IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i1d10.1007_s13198-021-01574-1.html
   My bibliography  Save this article

Network mining techniques to analyze the risk of the occupational accident via bayesian network

Author

Listed:
  • Nihar Ranjan Nayak

    (Sri Venkateswara College of Engineering Technology)

  • Sumit Kumar

    (Indian Institute of Management)

  • Deepak Gupta

    (Institute of Technology and Management)

  • Ashish Suri

    (Shri Mata Vaishno Devi University)

  • Mohd Naved

    (Jagannath University)

  • Mukesh Soni

    (Senior IEEE Member)

Abstract

Today, as the construction industry grows, the frequency of occupational accidents has risen as well. The advancement of technology, inadequacies in workplace safety procedures, and untrained workers are the primary causes of these workplace mishaps. In this research, occupational accident data were preprocessed and then subjected to univariate frequency and cross-tabulation analysis. As a consequence of the research, risk factors for occupational accidents were identified. Then, using Bayesian networks, the impacts of these factors on occupational accidents were examined (BNs). A Bayesian network is a graphical model that captures the conditional dependencies between variables. On a dataset from an international construction firm, the Bayesian network was deployed. Finally, we evaluated the correctness of the constructed Bayesian network and other performance criteria, as well as the model's efficacy. The experimental findings indicate that utilizing machine learning methods, some occupational accident situations may be predicted with great accuracy. The main aim of the paper is to aims to get rid of the repetitive patterns in the data and present a more reasonable level of data for the classification analysis.

Suggested Citation

  • Nihar Ranjan Nayak & Sumit Kumar & Deepak Gupta & Ashish Suri & Mohd Naved & Mukesh Soni, 2022. "Network mining techniques to analyze the risk of the occupational accident via bayesian network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 633-641, March.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01574-1
    DOI: 10.1007/s13198-021-01574-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01574-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-021-01574-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. 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).
    10. 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).
    11. 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.
    12. Qi Yuan & Hongqinq Zhu & Xiaolei Zhang & Baozhen Zhang & Xingkai Zhang, 2022. "An Integrated Quantitative Risk Assessment Method for Underground Engineering Fires," IJERPH, MDPI, vol. 19(24), pages 1-26, December.
    13. 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).
    14. 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.
    15. Shen, Shui-Long & Lin, Song-Shun & Zhou, Annan, 2023. "A cloud model-based approach for risk analysis of excavation system," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    16. Crispim, José & Fernandes, Jorge & Rego, Nazaré, 2020. "Customized risk assessment in military shipbuilding," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    17. Dongye Sun & Yuanhua Jia & Lingqiao Qin & Yang Yang & Juyong Zhang, 2018. "A Variance Maximization Based Weight Optimization Method for Railway Transportation Safety Performance Measurement," Sustainability, MDPI, vol. 10(8), pages 1-13, August.
    18. Chen, Fangyu & Wang, Hongwei & Xu, Gangyan & Ji, Hongchang & Ding, Shanlei & Wei, Yongchang, 2020. "Data-driven safety enhancing strategies for risk networks in construction engineering," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    19. Mohammad Yazdi, 2019. "A review paper to examine the validity of Bayesian network to build rational consensus in subjective probabilistic failure analysis," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(1), pages 1-18, February.
    20. Cai, Baoping & Liu, Yu & Fan, Qian, 2016. "A multiphase dynamic Bayesian networks methodology for the determination of safety integrity levels," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 105-115.

    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:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01574-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.