Data-driven safety enhancing strategies for risk networks in construction engineering. (May 2020)

• Record Type: Journal Article
• Authors: Chen, Fangyu; Wang, Hongwei; Xu, Gangyan; Ji, Hongchang; Ding, Shanlei; Wei, Yongchang
• Is Part Of: Reliability engineering & system safety. Volume 197(2020)
• Publication Date: 2020-05
• Subjects: Safety enhancing strategies -- Risk network -- Data-driven -- Construction engineering; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2020.106806
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A data-driven framework for designing safety-enhancing strategies is proposed. The metrics for identifying critical risk factors are designed. The risk attributes of bridge-tunnel hybrid construction projects are analyzed. The effectiveness of safety-enhancing strategies is verified. Critical conclusions and corresponding managerial suggestions are offered. 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.