IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i9p5179-d801714.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/9/5179/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/9/5179/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pan, Yue & Ou, Shenwei & Zhang, Limao & Zhang, Wenjing & Wu, Xianguo & Li, Heng, 2019. "Modeling risks in dependent systems: A Copula-Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 416-431.
    2. Wang, Fan & Li, Heng & Dong, Chao & Ding, Lieyun, 2019. "Knowledge representation using non-parametric Bayesian networks for tunneling risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    3. Guo, Qingjun & Amin, Shohel & Hao, Qianwen & Haas, Olivier, 2020. "Resilience assessment of safety system at subway construction sites applying analytic network process and extension cloud models," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    4. Zhou, Ying & Li, Chenshuang & Zhou, Cheng & Luo, Hanbin, 2018. "Using Bayesian network for safety risk analysis of diaphragm wall deflection based on field data," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 152-167.
    5. Lin, Song-Shun & Shen, Shui-Long & Zhou, Annan & Xu, Ye-Shuang, 2021. "Novel model for risk identification during karst excavation," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    6. Albert P. C. Chan & Francis K. W. Wong & Carol K. H. Hon & Tracy N. Y. Choi, 2018. "A Bayesian Network Model for Reducing Accident Rates of Electrical and Mechanical (E&M) Work," IJERPH, MDPI, vol. 15(11), pages 1-19, November.
    7. Xiaoyan Jiang & Sai Wang & Jie Wang & Sainan Lyu & Martin Skitmore, 2020. "A Decision Method for Construction Safety Risk Management Based on Ontology and Improved CBR: Example of a Subway Project," IJERPH, MDPI, vol. 17(11), pages 1-23, June.
    8. Sang-Guk Yum & Sungjin Ahn & Junseo Bae & Ji-Myong Kim, 2020. "Assessing the Risk of Natural Disaster-Induced Losses to Tunnel-Construction Projects Using Empirical Financial-Loss Data from South Korea," Sustainability, MDPI, vol. 12(19), pages 1-15, September.
    9. Junlong Peng & Jing Zhou & Fanyi Meng & Yan Yu, 2021. "Analysis on the hidden cost of prefabricated buildings based on FISM-BN," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    10. Hieu T. T. L. Pham & Mahdi Rafieizonooz & SangUk Han & Dong-Eun Lee, 2021. "Current Status and Future Directions of Deep Learning Applications for Safety Management in Construction," Sustainability, MDPI, vol. 13(24), pages 1-37, December.
    11. Yu, Shui & Wang, Zhonglai & Zhang, Kewang, 2018. "Sequential time-dependent reliability analysis for the lower extremity exoskeleton under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 45-52.
    12. Rasoul Amirzadeh & Dhananjay Thiruvady & Asef Nazari & Mong Shan Ee, 2023. "Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships," Papers 2306.08157, arXiv.org, revised Oct 2024.
    13. Song, Haifeng & Liu, Jieyu & Schnieder, Eckehard, 2017. "Validation, verification and evaluation of a Train to Train Distance Measurement System by means of Colored Petri Nets," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 10-23.
    14. Xue, Jie & Yip, Tsz Leung & Wu, Bing & Wu, Chaozhong & van Gelder, P.H.A.J.M., 2021. "A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China," Renewable Energy, Elsevier, vol. 172(C), pages 897-917.
    15. Shengyu Guo & Jiali He & Jichao Li & Bing Tang, 2019. "Exploring the Impact of Unsafe Behaviors on Building Construction Accidents Using a Bayesian Network," IJERPH, MDPI, vol. 17(1), pages 1-15, December.
    16. Liu, Wenli & Li, Ang & Fang, Weili & Love, Peter E.D. & Hartmann, Timo & Luo, Hanbin, 2023. "A hybrid data-driven model for geotechnical reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    17. Libiao Bai & Kanyin Zheng & Zhiguo Wang & Jiale Liu, 2022. "Service provider portfolio selection for project management using a BP neural network," Annals of Operations Research, Springer, vol. 308(1), pages 41-62, January.
    18. Ali Namazian & Siamak Haji Yakhchali & Vahidreza Yousefi & Jolanta Tamošaitienė, 2019. "Combining Monte Carlo Simulation and Bayesian Networks Methods for Assessing Completion Time of Projects under Risk," IJERPH, MDPI, vol. 16(24), pages 1-19, December.
    19. Zhang, Xiaoge & Mahadevan, Sankaran, 2021. "Bayesian network modeling of accident investigation reports for aviation safety assessment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    20. Ünsal-Altuncan, Izel & Vanhoucke, Mario, 2024. "A hybrid forecasting model to predict the duration and cost performance of projects with Bayesian Networks," European Journal of Operational Research, Elsevier, vol. 315(2), pages 511-527.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5179-:d:801714. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.