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Abstract
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Background and Objectives: The severity of industrial accidents is caused by various and different factors. This study aimed to analyze the causal factors of accidents severity and forecasting the severity of the accidents in construction industries.
Materials and Methods: This study was an analytical cross-sectional that analyzed and the forecasted the severity of accidents occurred during the years of 2009-2013 at largest construction industries in Iran. The data included information on 500 accidents causing human injury during the studied years. Data analyses were done using Artificial Neural Network, using Matlab R 2014. Ethical considerations in this study were adhered based on the Helsinki guidelines.
Results: The findings showed that, mean of age and education, Type of activity and number of workers in construction activities, health-safety-environment periodic training, content of health-safety-environment training and health-safety-environment training indicator and the hazard identification, risk assessment, safety audit and control measures such as personal-protective-equipment can be identified as indicators and Forecasting of accidents severity rate in the construction industry.
Conclusion: As results Artificial Neural Network can be used as a convenient tool to analyze and forecasting the causal layers of industrial accidents.
Key words: Forecasting, Accident Severity Rate, Construction Industry, Artificial Neural Network
Language: en |