Research on virtual trial-assembly technology of steel-pipe-arch ribs based on limited perception

Jun Xiao , Jianping Xian , Luchao Tian , Jiepeng Liu , Jianyong Ma , Haotian Li

Advances in Bridge Engineering ›› 2025, Vol. 6 ›› Issue (1) : 20

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Advances in Bridge Engineering ›› 2025, Vol. 6 ›› Issue (1) : 20 DOI: 10.1186/s43251-025-00165-5
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Research on virtual trial-assembly technology of steel-pipe-arch ribs based on limited perception

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Abstract

Concret-filled-steel-tube arch bridges often employ solid trial-assembly for the arch ribs to confirm matching accuracy and overall alignment. However, these methods often suffer from issues such as large site occupation, multiple assembly cycles, and prolonged construction periods. This paper proposes a virtual trial-assembly technology of steel-pipe-arch ribs based on limited perception, which achieves rapid virtual trial-assembly without the need for physical segment matching. By obtaining joint control point data through limited measurement perception, the method virtually assembles the control points according to the theoretical manufacturing configuration. It extracts the flange position parameters between the arch rib segments under the ideal configuration condition, ultimately guiding the adjustment and installation of the flanges. Additionally, a self-holding device for steel structure joints is designed, which achieves precise positioning and reliable installation of flanges through parameterized adjustment. A virtual trial-assembly experiment of the steel pipe arch rib joint was conducted using the proposed method. The results of the experiment indicate that the method has high control precision and good technical performance. It has overcome the technical barriers to the application of virtual trial-assembly technology in the construction process and has good potential for promotion and application in similar bridge types.

Keywords

Concrete-filled-steel-tube arch bridge / Intelligent construction / Virtual trial-assembly / Steel structure joint self-holding device / Flange installation

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Jun Xiao, Jianping Xian, Luchao Tian, Jiepeng Liu, Jianyong Ma, Haotian Li. Research on virtual trial-assembly technology of steel-pipe-arch ribs based on limited perception. Advances in Bridge Engineering, 2025, 6(1): 20 DOI:10.1186/s43251-025-00165-5

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References

[1]

Barazzetti L (2016) Parametric as-built model generation of complex shapes from point clouds. Adv Eng Inform 30(3):298311. https://doi.org/10.1016/j.aei.2016.03.005

[2]

British Standards Institution (2008) Execution of steel structures and aluminium structures (Part 2: Technical requirements for steel structures). BS EN 1090-2:2008.

[3]

Case F, Beinat A, Crosilla F, Alba IM (2014) Virtual trial assembly of a complex steel structure by Generalized Procrustes Analysis techniques. Autom Constr 37:155–165. https://doi.org/10.1016/j.autcon.2013.10.013

[4]

Ceglarek D, Huang W, Zhou S et al (2004) Time-based competition in multistage manufacturing: Stream-of-variation analysis (SOVA) methodology Review. Int J Flex Manuf Syst 16(1):11–44. https://doi.org/10.1023/B:FLEX0000039171.25141.a4

[5]

Cheng GZ, Liu JP, Cui N et al (2023) Virtual trial assembly of large steel members with bolted connections based on point cloud data. Autom Constr 151:104866. https://doi.org/10.1016/j.autcon.2023.104866

[6]

Chiara A, Domenico L, Luigi S (2015) Identifying Seismic Local Collapse Mechanisms in Unreinforced Masony Buildings through 3D Laser Scanning. Key Eng Mat 628:79–84. https://doi.org/10.4028/www.scientific.net/KEM.628.79

[7]

Japan Road Association (2002) Road and Bridge Illustrated Manual

[8]

KalasapudiVS, TangP. Automated tolerance analysis of curvilinear components using 3D point clouds for adaptive construction Quality Control. Comput Civ Eng, 2015, 151257-65.

[9]

KimMK, SohnH, ChangCC. Automated dimensional quality assessment of precast concrete panels using terrestrial laser scanning. Autom Constr, 2014, 45: 163-177.

