[1]朱劲松,王多吴,杨瑞鹏.基于点云数据的钢箱节段数字化预拼装方法[J].长安大学学报(自然科学版),2024,44(6):47-58.[doi:10.19721/j.cnki.1671-8879.2024.06.005]
　ZHU Jin-song,WANG Duo-wu,YANG Rui-peng.Digital pre-assembly method for steel box segments based on point cloud data[J].Journal of Chang’an University (Natural Science Edition),2024,44(6):47-58.[doi:10.19721/j.cnki.1671-8879.2024.06.005]
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基于点云数据的钢箱节段数字化预拼装方法()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第44卷
期数:
2024年6期
页码:
47-58
栏目:
桥梁与隧道工程
出版日期:
2024-12-30

文章信息/Info

Title:
Digital pre-assembly method for steel box segments based on point cloud data
文章编号:
1671-8879(2024)06-0047-12
作者:
朱劲松1; 2; 3; 王多吴2; 杨瑞鹏2
(1. 天津大学 水利工程智能建设与运维全国重点实验室,天津 300350; 2. 天津大学 建筑工程学院,天津 300350; 3. 天津大学 滨海土木工程结构与安全教育部重点实验室,天津 300350)
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)
关键词:
桥梁工程;  智能建造;  数字化预拼装;  激光点云;  质量检查
Keywords:
bridge engineering;  intelligent construction;  digital pre-assembly;  laser point cloud;  quality inspection
分类号:
U445.4
DOI:
10.19721/j.cnki.1671-8879.2024.06.005
文献标志码:
A
摘要:
为快速完成预制桥梁钢箱节段制造误差检查和预拼装,提出一种基于三维激光扫描点云模型的钢箱节段数字化预拼装方法。首先,基于三维激光扫描技术,对钢箱节段建立三维足尺点云模型并进行点云数据预处理获取完整无噪节段点云; 然后,开发点云边界快速提取算法与针对边界点云的平面投影算法,实现三维足尺点云模型边界点云自动提取和预拼装截面点云数据降维。接着,开发点云边界特征角点自动提取算法,并提出基于特征角点的钢箱节段预拼装截面配准拼接方法和截面预拼装效果评价指标,评估截面预拼装效果。最后,采用某系杆拱桥拱肋钢箱模拟点云进行方法对比和精度验证; 采用实验室钢箱节段现场扫描实测点云模型进行方法验证。研究结果表明:在模拟试验条件下,提出的方法与传统虚拟预拼装方法对比可实现识别钢箱结构最大0.02 mm制造尺寸误差的高效高精度模拟截面虚拟预拼装; 在现场试验条件下,构件最小尺寸为8 mm、扫描距离为10 m时,提出的算法可将预拼装精度控制到1 cm。该方法可为桥梁预制节段数字化预拼装提供参考与算法支撑。
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.

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备注/Memo

备注/Memo:
收稿日期:2024-06-19
基金项目:国家自然科学基金项目(52378310)
作者简介:朱劲松(1975-),男,安徽池州人,教授,博士研究生导师,E-mail:jszhu@tju.edu.cn。
更新日期/Last Update: 2024-12-30