Intelligent Monitoring Method of Assembled PC Components
Hoisting in Place based on Three-dimensional Trajectory

Kang Li*

doi:10.70711/frim.v3i7.6825

Intelligent Monitoring Method of Assembled PC Components Hoisting in Place based on Three-dimensional Trajectory

Kang Li*

Abstract

To achieve more efficient and safer posture monitoring during the lifting and lowering process of prefabricated precast concrete
(PC) components, and to address the low efficiency of existing monitoring methods, this study proposes an intelligent monitoring method
combining binocular vision with object detection. The method first designs artificial target points for coordinate transformation. It employs a
YOLOv8 model with an elliptical detection fusion and replaces the backbone network with Dconv2 to identify the targets and their centroids.
Subsequently, a 3D reconstruction-based coordinate transformation is used to estimate the posture of the PC components. The results show
that the average recognition accuracy of each artificial target point exceeds 99%, with alignment detection errors all within 5 mm. Both model
recognition and posture estimation perform well, meeting practical requirements overall. These findings provide a valuable reference for realizing full-process posture monitoring of prefabricated PC component lifting operations.

Keywords

Prefabricated PC components; Attitude monitoring; Binocular vision; Three-dimensional reconstruction; Attitude estimation

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References

[1] Guiding opinions of the General Office of the State Council on vigorously developing prefabricated buildings[J]. Building Design Management, 2016, 33(11): 40-42.

[2] Yang Z, Junjie X. Research and Application of Intelligent Construction in Prefabricated Building Construction[J].Sustainability in Environment, 2025, 10(1):

[3] Yin J, Huang R, Sun H, et al. Multi-objective optimization for coordinated production and transportation in prefabricated construction

with on-site lifting requirements[J]. Computers & Industrial Engineering, 2024, 189110017-.

[4] Zhe S, Zhufu Z, Ruoxin X, et al. Dynamic human systems risk prognosis and control of lifting operations during prefabricated building

construction[J]. Developments in the Built Environment, 2023, 14.

[5] Ning Y X, Zhang K, Jiang N, et al. 3D deformation analysis for earth dam monitoring based on terrestrial laser scanning (TLS) and the

iterative closest point (ICP) algorithm[J]. Frontiers in Earth Science, 2024, 121421705-1421705.

[6] Piekarczuk A, Mazurek A, Szer J, et al. A Case Study of 3D Scanning Techniques in Civil Engineering Using the Terrestrial Laser Scanning Technique[J]. Buildings, 2024, 14(12):3703-3703.

[7] Prasetyo Y, Ashar M W, Hadi F, et al. Selogriyo Temple Deformation Mapping in Three Dimensions (3D) Multitemporal Using Terrestrial Laser Scanner (TLS) Technology[J]. IOP Conference Series: Earth and Environmental Science, 2024, 1418(1):012022-012022.

[8] J. Wang, W.Z. Sun, W.C. Shou, X.Y. Wang, C.Z. Wu, H.Y. Chong, Y. Liu, C.F. Sun, Integrating BIM and LiDAR for real-time construction quality control, J. Intell. Robot. Syst.79(34)(2015) 417432.

[9] Cheng Y, Lin F, Wang W, et al. Vision-based trajectory monitoring for assembly alignment of precast concrete bridge components[J].

Automation in Construction, 2022, 140: 104350.

[10] Alzubaidi L, Zhang J, Humaidi A J, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future

directions[J]. Journal of big Data, 2021, 8: 1-74.

[11] Li Z, Hu Y, Salzmann M, et al. SD-pose: Semantic decomposition for cross-domain 6D object pose estimation[C]//Proceedings of the

AAAI Conference on Artificial Intelligence. 2021, 35(3): 2020-2028.

[12] Wu H, Wang J, Nan D, et al. Fault Location and Fault Cause Identification Method for Transmission Lines Based on Pose Normalized

Multi-Output Convolutional Nets[J]. IEEE Transactions on Instrumentation and Measurement, 2024.

[13] Q. Wang, M.K. Kim, J.C.P. Cheng, H. Sohn, Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning, Autom. Constr. 68 (2016) 170182.

[14] D. Rebolj, Z. Pu?cko, N.?C. Babi?c, M. Bizjak, D. Mongus, Point cloud quality requirements for scan-vs-BIM based automated construction progress monitoring, Autom. Constr. 84 (2017) 323334.

[15] Yue Z, Huang L, Lin Y, et al.Research on image deformation monitoring algorithm based on binocular vision[J]. Measurement, 2024,

228114394-.

[16] Meng B, Shi W. Small traffic sign recognition method based on improved YOLOv7[J]. Scientific Reports, 2025, 15(1):5482-5482.

[17] Quach D L, Quoc N K, Quynh N A, et al. Evaluating the Effectiveness of YOLO Models in Different Sized Object Detection and Feature-Based Classification of Small Objects[J].Journal of Advances in Information Technology, 2023, 14(5):907-917.

DOI: http://dx.doi.org/10.70711/frim.v3i7.6825

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