基于BIM和三维激光扫描的桁架几何质量自动化检测研究

doi:10.11996/JG.j.2095-302X.2024040845

图学学报 ›› 2024, Vol. 45 ›› Issue (4): 845-855.DOI: 10.11996/JG.j.2095-302X.2024040845

基于BIM和三维激光扫描的桁架几何质量自动化检测研究

邹亚坤¹^,²(), 陈贤川¹^,², 谭毅¹^,²(), 林永枫³, 张亚飞³

1.深圳大学土木与交通工程学院中澳BIM与智慧建造联合研究中心，广东深圳 518000
2.深圳大学滨海城市韧性基础设施教育部重点实验室，广东深圳 518000
3.广州建筑湾区智造科技有限公司，广东，广州 510000

收稿日期:2023-11-26 接受日期:2024-03-24 出版日期:2024-08-31 发布日期:2024-09-03
通讯作者:谭毅(1989-)，男，副教授，博士。主要研究方向为计算机视觉、数字孪生、BIM和工程物联网等。E-mail：tanyi@szu.edu.cn
第一作者:邹亚坤(2000-)，男，硕士研究生。主要研究方向为点云数据处理与BIM。E-mail：2210474005@email.szu.edu.cn
基金资助:
国家自然科学基金项目(52308319);广东省自然科学基金项目(2023A1515011119)

Automated detection of truss geometric quality based on BIM and 3D laser scanning

ZOU Yakun¹^,²(), CHEN Xianchuan¹^,², TAN Yi¹^,²(), LIN Yongfeng³, ZHANG Yafei³

1. Sino-Australia Joint Research Center in BIM and Smart Construction, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen Guangdong 518000, China
2. Key Laboratory of Coastal Urban Resilient Infrastructure (MOE), Shenzhen University, Shenzhen Guangdong 518000, China
3. GMC Grand-bay Intelligent Manufacturing and Technology Co., Ltd., Guangzhou Guangdong 510000, China

Received:2023-11-26 Accepted:2024-03-24 Published:2024-08-31 Online:2024-09-03
Contact: TAN Yi (1989-), associate professor, Ph.D. His main research interests cover computer vision, digital twin, BIM and engineering IoT, etc. E-mail：tanyi@szu.edu.cn
First author：ZOU Yakun (2000-), master student. His main research interests cover point cloud data process and BIM. E-mail：2210474005@email.szu.edu.cn
Supported by:
National Natural Science Foundation of China(52308319);Natural Science Foundation of Guangdong Province of China(2023A1515011119)

摘要/Abstract

摘要：

桁架结构因其自重轻、承载能力强而广泛应用于大跨度公共建筑中，随着使用时间的增加，需要对其结构几何质量进行定期检测以确保其安全性。然而，传统的桁架结构几何质量检测主要依赖人工手段，效率低下且成本高昂。为了实现桁架结构的高效几何质量检测，提出一种基于建筑信息模型(BIM)和三维激光扫描的自动化检测算法。首先，通过BIM将获得的原始点云数据中的桁架结构与背景分离。然后，基于关键点检测技术自动提取桁架结构的几何特征并实现节点坐标的定位计算。最后，将计算结果与BIM中的设计信息进行比较，获得几何质量检测结果。深圳市某校园内的演会中心被用于该方法的验证。实验结果表明，该算法的计算结果与全站仪的测量结果误差不超过2 mm。其与BIM模型数据进行对比，检测出桁架结构的节点存在不同程度的沉降。因此，该方法能准确快速地实现节点的空间定位，提高桁架结构几何质量检测的效率。

关键词: 自动化, 桁架, 几何质量, BIM, 三维激光扫描

Abstract:

The truss structure, widely employed in large-span public buildings for its lightweight and high load-bearing capacity, requires periodic inspections of its geometric quality to ensure safety with its usage over time. However, conventional methods for inspecting the geometric quality of truss structures rely mainly on manual processes, which are inefficient and costly.This paper proposed an automated detection algorithm to perform geometric quality inspection of truss structures. Firstly, the truss structure was separated from the background in the acquired raw point cloud data using building information model (BIM). Subsequently, an algorithm based on key point detection technology automatically extracted geometric features of the truss structure and calculated node coordinates. Finally, by comparing computed results with the BIM design information, geometric quality inspection results were obtained. The validation of the proposed method was conducted in the auditorium of a campus in Shenzhen, China. The experimental results demonstrated that the computational outcomes of the proposed algorithm exhibited an error within 2 mm compared to the measurements obtained from the total station. When the computational results of the proposed method were contrasted with BIM model data, variations in the truss structure nodes were detected, indicating different degrees of settlement. Consequently, the proposed method enabled accurate and rapid spatial positioning of nodes, thereby enhancing the efficiency of geometric quality inspection for truss structures.

