的桥梁施工进度识别方法

冯东明1,2,3，葛健1,2，吴刚1,2,3，员建斌4   

  1. (1.东南大学 混凝土及预应力混凝土结构教育部重点实验室，江苏 南京 211189；2.东南大学 智慧建造与运维国际地方联合工程研究中心，江苏 南京 211189；3.东南大学 土木工程学院，江苏 南京 211189；4.中铁建投山西高速公路有限公司，山西 运城 044031)
  • 收稿日期:2023-07-24 修回日期:2024-01-10 发布日期:2024-06-24
  • 作者简介:冯东明(1985—)，男，山东寿光人，教授，博士，主要从事桥梁智慧运维与健康监测方面的研究。E-mail: dfeng@seu.edu.cn 通信作者：吴刚（1976—），男，浙江东阳人，教授，博士，主要从事桥梁智慧运维与健康监测方面的研究。E-mail: g.wu@seu.edu.cn
  • 基金资助:
    国家重大科研仪器研制项目（52127813）；国家重点研发计划项目（2020YFC1511900）

Bridge Construction Progress Identification Method Based on UAV Point Cloud and BIM Model

FENG Dongming1,2,3， GE Jian1,2， WU Gang1,2,3， YUAN Jianbin4   

  1. (1. Key Laboratory of Concrete and Prestressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 211189, Jiangsu, China; 2. International and Local Joint Engineering Research Center for Intelligent Construction and Maintenance, Southeast University, Nanjing 211189, Jiangsu, China; 3. School of Civil Engineering, Southeast University, Nanjing 211189, Jiangsu, China; 4. China Railway Construction Investment Shanxi Expressway Co., Ltd., Yuncheng 044031, Shanxi, China)
  • Received:2023-07-24 Revised:2024-01-10 Published:2024-06-24

PDF

摘要： 为准确实现桥梁施工进度监控自动化，提出一种基于无人机（unmanned aerial vehicle，UAV）点云和BIM模型的桥梁施工进度识别方法。该方法基于UAV路径规划及三维重建获取实际进度点云，通过BIM模型转点云的方式将BIM模型转化为计划进度点云，配准实际与计划进度点云，基于最近邻搜索及颜色区域生长分割桥梁构件，获得桥梁各构件的完成度。以山西临猗黄河大桥为研究对象，采集实际与计划点云数据，计算所得的各构件完成度准确度均在95％以上。该方法可快速采集进度数据并实现构件级别的桥梁施工进度识别，提高桥梁施工进度监控的自动化。

关键词: 桥梁工程；施工进度识别；无人机；点云；BIM；三维重建

Abstract: In order to accurately and automatically monitor bridge construction progress, a bridge construction progress identification method based on unmanned aerial vehicle (UAV) point cloud and BIM model was proposed. Firstly, as-built point clouds were obtained based on UAV flight planning and 3D reconstruction, and the BIM model was converted into as-planned point clouds by turning the BIM model into point clouds. Secondly, bridge components were segmented based on the nearest neighbor search and color region growth after the registration between as-built and as-planned point clouds. Finally, the percentage of completion of each bridge component was acquired. Taking Shanxi Linyi Yellow River Bridge as the research example, the as-built and as-planned point cloud data were collected, and the accuracy of the calculated percentage of completion of each component was above 95%. The proposed method can quickly collect progress data and realize bridge construction progress identification at the component level, which improves the automation of bridge construction progress monitoring.

Key words: bridge engineering; construction progress identification; UAV; point cloud; BIM; 3D reconstruction

中图分类号: