|本期目录/Table of Contents|

[1]付传清,王沈昕旸,舒江鹏,等.基于无人机巡检图像与BIM模型的桥梁病害自动化定位研究[J].建筑科学与工程学报,2025,42(05):135-144.[doi:10.19815/j.jace.2024.08087]
　FU Chuanqing,WANG Shenxinyang,SHU Jiangpeng,et al.Research of bridge defect automated localization based on UAV inspection images and BIM model[J].Journal of Architecture and Civil Engineering,2025,42(05):135-144.[doi:10.19815/j.jace.2024.08087]
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基于无人机巡检图像与BIM模型的桥梁病害自动化定位研究(PDF)
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《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

卷:
42卷
期数:
2025年05期
页码:
135-144
栏目:
隧道工程
出版日期:
2025-09-30

文章信息/Info

Title:
Research of bridge defect automated localization based on UAV inspection images and BIM model
文章编号:
1673-2049(2025)05-0135-10
作者:
付传清1,王沈昕旸1,舒江鹏2,3,徐庆凯4,章金勇4,蒋友4,谢义华4,徐声亮5
(1. 浙江工业大学 土木工程学院,浙江 杭州 310023; 2. 浙江大学 建筑工程学院,浙江 杭州 310058; 3. 浙江大学 长三角智慧绿洲创新中心,浙江 嘉兴 314100; 4. 中铁十二局集团城市发展建设有限公司,江苏 苏州 215011; 5. 宁波市政工程建设集团股份有限公司,浙江 宁波 315000)
Author(s):
FU Chuanqing1, WANG Shenxinyang1, SHU Jiangpeng2,3, XU Qingkai4, ZHANG Jinyong4, JIANG You4, XIE Yihua4, XU Shengliang5
1.College of Civil Engineering,Zhejiang University of Technology,Hangzhou 310023,Zhejiang,China;2.College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,Zhejiang,China;3.Innovation Center of Yangtze River Delta,Zhejiang University,Jiaxing 314100,Zhejiang,China;4.China Railway 12th Bureau Group Urban Development and Construction Co.,Ltd, Suzhou 215011,Jiangsu,China;5.Ningbo Municipal Engineering Construction Group Ltd,Ningbo 315000,Zhejiang,China
关键词:
桥梁工程;  桥梁病害定位;  坐标投影与映射;  配准;  无人机;  BIM
Keywords:
bridge engineering;  bridge defect localization;  coordinate projection and mapping;  registration;  unmanned aerial vehicle;  BIM
分类号:
U446
DOI:
10.19815/j.jace.2024.08087
文献标志码:
A
摘要:
为实现局部病害图像在桥梁三维运维模型中的实时自动精准定位,提出基于无人机巡检图像、GPS坐标和BIM模型的桥梁病害自动化定位方法。首先提出通过坐标投影与映射方法将病害图像坐标由GPS WGS-84坐标系映射至BIM模型坐标系; 然后建立基于BIM模型的坐标配准算法,将2D病害图像配准于所处的BIM模型实体单元,实现病害图像与BIM模型的联动; 最后基于Autodesk Revit环境开展二次开发,实现算法落地。采用无人机进行了桥梁病害采集与现场试验,验证该方法的精度和可行性。结果表明:基于无人机拍摄的图像及所包含GPS信息,采用本方法可实现桥梁病害在BIM模型上的实时自动化精准定位,整体定位绝对误差平均值为19.50 cm,构件定位准确率为98.8%,基本满足桥梁巡检要求; 该研究成果具有较高的可行性和工程应用潜力,可以为桥梁长期运维管养提供精准数据与决策支持。
Abstract:
In order to realize the automatic real-time and accurate localization of local defect images in the 3D operation and maintenance model of bridges, a bridge defect automated localization method based on unmanned air vehicle(UAV)inspection images, global positioning system(GPS)coordinates and BIM models was proposed. Firstly, a coordinate projection and mapping method was proposed to map the defect image coordinates from GPS WGS-84 coordinate system to BIM model coordinate system. Then, a coordinate registration algorithm based on the BIM model was established to register and align the 2D defect image to the component unit of BIM model, and realized the linkage between the defect image and the BIM model. Finally, the algorithm was implemented by secondary development based on Autodesk Revit environment. The UAV was used to conduct the bridge defect acquisition and field test, and the accuracy and feasibility of the method were verified. The results show that based on the images taken by UAV and the contained GPS information, the proposed method can realize real-time automated accurate localization of bridge defects on BIM model. The overall localization mean absolute error is 19.50 cm, and the component localization accuracy rate is 98.8%, which basically meets the requirements of the bridge inspection. This research has enough feasibility and engineering application potential, and can provide accurate data basis and decision support for the long-term inspection and maintenance of bridges.

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备注/Memo

备注/Memo:
收稿日期:2024-08-25 投稿网址:http://jace.chd.edu.cn
基金项目:江苏省科技计划专项(BZ2024047); 宁波市“科创甬江2035”重点研发计划项目(2024H013); 宁波市公益性科技计划项目(2023S050)
作者简介:付传清(1982-),男,工学博士,教授,博士生导师,E-mail:chuanqingfu@126.com。
通信作者:舒江鹏(1987-),男,工学博士,研究员,博士生导师,E-mail:jpeshu@zju.edu.cn。
Author resumes: FU Chuanqing(1982-), male, PhD, professor, E-mail: chuanqingfu@126.com; SHU Jiangpeng(1987-), male, PhD, research fellow, E-mail: jpshu@zju.edu.cn.
更新日期/Last Update: 2025-09-25