Key words: automation, truss, geometric quality, building information model, 3D laser scanning

中图分类号:

邹亚坤, 陈贤川, 谭毅, 林永枫, 张亚飞. 基于BIM和三维激光扫描的桁架几何质量自动化检测研究[J]. 图学学报, 2024, 45(4): 845-855.

ZOU Yakun, CHEN Xianchuan, TAN Yi, LIN Yongfeng, ZHANG Yafei. Automated detection of truss geometric quality based on BIM and 3D laser scanning[J]. Journal of Graphics, 2024, 45(4): 845-855.

图/表 21

参考文献 28

[1]	GUO M, SUN M X, PAN D, et al. High-precision detection method for large and complex steel structures based on global registration algorithm and automatic point cloud generation[J]. Measurement, 2021, 172: 108765.
[2]	马晓晓, 张胜, 程国忠, 等. 基于点云数据的预制叠合板尺寸质量智能检测方法[J]. 土木与环境工程学报: 中英文, 2024, 46(1): 102-109.
	MA X X, ZHANG S, CHENG G Z, et al. Automated dimensional quality assessment of precast laminated panels based on 3D laser scanning[J]. Journal of Civil and Environmental Engineering, 2024, 46(1): 102-109 (in Chinese).
[3]	刘界鹏, 崔娜, 周绪红, 等. 基于三维激光扫描的房屋尺寸质量智能化检测方法[J]. 建筑科学与工程学报, 2022, 39(4): 71-80, 3-4.
	LIU J P, CUI N, ZHOU X H, et al. Intelligent inspection method for dimensional quality of houses based on 3D laser scanning[J]. Journal of Architecture and Civil Engineering, 2022, 39(4): 71-80, 3-4 (in Chinese).
[4]	LI J L, SAYDAM S, XU Y Y, et al. Class-aware tiny object recognition over large-scale 3D point clouds[J]. Neurocomputing, 2023, 529(C): 166-181.
[5]	LIN C Y, LIU H, WU D Y, et al. Background point filtering of low-channel infrastructure-based LiDAR data using a slice-based projection filtering algorithm[J]. Sensors, 2020, 20(11): 3054.
[6]	YANG S, ZHENG L H, GAO W L, et al. An efficient processing approach for colored point cloud-based high-throughput seedling phenotyping[J]. Remote Sensing, 2020, 12(10): 1540.
[7]	MA Z H, SUN D W, XU H X, et al. Optimization of 3D point clouds of oilseed rape plants based on time-of-flight cameras[J]. Sensors, 2021, 21(2): 664.
[8]	HU Z T, CHEN C, YANG B S, et al. Geometric feature enhanced line segment extraction from large-scale point clouds with hierarchical topological optimization[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 112: 102858.
[9]	王浩, 郑德华, 刘存泰, 等. 基于SHOT特征描述子的自动提取球形标靶方法研究[J]. 图学学报, 2022, 43(5): 849-857.
	WANG H, ZHENG D H, LIU C T, et al. Research on automatic extraction of spherical targets based on SHOT feature descriptor[J]. Journal of Graphics, 2022, 43(5): 849-857 (in Chinese).
[10]	SHAO Y Y, TONG G F, PENG H. Mining local geometric structure for large-scale 3D point clouds semantic segmentation[J]. Neurocomputing, 2022, 500(C): 191-202.
[11]	XIANG X Y, WANG L, ZONG W P, et al. Extraction of local structure information of point clouds through space-filling curve for semantic segmentation[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 114: 103027.
[12]	HE G Z, YANG J, BEHNKE S. Research on geometric features and point cloud properties for tree skeleton extraction[J]. Personal and Ubiquitous Computing, 2018, 22(5): 903-910.
[13]	ZHANG J P, WANG J L, DONG P L, et al. Tree stem extraction from TLS point-cloud data of natural forests based on geometric features and DBSCAN[J]. Geocarto International, 2022, 37(25): 10392-10406.
[14]	TAN Y, CHEN L M, WANG Q, et al. Geometric quality assessment of prefabricated steel box girder components using 3D laser scanning and building information model[J]. Remote Sensing, 2023, 15(3): 556.
