USE OF ARTIFICIAL NEURAL NETWORKS (ANNS) FOR THE ANALYSIS AND MODELING OF FACTORS THAT AFFECT OCCUPATIONAL INJURIES IN LARGE CONSTRUCTION INDUSTRIES

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MOHAMMADFAM IRAJ; SOLTANZADEH AHMAD; MOGHIMBEIGI ABBAS; ALIZADEH SAVAREH BEHROUZ

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Journal: ELECTRONIC PHYSICIAN Year:2015 | Volume:7 | Issue:7 Page(s): 1515-1522

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Title

USE OF ARTIFICIAL NEURAL NETWORKS (ANNS) FOR THE ANALYSIS AND MODELING OF FACTORS THAT AFFECT OCCUPATIONAL INJURIES IN LARGE CONSTRUCTION INDUSTRIES

Author(s)

MOHAMMADFAM IRAJ | SOLTANZADEH AHMAD | MOGHIMBEIGI ABBAS | ALIZADEH SAVAREH BEHROUZ | Issue Writer Certificate

Keywords

WORKFORCE’S HEALTH

OCCUPATIONAL INJURY

ACCIDENT SEVERITY RATE (ASR)

ARTIFICIAL NEURAL NETWORKS (ANN)

ROUGH SET THEORY

Abstract

Introduction: Occupational injuries as a WORKFORCE’S HEALTH problem are very important in large-scale workplaces. Analysis and modeling the health-threatening factors are good ways to promote the WORKFORCE’S HEALTH and a fundamental step in developing health programs. The purpose of this study was ANN modeling of the severity of occupational injuries to determine the health-threatening factors and to introduce a model to predict the severity of occupational injuries.Methods: This analytical chain study was conducted in 10 large construction industries during a 10-year period (2005-2014). Nine hundred sixty occupational injuries were analyzed and modeled based on feature weighting by the ROUGH SET THEORY and artificial neural networks (ANNs). Two analytical software programs, i.e., RSES and MATLAB 2014 were used in the study.Results: The severity of occupational injuries was calculated as 557.47 ± 397.87 days. The findings of both models showed that the injuries' severity as a health problem resulted in various factors, including individual, organizational, health and safety (H& S) training, and risk management factors, which could be considered as causal and predictive factors of accident severity rate (ASR).Conclusion: The results indicated that ANNs were a reliable tool that can be used to analyze and model the severity of occupational injuries as one of the important health problems in large-scale workplaces. Additionally, the combination of rough set and ANNs is a good and proper chain approach to modeling the factors that threaten the health of workforces and other H& S problems.

Cites

ANALYSIS AND FORECASTING THE SEVERITY OF CONSTRUCTION ACCIDENTS USING ARTIFICIAL NEURAL NETWORK

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APA: Copy

MOHAMMADFAM, IRAJ, SOLTANZADEH, AHMAD, MOGHIMBEIGI, ABBAS, & ALIZADEH SAVAREH, BEHROUZ. (2015). USE OF ARTIFICIAL NEURAL NETWORKS (ANNS) FOR THE ANALYSIS AND MODELING OF FACTORS THAT AFFECT OCCUPATIONAL INJURIES IN LARGE CONSTRUCTION INDUSTRIES. ELECTRONIC PHYSICIAN, 7(7), 1515-1522. SID. https://sid.ir/paper/345397/en

Vancouver: Copy

MOHAMMADFAM IRAJ, SOLTANZADEH AHMAD, MOGHIMBEIGI ABBAS, ALIZADEH SAVAREH BEHROUZ. USE OF ARTIFICIAL NEURAL NETWORKS (ANNS) FOR THE ANALYSIS AND MODELING OF FACTORS THAT AFFECT OCCUPATIONAL INJURIES IN LARGE CONSTRUCTION INDUSTRIES. ELECTRONIC PHYSICIAN[Internet]. 2015;7(7):1515-1522. Available from: https://sid.ir/paper/345397/en

IEEE: Copy

IRAJ MOHAMMADFAM, AHMAD SOLTANZADEH, ABBAS MOGHIMBEIGI, and BEHROUZ ALIZADEH SAVAREH, “USE OF ARTIFICIAL NEURAL NETWORKS (ANNS) FOR THE ANALYSIS AND MODELING OF FACTORS THAT AFFECT OCCUPATIONAL INJURIES IN LARGE CONSTRUCTION INDUSTRIES,” ELECTRONIC PHYSICIAN, vol. 7, no. 7, pp. 1515–1522, 2015, [Online]. Available: https://sid.ir/paper/345397/en

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