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

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

31 Citations (Scopus)


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.

Original languageEnglish
Article number106806
JournalReliability Engineering and System Safety
Publication statusPublished - May 2020
Externally publishedYes


  • Construction engineering
  • Data-driven
  • Risk network
  • Safety enhancing strategies

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering


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