Creation and application of a geometric digital twin model for construction of spatial cable structures: A case study

Siwei LIN , Liping DUAN , Jiming LIU , Ji MIAO , Hongmei LI , Jincheng ZHAO

ENG. Struct. Civ. Eng ››

PDF (9658KB)
ENG. Struct. Civ. Eng ›› DOI: 10.1007/s11709-026-1301-0
RESEARCH ARTICLE
Creation and application of a geometric digital twin model for construction of spatial cable structures: A case study
Author information +
History +
PDF (9658KB)

Abstract

This paper presents a dynamic geometric digital twin creation method for cable-net structures in construction which creates the building information model (BIM) for construction process visualization and finite element model (FEM) for cable shape and force prediction. The basic dynamic geometric information for parametric BIMs and FEMs is extracted from multi-stage laser scanning during construction. Multi-stage BIMs are presented as Industry Foundation Classes (IFC) format where the cables are created by approximating the curved shapes with straight segments. Complex components like cable clamps are imported as Revit Family and replicated at the measured locations. Multi-stage FEMs utilize the ABAQUS secondary development technology to parametrize geometric configuration, loads and constraints between components based on actual connections. A continuous dynamic BIM from one stage to another can be created based on construction progress and the deformed shapes derived from synchronously updated FEM calculations. Total duration for modeling and computation of each stage in construction is within 140 s. The average distances between the multi-stage BIMs and point clouds are within 15–20 mm. The prediction deviations of cable shapes and forces between FEM results and the measured data of the next stage are 1.2% and 1.5%, which considers temperature and construction progress.

Graphical abstract

Keywords

geometric digital twin / spatial cable structure / FEM / BIM / point cloud

Cite this article

Download citation ▾
Siwei LIN, Liping DUAN, Jiming LIU, Ji MIAO, Hongmei LI, Jincheng ZHAO. Creation and application of a geometric digital twin model for construction of spatial cable structures: A case study. ENG. Struct. Civ. Eng DOI:10.1007/s11709-026-1301-0

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Li H , Li L , Hu R , Ye M . Simplified design of nonlinear damper parameters and seismic responses for long-span cable-stayed bridges with nonlinear viscous dampers. Frontiers of Structural and Civil Engineering, 2024, 18(7): 1103–1116

[2]

Ou T , Zhu W , Lan C , Bai B , Bai S , Qiu Y , Liu R , Shen Q . Development and engineering application of fiber bragg grating intelligent cable in large-span hyperbolic parabolic spatial cable network. Structures, 2024, 68: 107196

[3]

Li Z , Xiang X , Wu T . Long short-term memory-enhanced semi-active control of cable vibrations with a magnetorheological damper. Frontiers of Structural and Civil Engineering, 2025, 19(2): 163–179

[4]

Wang L , Liu H , Zhang F , Guo L , Chen Z . Spatial structure digital twins: Application in intelligent health monitoring of cable dome structures. Automation in Construction, 2024, 165: 105489

[5]

Liu J , Duan L , Lin S , Miao J , Zhao J . Concept, creation, services and future directions of digital twins in the construction industry: A systematic literature review. Archives of Computational Methods in Engineering, 2024, 32: 319–342

[6]

Luo Q , Sun C , Li Y , Qi Z , Zhang G . Applications of digital twin technology in construction safety risk management: A literature review. Engineering, Construction, and Architectural Management, 2025, 32(6): 3587–3607

[7]

Jiang F , Ma L , Broyd T , Chen K . Digital twin and its implementations in the civil engineering sector. Automation in Construction, 2021, 130: 103838

[8]

Yoon S . Building digital twinning: Data, information, and models. Journal of Building Engineering, 2023, 76: 107021

[9]

Chiachío M , Megia M , Chiachio J , Fernandez J , Jalon M L . Structural digital twin framework: Formulation and technology integration. Automation in Construction, 2022, 140: 104333

[10]

Mohammadi M , Rashidi M , Yu Y , Samali B . Integration of TLS-derived Bridge Information Modeling (BrIM) with a Decision Support System (DSS) for digital twinning and asset management of bridge infrastructures. Computers in Industry, 2023, 147: 103881

[11]

Heng J , Dong Y , Lai L , Zhou Z , Frangopol D M . Digital twins-boosted intelligent maintenance of ageing bridge hangers exposed to coupled corrosion–fatigue deterioration. Automation in Construction, 2024, 167: 105697

[12]

Wang L , Liu H , Chen Z , Zhang F , Guo L . Combined digital twin and hierarchical deep learning approach for intelligent damage identification in cable dome structure. Engineering Structures, 2023, 274: 115172

[13]

Liu Z , Shi G , Liu Y , Sun Z , Zeng B , Wang J , Tafsirojjaman T . Investigation of mechanical behaviors of spoke-wheel cable structures through experimental and numerical analysis driven by digital-twin. Structures, 2024, 62: 106099

[14]

Shi G , Liu Z , Lu D , Zhang Q , Wang Z , Zhao Y . Digital twin-based model updating method for mechanical behaviors analysis of cable truss structure. Journal of Constructional Steel Research, 2024, 221: 108917

[15]

Liu Z , Zhang Z , Yuan C . An intelligent evaluation method for service safety of cable net structures under multiple factors. Sustainability, 2023, 15(21): 15633

[16]

Li Q W , Jiang P , Li H . Prognostics and health management of FAST cable-net structure based on digital twin technology. Research in Astronomy and Astrophysics, 2020, 20(5): 067

[17]

Eshaghi M S , Anitescu C , Thombre M , Wang Y , Zhuang X , Rabczuk T . Variational Physics-informed Neural Operator (VINO) for solving partial differential equations. Computer Methods in Applied Mechanics and Engineering, 2025, 437: 117785

