Assessment of accident severity in the construction industry using the Bayesian theorem

• Autorzy: Alizadeh S. S. , Mortazavi S. B. , Sepehri M. M.
• Języki publikacji: EN

Abstrakty

EN

Aim: Construction is a major source of employment in many countries. In construction, workers perform a great diversity of activities, each one with a specific associated risk. The aim of this paper is to identify workers who are at risk of accidents with severe consequences and classify these workers to determine appropriate control measures. Methods: We defined 48 groups of workers and used the Bayesian theorem to estimate posterior probabilities about the severity of accidents at the level of individuals in construction sector. First, the posterior probabilities of injuries based on four variables were provided. Then the probabilities of injury for 48 groups of workers were determined. Results: With regard to marginal frequency of injury, slight injury (0.856), fatal injury (0.086) and severe injury (0.058) had the highest probability of occurrence. It was observed that workers with <1 year's work experience (0.168) had the highest probability of injury occurrence. The first group of workers, who were extensively exposed to risk of severe and fatal accidents, involved workers ≥50 years old, married, with 1–5 years' work experience, who had no past accident experience. Conclusion: The findings provide a direction for more effective safety strategies and occupational accident prevention and emergency programmes.

Słowa kluczowe

EN

construction sector; Bayesian theory; accident consequences; posterior probabilities

PL

budownictwo; czynniki ryzyka; analiza prawdopodobieństwa; badanie wypadków

• Wydawca: Taylor & Francis
• Czasopismo: International Journal of Occupational Safety and Ergonomics
• Rocznik: 2015
• Tom: Vol. 21, No. 4
• Strony: 551--557
• Opis fizyczny: Bibliogr. 36 poz.

Twórcy

autor

Alizadeh S. S.

• Tabriz University of Medical Sciences, Iran

autor

Mortazavi S. B.

• Tarbiat Modares University, Iran

autor

Sepehri M. M.

• Tarbiat Modares University, Iran

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.