Volume 5, Issue 3 (9-2019)                   J. Hum. Environ. Health Promot 2019, 5(3): 121-126 | Back to browse issues page

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Alizadeh Savareh B, Mahdinia M, Ghiyasi S, Rahimi J, Soltanzadeh A. Accident Modeling in Small-scale Construction Projects Based on Artificial Neural Networks. J. Hum. Environ. Health Promot. 2019; 5 (3) :121-126
URL: http://zums.ac.ir/jhehp/article-1-216-en.html
1- Department of Medical Informatics, School of Management and Medical Education, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2- Department of Occupational Safety & Health Engineering, Health School and Research Center for Environmental Pollutants, Qom University of Medical Sciences, Qom, Iran.
3- Department of Environmental Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
4- Department of Occupational Safety & Health Engineering, Health School, Alborz University of Medical Sciences, Karaj, Iran.
Abstract:   (943 Views)
Background: Several factors contribute to accidents in small-scale construction projects (SSCPs). The present study aimed to assess the influential factors in SSCP accidents and introduce a model to predict their frequency.
Methods: In total, 38 SSCPs were within the scope of this investigation. The safety index of accident frequency rate (AFR) causing 452 injury construction accidents during 12 years (2007-2018) was analyzed and modeled. Data analysis was performed based on feature selection using Pearson's χ2 coefficient and SPSS modeler, as well as the artificial neural networks (ANNs) in MATLAB software.
Results: Mean AFR was estimated at 26.32 ± 14.83, and the results of both approaches revealed that individual factors, organizational factors, training factors, and risk management-related factors could predict the AFR involved in SSCPs.
Conclusion: The findings of this research could be reliably applied in the decision-making regarding safety and health construction issues. Furthermore, Pearson's correlation-coefficient and ANN modeling are considered to be reliable tools for accident modeling in SSCPs.
Full-Text [PDF 790 kb]   (292 Downloads)    
Type of Study: Research Article | Subject: Occupational and Industrial Health
Received: 2019/06/8 | Accepted: 2019/08/26 | Published: 2019/09/21

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