[18]

Es-haghi M S , Anitescu C , Rabczuk T . Methods for enabling real-time analysis in digital twins: A literature review. Computers & Structures, 2024, 297: 107342

[19]

Eshaghi M S , Bamdad M , Anitescu C , Wang Y , Zhuang X , Rabczuk T . Applications of scientific machine learning for the analysis of functionally graded porous beams. Neurocomputing, 2025, 619: 129119

[20]

Eshaghi M SValizadeh NAnitescu CWang YZhuang XRabczuk T. Multi-head neural operator for modelling interfacial dynamics. 2025, arXiv: 2507.17763

[21]

Gu Y , Li W , Yao X , Liu G . Research on concrete structure defect repair based on three-dimensional printing. Frontiers of Structural and Civil Engineering, 2024, 18(5): 731–742

[22]

Mirzaei K , Arashpour M , Asadi E , Masoumi H , Bai Y , Behnood A . 3D point cloud data processing with machine learning for construction and infrastructure applications: A comprehensive review. Advanced Engineering Informatics, 2022, 51: 101501

[23]

Cui L , Zhou L , Xie Q , Liu J , Han B , Zhang T , Luo H . Direct generation of finite element mesh using 3D laser point cloud. Structures, 2023, 47: 1579–1594

[24]

Drobnyi V , Hu Z , Fathy Y , Brilakis I . Construction and maintenance of building geometric digital twins: State of the art review. Sensors, 2023, 23(9): 4382

[25]

Drobnyi V , Li S , Brilakis I . Digitization of existing buildings with arbitrary shaped spaces from point clouds. Journal of Computing in Civil Engineering, 2024, 38(5): 04024027

[26]

Mahmoud M , Chen W , Yang Y , Li Y . Automated BIM generation for large-scale indoor complex environments based on deep learning. Automation in Construction, 2024, 162: 105376

[27]

Xue F , Lu W , Chen K , Zetkulic A . From semantic segmentation to semantic registration: derivative-free optimization-based approach for automatic generation of semantically rich as-built building information models from 3D point clouds. Journal of Computing in Civil Engineering, 2019, 33(4): 04019024

[28]

Smith A , Sarlo R . Automated extraction of structural beam lines and connections from point clouds of steel buildings. Computer-Aided Civil and Infrastructure Engineering, 2022, 37(1): 110–125

[29]

Yang L , Cheng J C P , Wang Q . Semi-automated generation of parametric BIM for steel structures based on terrestrial laser scanning data. Automation in Construction, 2020, 112: 103037

[30]

Pan Y , Wang M , Lu L , Wei R , Cavazzi S , Peck M , Brilakis I . Scan-to-graph: Automatic generation and representation of highway geometric digital twins from point cloud data. Automation in Construction, 2024, 166: 105654

[31]

Lu R , Brilakis I . Digital twinning of existing reinforced concrete bridges from labelled point clusters. Automation in Construction, 2019, 105: 102837

[32]

Hu K , Han D , Qin G , Zhou Y , Chen L , Ying C , Guo T , Liu Y . Semi-automated generation of geometric digital twin for bridge based on terrestrial laser scanning data. Advances in Civil Engineering, 2023, 2023(1): 1–13

[33]

Mafipour M S , Vilgertshofer S , Borrmann A . Automated geometric digital twinning of bridges from segmented point clouds by parametric prototype models. Automation in Construction, 2023, 156: 105101

[34]

Shu J , Zeng Z , Li W , Zhou S , Zhang C , Xu C , Zhang H . Automatic geometric digital twin of box girder bridge using a laser-scanned point cloud. Automation in Construction, 2024, 168: 105781

[35]

Zhang A , Ma H , Zhao X , Zhang Y , Wang J , Su M . 3D laser scanning for automated structural modeling and deviation monitoring of multi-section prefabricated cable domes. Automation in Construction, 2024, 165: 105573

[36]

Zhang A , Wang J , Zhang Y , Shangguan G . Joint detection PCD-based method for automatic construction of geometric digital twin in cable dome structure. Engineering Structures, 2024, 320: 118908

[37]

Agapaki E , Brilakis I . CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets. Advanced Engineering Informatics, 2020, 45: 101121

[38]

Agapaki E , Brilakis I . Instance segmentation of industrial point cloud data. Journal of Computing in Civil Engineering, 2021, 35(6): 04021022

[39]

Lin S , Duan L , Liu J , Xiao X , Miao J , Zhao J . Automated geometric reconstruction and cable force inference for cable-net structures using 3D point clouds. Automation in Construction, 2024, 165: 105543

[40]

Krijnen T , Beetz J . An IFC schema extension and binary serialization format to efficiently integrate point cloud data into building models. Advanced Engineering Informatics, 2017, 33: 473–490

[41]

Pan Z , Yu Y , Xiao F , Zhang J . Recovering building information model from 2D drawings for mechanical, electrical and plumbing systems of ageing buildings. Automation in Construction, 2023, 152: 104914

[42]

Enferadi J , Shahi A . On the position analysis of a new spherical parallel robot with orientation applications. Robotics and Computer-integrated Manufacturing, 2016, 37: 151–161

[43]

Nagle-McNaughton T , Cox R . Measuring change using quantitative differencing of repeat structure-from-motion photogrammetry: The effect of storms on coastal boulder deposits. Remote Sensing, 2020, 12(1): 42

RIGHTS & PERMISSIONS

Higher Education Press

PDF (9658KB)

Supplementary files

Supplementary materials

513

Accesses

0

Citation

Detail

Sections
Recommended

/