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Realization of the physical to virtual connection for digital twin of construction crane

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Realization of the physical to virtual connection for digital twin of construction crane. / Yuan, E.; Yang, J.; Saafi, M. et al.
In: Journal of Industrial Information Integration, Vol. 44, 100779, 31.03.2025.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Yuan, E, Yang, J, Saafi, M, Wang, F & Ye, J 2025, 'Realization of the physical to virtual connection for digital twin of construction crane', Journal of Industrial Information Integration, vol. 44, 100779. https://doi.org/10.1016/j.jii.2025.100779

APA

Yuan, E., Yang, J., Saafi, M., Wang, F., & Ye, J. (2025). Realization of the physical to virtual connection for digital twin of construction crane. Journal of Industrial Information Integration, 44, Article 100779. https://doi.org/10.1016/j.jii.2025.100779

Vancouver

Yuan E, Yang J, Saafi M, Wang F, Ye J. Realization of the physical to virtual connection for digital twin of construction crane. Journal of Industrial Information Integration. 2025 Mar 31;44:100779. Epub 2025 Jan 13. doi: 10.1016/j.jii.2025.100779

Author

Yuan, E. ; Yang, J. ; Saafi, M. et al. / Realization of the physical to virtual connection for digital twin of construction crane. In: Journal of Industrial Information Integration. 2025 ; Vol. 44.

Bibtex

@article{26d0048fc7fe4bf2b5eda89a16ba7bdf,
title = "Realization of the physical to virtual connection for digital twin of construction crane",
abstract = "A digital twin is an integrated multi-physics representation of a complex physical entity. This article develops the physical-to-virtual connection of the digital twin and proposes a framework for the construction of a tower crane digital twin. The main contributions of this paper include development of tower crane monitoring dataset, tower crane detection and tower crane operation mode recognition. By annotating >20,000 tower crane images in 583 tower crane videos, a tower crane image recognition dataset and a tower crane operating mode dataset are established. Yolov5x algorithm is used in the tower crane detection, and the test set detection accuracy is 93.85 %. After comparing the LSTM and CNN algorithms, 3DResNet algorithm is selected for tower crane operational mode recognition. The dataset is augmented by rotating the image and the final recognition accuracy reaches 87 %. These models can be installed on CCTV to monitor operational status of tower crane in real time and transfer relevant information to the virtual model. The tower crane in the virtual space completes the action of the physical tower crane, thereby realizing the physical-to-virtual mapping in the digital twin.",
author = "E. Yuan and J. Yang and M. Saafi and F. Wang and J. Ye",
note = "Export Date: 22 January 2025",
year = "2025",
month = mar,
day = "31",
doi = "10.1016/j.jii.2025.100779",
language = "English",
volume = "44",
journal = "Journal of Industrial Information Integration",
issn = "2452-414X",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Realization of the physical to virtual connection for digital twin of construction crane

AU - Yuan, E.

AU - Yang, J.

AU - Saafi, M.

AU - Wang, F.

AU - Ye, J.

N1 - Export Date: 22 January 2025

PY - 2025/3/31

Y1 - 2025/3/31

N2 - A digital twin is an integrated multi-physics representation of a complex physical entity. This article develops the physical-to-virtual connection of the digital twin and proposes a framework for the construction of a tower crane digital twin. The main contributions of this paper include development of tower crane monitoring dataset, tower crane detection and tower crane operation mode recognition. By annotating >20,000 tower crane images in 583 tower crane videos, a tower crane image recognition dataset and a tower crane operating mode dataset are established. Yolov5x algorithm is used in the tower crane detection, and the test set detection accuracy is 93.85 %. After comparing the LSTM and CNN algorithms, 3DResNet algorithm is selected for tower crane operational mode recognition. The dataset is augmented by rotating the image and the final recognition accuracy reaches 87 %. These models can be installed on CCTV to monitor operational status of tower crane in real time and transfer relevant information to the virtual model. The tower crane in the virtual space completes the action of the physical tower crane, thereby realizing the physical-to-virtual mapping in the digital twin.

AB - A digital twin is an integrated multi-physics representation of a complex physical entity. This article develops the physical-to-virtual connection of the digital twin and proposes a framework for the construction of a tower crane digital twin. The main contributions of this paper include development of tower crane monitoring dataset, tower crane detection and tower crane operation mode recognition. By annotating >20,000 tower crane images in 583 tower crane videos, a tower crane image recognition dataset and a tower crane operating mode dataset are established. Yolov5x algorithm is used in the tower crane detection, and the test set detection accuracy is 93.85 %. After comparing the LSTM and CNN algorithms, 3DResNet algorithm is selected for tower crane operational mode recognition. The dataset is augmented by rotating the image and the final recognition accuracy reaches 87 %. These models can be installed on CCTV to monitor operational status of tower crane in real time and transfer relevant information to the virtual model. The tower crane in the virtual space completes the action of the physical tower crane, thereby realizing the physical-to-virtual mapping in the digital twin.

U2 - 10.1016/j.jii.2025.100779

DO - 10.1016/j.jii.2025.100779

M3 - Journal article

VL - 44

JO - Journal of Industrial Information Integration

JF - Journal of Industrial Information Integration

SN - 2452-414X

M1 - 100779

ER -