[15]	SELVARAJ S, MADHAVAN M. Geometric imperfection measurements and validations on cold-formed steel channels using 3D noncontact laser scanner[J]. Journal of Structural Engineering, 2018, 144(3): 04018010.
[16]	XIA Y, BLUM H. Geometric imperfection measurements of cold-formed steel members using a portable non-contact 3D laser scanner[EB/OL]. [2024-01-20]. https://www.researchgate.net/publication/346533087_Geometric_imperfection_measurements_of_cold-formed_steel_members_using_a_portable_non-contact_3D_laser_scanner.
[17]	LIU J D, ZHANG Q L, WU J, et al. Dimensional accuracy and structural performance assessment of spatial structure components using 3D laser scanning[J]. Automation in Construction, 2018, 96: 324-336.
[18]	CHA G, PARK S, OH T. A terrestrial LiDAR-based detection of shape deformation for maintenance of bridge structures[J]. Journal of Construction Engineering and Management, 2019, 145(12): 04019075.
[19]	XU D D, WANG Y Q, LIU X L, et al. A novel method and modelling technique for determining the initial geometric imperfection of steel members using 3D scanning[J]. Structures, 2023, 49: 855-874.
[20]	National Institute of Building Sciences. National BIM standard-United States version 3[EB/OL]. (2007-12-15) [2024-01-20]. https://classes.engr.oregonstate.edu/cce/winter2018/cce203/NBIMS-US_V3/NBIMS-US_V3_Annex_B_NBIMS-V1P1_December_2007.pdf.
[21]	中华人民共和国住房和城乡建设部.建筑信息模型设计交付标准: GB/T 51301—2018[S]. 北京: 中国建筑工业出版社, 2018: 8-9.
	Ministry of Housing and Urban-Rural Development of the People’s Republic of China. Standard for design delivery of building information modeling: GB/T 51301—2018[S]. Beijing: China Architecture & Building Press, 2018: 8-9 (in Chinese).
[22]	BESL P J, MCKAY N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 239-256.
[23]	范强, 刘鹏, 杨俊, 等. 基于3D-Harris 与 FPFH 改进的 3D-NDT 配准算法[J]. 图学学报, 2020, 41(4): 567-575. DOI
	FAN Q, LIU P, YANG J, et al. Improved 3D-NDT point cloud registration algorithm based on 3D-Harris and FPFH[J]. Journal of Graphics, 2020, 41(4): 567-575 (in Chinese). DOI
[24]	ASSEM I, DUPONT G. Friezes and a construction of the Euclidean cluster variables[J]. Journal of Pure and Applied Algebra, 2011, 215(10): 2322-2340.
[25]	FISCHLER M A, BOLLES R C. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[M]// Readings in Computer Vision. Amsterdam: Elsevier, 1987: 726-740.
[26]	中华人民共和国住房和城乡建设部.钢结构设计标准: GB 50017—2017[S]. 北京: 中国建筑工业出版社, 2017: 145-146.
	Ministry of Housing and Urban-Rural Development of the People’s Republic of China.Standard for design of steel structures: GB 50017—2017[S]. Beijing: China Architecture & Building Press, 2017: 145-146 (in Chinese).
[27]	HAN L, BANCROFT J. Nearest approaches to multiple lines in n-dimensional space[EB/OL]. [2024-01-20]. https://www.researchgate.net/publication/265001563_Nearest_approaches_to_multiple_lines_in_n-dimensional_space.
[28]	王佳, 吴磊, 周小平, 等. 基于构件形状分布和配准位置的BIM模型对比方法[J]. 图学学报, 2020, 41(3): 480-489. DOI
	WANG J, WU L, ZHOU X P, et al. Comparison method of BIM models based on component shape distribution and registration position[J]. Journal of Graphics, 2020, 41(3): 480-489 (in Chinese). DOI

等级	英文名	代号	包含的最小模型单元
1.0级模型精细度	Level of Model Definition 1.0	LOD1.0	项目级模型单元
2.0级模型精细度	Level of Model Definition 2.0	LOD2.0	功能级模型单元
3.0级模型精细度	Level of Model Definition 3.0	LOD3.0	构件级模型单元
4.0级模型精细度	Level of Model Definition 4.0	LOD4.0	零件级模型单元

