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Data-driven safety enhancing strategies for risk networks in construction engineering

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  • Chen, Fangyu
  • Wang, Hongwei
  • Xu, Gangyan
  • Ji, Hongchang
  • Ding, Shanlei
  • Wei, Yongchang

Abstract

Risk management is crucial and indispensable to the success of projects, while identifying critical risks is the fundamental step in devising the corresponding safety measures. To fully exploit the value of richly accumulated accidental cases, this paper presents a data-driven research framework for proposing effective safety enhancing strategies based on risk networks in construction engineering, spanning the whole process from extracting accident chains from accidents to construct a risk network to devising safety measures. Aiming at the weighted heterogeneity of the risk network, both the performance metrics at network level and critical-risk identification metrics at node level are deliberately designed. These metrics then enable the proposing of a series of safety-enhancing strategies. In the case study, based on the accident-related data in China’s bridge-and-tunnel hybrid projects, different safety-enhancing strategies are compared through simulation experiments and analyzed to verify their effectiveness on optimizing costs and improving safety. Finally, based on results from simulations, relevant managerial suggestions are proposed.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:197:y:2020:i:c:s0951832019306659
    DOI: 10.1016/j.ress.2020.106806
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    References listed on IDEAS

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    Cited by:

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    3. Fatemeh Mostofi & Vedat Toğan & Yunus Emre Ayözen & Onur Behzat Tokdemir, 2022. "Construction Safety Risk Model with Construction Accident Network: A Graph Convolutional Network Approach," Sustainability, MDPI, vol. 14(23), pages 1-18, November.
    4. Lee, Junho & Kim, Ikjun & Kim, Hyomin & Kang, Juyoung, 2021. "SWOT-AHP analysis of the Korean satellite and space industry: Strategy recommendations for development," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    5. Ji, Chenyi & Su, Xing & Qin, Zhongfu & Nawaz, Ahsan, 2022. "Probability Analysis of Construction Risk based on Noisy-or Gate Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. Zhang, Hengqi & Geng, Hua, 2023. "A methodology to identify and assess high-risk causes for electrical personal accidents based on directed weighted CN," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    7. Wang, Ying & Zhang, Limao, 2021. "Simulation-based optimization for modeling and mitigating tunnel-induced damages," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    8. Wang, Wenhao & Wang, Yanhui & Wang, Guangxing & Li, Man & Jia, Limin, 2023. "Identification of the critical accident causative factors in the urban rail transit system by complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    9. Usama Issa & Muwaffaq Alqurashi & Ibrahim Salama, 2021. "Qualitative Analysis of Risks Affecting the Delivery of Land Surveying Project Activities," Sustainability, MDPI, vol. 13(22), pages 1-20, November.

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