Prioritization of risk mitigation strategies for contact with sharp object accidents using hybrid bow-tie approach

Dilşen Kuzucuoğlu, Kerim Koc*, Gokhan Kazar, Onur Behzat Tokdemir

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2 Atıf (Scopus)


Occupational accidents are still a priority for the construction industry and safety professionals. Appropriate and proactive risk management strategies for complex construction safety management issues are essential to transform the “if-it-breaks-fix-it” approach into “fix-it-so-as-not-to-break.” This study introduces a novel hybrid bow-tie (H-BT) model to identify the riskiest causal paths and the most effective protective and preventive strategies. A dataset comprising 829 “contact-with-sharp-object” (CwSO) injury cases collected from 63 projects carried out by three companies is used to define the pairwise relationships among risk factors and define the risk paths using a mutual information matrix. A Delphi analysis is conducted to achieve the final strategies and calculate the risk reduction effect (ΔRR) and cost of each strategy. Finally, protective and preventive strategies are prioritized according to the ΔRR/cost ratio. The findings show that to reduce the number and severity of CwSO accidents, “stop working authority” and “staffing for safety” are the most effective preventative and protective strategies, respectively. Besides, inadequate supervision/management leadership is found to be the originating risk factor in many of the risk paths. This study incorporates mutual information between risk factors, BT, and preventive and protective barriers in the same, easy-to-understand visual model to examine a particular injury type, which is the primary contribution to theory. Overall, the risk assessment model developed is expected to improve the effectiveness of safety management applications in construction sites and eventually contribute to the minimization of incidents in construction projects.

Orijinal dilİngilizce
Makale numarası106248
DergiSafety Science
Yayın durumuYayınlandı - Eki 2023

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Publisher Copyright:
© 2023 Elsevier Ltd

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