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Digital Twin-Based Intelligent Safety Risks Prediction of Prefabricated Construction Hoisting

Author

Listed:
  • Zhan-Sheng Liu

    (Department of Urban Construction, Beijing University of Technology, Beijing 100124, China)

  • Xin-Tong Meng

    (Department of Urban Construction, Beijing University of Technology, Beijing 100124, China)

  • Ze-Zhong Xing

    (Department of Urban Construction, Beijing University of Technology, Beijing 100124, China)

  • Cun-Fa Cao

    (Department of Urban Construction, Beijing University of Technology, Beijing 100124, China)

  • Yue-Yue Jiao

    (Department of Urban Construction, Beijing University of Technology, Beijing 100124, China)

  • An-Xiu Li

    (Department of Urban Construction, Beijing University of Technology, Beijing 100124, China)

Abstract

Prefabricated construction hoisting has one of the highest rates of fatalities and injuries compared to other construction processes, despite technological advancements and implementations of safety initiatives. Current safety risk management frameworks lack tools that are able to process in-situ data efficiently and predict risk in advance, which makes it difficult to guarantee the safety of hoisting. Thus, this article proposed an intelligent safety risk prediction framework of prefabricated construction hoisting. It can predict the hoisting risk in real-time and investigate the spatial-temporal evolution law of the risk. Firstly, the multi-dimensional and multi-scale Digital Twin model is built by collecting the hoisting information. Secondly, a Digital Twin-Support Vector Machine (DT-SVM) algorithm is proposed to process the data stored in the virtual model and collected on the site. A case study of a prefabricated construction project reveals its prediction function and deduces the spatial-temporal evolution law of hoisting risk. The proposed method has made advancements in improving the safety management level of prefabricated hoisting. Moreover, the proposed method is able to identify the deficiencies regarding digital-twin-level control methods, which can be improved towards automatic controls in future studies.

Suggested Citation

  • Zhan-Sheng Liu & Xin-Tong Meng & Ze-Zhong Xing & Cun-Fa Cao & Yue-Yue Jiao & An-Xiu Li, 2022. "Digital Twin-Based Intelligent Safety Risks Prediction of Prefabricated Construction Hoisting," Sustainability, MDPI, vol. 14(9), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5179-:d:801714
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    References listed on IDEAS

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    1. Wu, Xianguo & Liu, Huitao & Zhang, Limao & Skibniewski, Miroslaw J. & Deng, Qianli & Teng, Jiaying, 2015. "A dynamic Bayesian network based approach to safety decision support in tunnel construction," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 157-168.
    2. Huazan Liu & Yukang He & Qichao Hu & Jianfei Guo & Lan Luo, 2020. "Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-15, July.
    3. Zhang, Limao & Wu, Xianguo & Skibniewski, Miroslaw J. & Zhong, Jingbing & Lu, Yujie, 2014. "Bayesian-network-based safety risk analysis in construction projects," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 29-39.
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    Cited by:

    1. Hossein Omrany & Karam M. Al-Obaidi & Amreen Husain & Amirhosein Ghaffarianhoseini, 2023. "Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions," Sustainability, MDPI, vol. 15(14), pages 1-26, July.

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