等级	英文名	代号	包含的最小模型单元
1.0级模型精细度	Level of Model Definition 1.0	LOD1.0	项目级模型单元
2.0级模型精细度	Level of Model Definition 2.0	LOD2.0	功能级模型单元
3.0级模型精细度	Level of Model Definition 3.0	LOD3.0	构件级模型单元
4.0级模型精细度	Level of Model Definition 4.0	LOD4.0	零件级模型单元

性能参数	数值
视场	360°×282°
扫描时间	最快94 s
扫描速度	最大500 kHz
测程	0.6~80 m
测距精度	2 mm
角度精度	21''

性能参数	数值
视场	360°×282°
扫描时间	最快94 s
扫描速度	最大500 kHz
测程	0.6~80 m
测距精度	2 mm
角度精度	21''

性能参数	数值
测角精度	2''
无棱镜测程	800 m
测距精度	3 mm+2 ppm
测量时间	约1.5 s

基于BIM和三维激光扫描的桁架几何质量自动化检测研究

Automated detection of truss geometric quality based on BIM and 3D laser scanning

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摘要/Abstract

引用本文

使用本文

图/表 21

参考文献 28

相关文章 15

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Metrics

本文评价

GUID	中心坐标/m	PCD_ID	中心坐标/m
233042	(3.986, 7.989, 11.577)	1	(9.988, 38.001, 8.023)
233218	(3.986, 11.992, 11.577)	2	(53.997, 17.992, 8.025)
233219	(-0.014, 11.992, 11.577)	3	(35.998, -0.019, 11.535)
233304	(3.986, 15.993, 11.577)	4	(61.998, 42.014, 7.988)
233305	(-0.014, 15.993, 11.577)	5	(56.004, 48.002, 11.561)
···	···	···	···
235945	(62.000, 29.999, 8.026)	418	(39.990, 56.013, 11.545)
235949	(64.001, 36.002, 11.577)	419	(9.993, 17.992, 8.029)
235955	(62.000, 34.001, 8.026)	420	(17.988, 9.996, 8.022)
235965	(62.000, 38.003, 8.026)	421	(45.999, 45.998, 8.026)
235975	(62.000, 42.004, 8.026)	422	(53.997, 37.999, 8.032)

GUID	中心坐标/m	PCD_ID	中心坐标/m
233042	(3.986, 7.989, 11.577)	39	(4.019, 8.022, 11.533)
233218	(3.986, 11.992, 11.577)	404	(3.989, 11.995, 11.547)
233219	(-0.014, 11.992, 11.577)	403	(-0.011, 12.003, 11.534)
233304	(3.986, 15.993, 11.577)	259	(3.987, 16.003, 11.552)
233305	(-0.014, 15.993, 11.577)	24	(-0.002, 15.992, 11.532)
···	···	···	···
235945	(62.000, 29.999, 8.026)	107	(61.993, 29.996, 7.983)
235949	(64.001, 36.002, 11.577)	413	(64.003, 36.002, 11.538)
235955	(62.000, 34.001, 8.026)	181	(61.997, 33.999, 7.980)
235965	(62.000, 38.003, 8.026)	188	(61.994, 38.000, 7.989)
235975	(62.000, 42.004, 8.026)	4	(61.998, 42.014, 7.988)