[10]

Kim MK, Thedja JPP, Wang Q (2020) Automated dimensional quality assessment for formwork and rebar of reinforced concrete components using 3D point cloud data. Autom Constr 112:103077. https://doi.org/10.1016/j.autcon.2020.103077

[11]

Kim MK, Wang Q, Li H (2019) Non-contact sensing based geometric quality assessment of buildings and civil structures: A review. Autom Constr 100:163–179. https://doi.org/10.1016/j.autcon.2019.01.002

[12]

KimMK, WangQ, ParkJW, et al. . Automated dimensional quality assurance of full-scale precast concrete elements using laser scanning and BlM. Autom Constr, 2016, 72: 102-114.

[13]

Lee KH, Woo H, Suk T (2001) Data Reduction Methods for Reverse gineering. Int J Adv Manuf Technol 17:735–743. https://doi.org/10.1007/S001700170119

[14]

Li YD (2012) Application of digital simulation pre-assembling technology in large-scale steel structure. Constr Technol 41(18):23–26. in Chinese

[15]

Li X, Hu HF, Chen TG et al (2023) Intelligent virtual preassembly application of long span steel truss based on point cloud and BIM. Steel Construction(Chinese & English) 38(10):10–15. https://link.cnki.net/doi/10.13206/j.gjgs23081201

[16]

Maset E, Scalera L, Zonta D, Alba IM, Crosilla F, Fusiello A (2020) Procrustes analysis for the virtual trial assembly of large-size elements. Robot Comp-integr Manuf 62:101885. https://doi.org/10.1016/j.rcim.2019.101885

[17]

Ministry of Housing and Urban-Rural Development of the People's Republic of China (2020) Code for Acceptance of Construction Quality of Steel Structures. GB50205-2020. China Planning Press, Beijing

[18]

Ministry of Transport of the People's Republic of China (2022) Specification for manufacture and installation of highway steel bridge. JTG/T 3651—2022. China Communication Press, Beijing

[19]

Schnabel R, Möser S, Klein R (2008) Fast vector quantization for efficient rendering of compressed point-clouds. Comput Graph 32(2):246–259. https://doi.org/10.1016/j.cag.2008.01.014

[20]

Smith A, Sarlo R (2022) Automated extraction of structural beam lines and connections from point clouds of steel buildings. Comp-Aided Civ lnfrastruct Eng 37(1):110–125 https://doi.org/10.1111/MICE.12699

[21]

Tamai S, Yagata Y, Hosoya T (2002) New technologies in fabrication of steel bridges in Japan. J Construct steel Res 58:151–192. https://doi.org/10.1016/S0143-974X(01)00032-3

[22]

The AlSC Committee on Specifications (2010) Specification for Structural Steel Buildings. AISC 360-10-2010. AISC, Chicago

[23]

The American Association of State Highway and Transportation Officials (1992) Standard specifications for highway bridges. AASHTO. The Association General Offices, Washington

[24]

Turkan Y, Bosche F, Haas CT et al (2012) Automated progress tracking using 4D schedule and 3D sensing technologies. Autom Constr 22:414–421. https://doi.org/10.1016/j.autcon.2011.10.003

[25]

Wang YG, He XJ, He J, et al (2022) Virtual trial assembly of steel structure based on BIM platform. Autom Constr 141:104395. https://doi.org/10.1016/j.autcon.2022.104395

[26]

Wang Q, Kim MK (2019) Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018. Adv Eng Inform 39(February):306–319. https://doi.org/10.1016/j.aei.2019.02.007

[27]

Zhou XH, Liu JP, Cheng GZ et al (2021) Intelligent virtual trial assembly of large and complex steel arch bridges based on point cloud data. China J Highw Transp 34(11):1–9. https://doi.org/10.19721/j.cnki.1001-7372.2021.11.001

Funding

the 2023 Shaanxi Provincial Department of Transportation Research Project (23-47K)

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