Abstract

According to jab nature and inherent hazards, occupational accidents’ size in the construction industry is much severe than other industries. It is caused by different variables and factors. Therefore, this study has been done with the purpose of analytical modelling of occupational accidents’ size, as well as investigation the role of different risk management variables and factors on accidents’ severity in the big construction industries.

This analytical study was carried out on 500-human injuring accidents in the 10 big construction industries during 5-years (2009–2013). Data analysis and modelling were done based on feature selection by Pearson χ2 coefficient and SEM approach using statistical software of IBM SPSS AMOS v22.0.

Feature selection findings showed that 24 studied variables are qualified for accidents modelling. The results of the SEM preferred causation model showed that independent exogenous latent factors including IDF, OF, TF, WHIF, TAF, RMSF, and ATF and their indicator variables significantly affect occupational accidents’ size in the construction industries.

The findings indicated that combination of underlying indicator variables and different latent factors affect occupational accidents’ size and they have different role and impact on the severity of occupational accidents in the big construction industries. Therefore; SEM can be considered as an applicable and analytical approach which is applied to analyse and modelling occupational accidents severity, identification of accidents’ variables and factors, each part, their relation with each other, and also to prevent and reduce the severity of accidents.