PCDID	三维激光扫描仪			全站仪			偏差/mm
PCDID	x	y	z	x	y	z	偏差/mm
23	2.044 53	18.003 90	7.974 56	2.045	18.005	7.975	1.27
42	5.976 54	17.997 60	7.994 92	5.977	17.998	7.996	1.24
49	1.994 00	37.992 60	7.972 50	1.995	37.993	7.972	1.19
70	5.997 44	9.996 55	7.995 16	5.998	9.997	7.996	1.11
71	5.985 68	13.999 70	8.000 11	5.985	13.999	8.000	0.98
84	1.999 16	21.987 70	7.991 13	2.000	21.989	7.991	1.55
90	61.995 00	21.993 10	7.984 19	61.996	21.993	7.985	1.29
98	1.993 92	25.978 40	7.997 08	1.994	25.979	7.997	0.61
103	17.987 60	29.997 30	7.995 96	17.987	29.998	7.997	1.39
109	1.995 78	29.997 30	7.975 51	1.996	29.997	7.976	0.62
116	21.991 70	33.993 40	7.999 09	21.992	33.993	8.000	1.04
117	57.984 90	33.996 00	7.989 51	57.986	33.997	7.990	1.57
120	13.986 50	37.999 20	7.998 21	13.987	38.000	7.999	1.23
163	49.997 20	13.990 70	8.000 75	49.998	13.991	8.000	1.14
165	1.991 44	14.002 70	7.991 35	1.990	14.004	7.992	2.05
166	62.013 90	14.006 60	8.004 91	62.015	14.006	8.006	1.66
187	49.990 10	34.008 90	7.993 09	49.990	34.008	7.993	0.91
219	37.985 70	54.005 10	7.986 91	37.986	54.005	7.987	0.33
222	49.999 90	54.001 20	7.987 06	50.001	54.001	7.987	1.12
421	45.999 10	45.998 30	8.026 99	46.000	45.997	8.026	1.87

[1]	张新宇, 张家意, 高欣. ASC-Net：腹腔镜视频中手术器械与脏器快速分割网络[J]. 图学学报, 2024, 45(4): 659-669.
[2]	陈菁, 余芳强, 易思坤, 仇春华, 曹盈. 基于BIM的施工深化自动出图系统设计与开发[J]. 图学学报, 2023, 44(4): 801-809.
[3]	甘子琛, 范晶晶, 薛芝婷, 林鼎量, 许镇. 集成BIM和区块链的设计责权追溯系统研究[J]. 图学学报, 2023, 44(4): 810-817.
[4]	马欢, 冀晶晶, 刘佳豪, 刘雨婷. 面向机器人自主分割的肉品识别分类系统实现[J]. 图学学报, 2021, 42(6): 924-930.
[5]	梁裕卿, 吉久茂, 杨佳蕾, 张东升, 王珂, 王凌宇, . 基于人工智能的 BIM 疏散设计自动化方法[J]. 图学学报, 2021, 42(2): 299-306.
[6]	张碧含, 由芳. 基于自动化接受度模型的车外交互研究[J]. 图学学报, 2020, 41(6): 1012-1017.
[7]	朱攀，史健勇 . 基于 AISI 网络的 BIM 三维重建方法研究[J]. 图学学报, 2020, 41(5): 839-846.
[8]	高歌 1,2，张越美 1,2，刘寒 1,2，李智 3，顾明 1,2 . 基于知识库的 IFC 模型检查方法研究[J]. 图学学报, 2019, 40(6): 1099-1108.
[9]	侯文龙，邓雪原 . 基于 IFC 标准的楼层平面尺寸标注自动生成方法研究[J]. 图学学报, 2019, 40(6): 1116-1122.
[10]	刘颖华 1,2,3，解琳琳 1,4，李爱群 1,4，侯妙乐 1,2,3，刘浩宇 1,2,3 . 古木建筑裂缝多 LoD 表达与信息自动集成[J]. 图学学报, 2019, 40(6): 1123-1129.
[11]	石力文 1,2,3，解琳琳 1,4，侯妙乐 1,2,3，李爱群 1,4，胡云岗 1,2,3，刘浩宇 1,2,3 . 基于显著几何特征的古木建筑关键构件多 LoD 尺寸信息提取方法[J]. 图学学报, 2019, 40(4): 651-658.
[12]	王茹，权超超 . 公路立交 BIM 参数化快速精确建模方法研究[J]. 图学学报, 2019, 40(4): 766-770.
[13]	张超 . 基于 4D-BIM 的曲港高速公路质量与安全研究[J]. 图学学报, 2019, 40(4): 778-782.
[14]	姜韶华，吴峥，王娜，姜丽萍，韩芸蔚 . OpenBIM 综述及其工程应用[J]. 图学学报, 2018, 39(6): 1139-1147.
[15]	吴松飞，邓逸川，申琪玉，罗德焕 . BIM 支持的施工安全规范合规检查研究综述[J]. 图学学报, 2018, 39(6): 1156-1164.

方法	扫描站点规划	数据采集	数据处理	总时间
本文	5	30	16	51
全站仪	10	10	70	90

方法	扫描站点规划	数据采集	数据处理	总时间
本文	5	30	16	51
全站仪	10	10	70	90