Analysis and Forecasting the Severity of Construction Accidents using Artificial Neural Network

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 How to cite this article: Soltanzadeh A, Mohammadfam I, Mahmoudi Sh, Alizadeh Savareh B, Mohamadi Arani A. Analysis and Forecasting the Severity of Construction Accidents. J Saf Promot Inj Prev. 2016; 4(3): 185-92. References: 1. Fernandez-Muniz B, Montes-Peon JM, Vazquez-Ordas CJ. Relation Between Occupational Safety Management and Firm Performance. Safety Science. 2009; 47 (7):980-91. [Scopus] 2. Soltanzadeh A, Mohammadfam I, Moghimbeigi A, Akbarzadeh M, Ghiasvand R. Key Factors Contributing to Accident Severity Rate in Construction Industry in Iran: A Regression Modelling Approach. Arh Hig Rada Toksikol. 2016; 67 (1):47-53 . [pubmed] 3. Mahmoudi S, Ghasemi F, Mohammadfam I, Soleimani E. Framework for Continuous Assessment and Improvement of Occupational Health and Safety Issues in Construction Companies. Saf Health Work. 2014; 5 (3):125-30. [ pubmed ] 4. Mohammadfam I, Moghimbeigi A. Evaluation of Injuries Among a Manufacturing Industry Staff in Iran. J Res Health Sci. 2009; 9 (1):7-12. [pubmed] 5. Cheng C-W, Leu S-S, Cheng Y-M, Wu T-C, Lin C-C. Applying Data Mining Techniques to Explore Factors Contributing to Occupational Injuries in Taiwan's Construction Industry. Accid Anal Prev. 2012; 48 :214-22. [ pubmed ] 6. Yung P. Institutional Arrangements and Construction Safety in China: an Empirical Examination. Construction Management and Economics. 2009; 27 (5):439-50. [Scopus] 7. Manu PA, Ankrah NA, Proverbs DG, Suresh S. Investigating the Multi-Causal and Complex Nature of the Accident Causal Influence of Construction Project Features. Accid Anal Prev. 2012; 48 :126-33. [pubmed] 8. Hinze JW, Teizer J. Visibility-Related Fatalities Related to Construction Equipment. Safety Science. 2011; 49 (5):709-18. [scopus] 9. Soltanzadeh A, Mohammadfam I, Akbarzadeh M. Studying Disabling Occupational Accidents in the Construction Industry During Two Years. Journal of Occupational Hygiene Engineering. 2014; 1 (2):57-66. 10. Mohamadfam I, Soleimani E, Ghasemi F, Zamanparvar A. Comparison of Management Oversight and Risk Tree and Tripod-Beta In Excavation Accident Analysis. Jundishapur journal of health sciences. 2015; 7 (1):e23554. 11. Azadeh A, MohammadFam I. A Framework for Development of Integrated Intelligent Human Engineering Environment. Information Technology Journal. 2006; 5 (2):290-9. [Scopus] 12. Cho K, Hong T, Hyun C. Effect of Project Characteristics on Project Performance in Construction Projects Based on Structural Equation Model. Expert Systems with Applications. 2009; 36 (7):10461-70. [Scopus] 13. Fam IM, Nikoomaram H, Soltanian A. Comparative Analysis of Creative and Classic Training Methods in Health, Safety And Environment (HSE) Participation Improvement. Journal of Loss Prevention in the Process Industries. 2012; 25 (2):250-3. 14. Silvey AB, Officer HCQI. Introduction to Root Cause Analysis (RCA). 2010. 15. Mohammadfam I, Soltanzadeh A, Moghimbeigi A, Akbarzadeh M. Modeling of Individual and Organizational Factors Affecting Traumatic Occupational Injuries Based on the Structural Equation Modeling: A Case Study in Large Construction Industries. Archives of trauma research. 2016; 5 (3): e33595. [pubmed] 16. Mohammadfam I, Soltanzadeh A, Moghimbeigi A, Akbarzadeh M. Confirmatory factor analysis of occupational injuries: presenting an analytical tool. Trauma monthly. 2016(Inpress). 17. Mohammadfam I, Soltanzadeh A, Moghimbeigi A, Savareh BA. Analysis and Modeling of Threatening Factors of Workforce’s Health in Large-Scale Workplaces: Comparison of Four-Fitting Methods to select optimum technique. Electron Physician. 2016; 8 (2):1918. [pubmed] 18. Mohammadfam I, Soltanzadeh A, Moghimbeigi A, Savareh BA. Use of Artificial Neural Networks (ANNs) for the Analysis and Modeling of Factors That Affect Occupational Injuries in Large Construction Industries. Electronic Physician. 2015; 7 (7). [pubmed] 19. Gyekye SA, Salminen S, Ojajarvi A. A Theoretical Model to Ascertain Determinates of Occupational Accidents Among Ghanaian Industrial Workers. International Journal of Industrial Ergonomics. 2012; 42 (2):233-40. [Scopus] 20. Jakhar SK, Barua MK. An Integrated Model of Supply Chain Performance Evaluation and Decision-Making Using Structural Equation Modelling and Fuzzy AHP. Production Planning & Control. 2013(ahead-of-print):1-20. [Scopus] 21. Moghaddam FR, Afandizadeh S, Ziyadi M. Prediction of Accident Severity Using Artificial Neural Networks. International Journal of Civil Engineering. 2011; 9 (1):41-9. [Scopus] 22. Carrillo-Castrillo JA, Martin JG, de la Vega RG, Onieva L. Neural Network Application for Risk Factors Estimation in Manufacturing Accidents. Managing Complexity: Springer; 2014. p. 277-83.