Sun, Jan 26, 2025
[Archive]
.png)
Volume 5, Issue 3 (9-2019)
jhehp 2019, 5(3): 121-126 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Alizadeh Savareh B, Mahdinia M, Ghiyasi S, Rahimi J, Soltanzadeh A. Accident Modeling in Small-scale Construction Projects Based on Artificial Neural Networks. jhehp 2019; 5 (3) :121-126
URL: http://jhehp.zums.ac.ir/article-1-216-en.html
URL: http://jhehp.zums.ac.ir/article-1-216-en.html
Behrouz Alizadeh Savareh1 
, Mohsen Mahdinia2 , Samira Ghiyasi3 , Jamshid Rahimi4 , Ahmad Soltanzadeh * 2
, Mohsen Mahdinia2 , Samira Ghiyasi3 , Jamshid Rahimi4 , Ahmad Soltanzadeh * 2
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.
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: (10291 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.
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.
Type of Study: Original Article |
Subject:
Occupational and Industrial Health
Received: 2019/06/8 | Accepted: 2019/08/26 | Published: 2019/09/21
Received: 2019/06/8 | Accepted: 2019/08/26 | Published: 2019/09/21
References
1. Farshadnia SM, Arghami S, Shahab SA, Majidi F. A Budgeting Model for the Safety Unit of an Under ConstructionMetro Station in Tehran Using a Robust Optimization. J Hum Environ Health Promot, 2018; 4(3): 131-7. [Crossref]
2. Soltanzadeh A, Mohammadfam I, Moghimbeygi A, Ghiasvand R . Exploring Causal Factors on the Severity Rate of Occupational Accidents in Construction Worksites. Int J Civ Eng, 2017; 15(7): 959-65. [Crossref]
3. Hinze JW, Teizer J. Visibility-Related Fatalities Related to Construction Equipment. Saf Sci. 2011; 49(5): 709-18. [Crossref]
4. Kines P, Spangenberg S, Dyreborg J. Dyreborg, Prioritizing Occupational Injury Prevention in the Construction Industry: Injury Severity or Absence? J Saf Res, 2007; 38(1): 53-8. [Crossref]
5. Mohammadfam I, Soltanzadeh A, Mahmoudi S, Moghimbeigi A. P154 Analytical Modelling of Occupational Accidents’ Size Using Structural Equation Modelling approach (SEM); A Field Study in Big Construction Industries. BMJ Publishing Group Ltd . 2016; 73: A172. [Crossref]
6. Soltanzadeh A, Mohammadfam I, Moghimbeigi A, 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. [Crossref]
7. Saffarzadeh M, Pooryari M. Accident Prediction Model Based on Traffic and Geometric Design Characteristics. Int J Civ Eng, 2005; 3(2): 112-9.
8. Sari M, Selcuk AS, Karpuz C, Duzgun HS. Stochastic Modeling of Accident Risks Associated with an Underground Coal Mine in Turkey. Saf Sci. 2009; 47(1): 78-87. [Crossref]
9. Venkataraman N. Safety Performance Factor. Int J Occup Saf Ergon, 2008; 14(3): 327-31. [Crossref]
10. Carrillo Castrillo JA, Trillo Cabello AF, Rubio Romero JC. Construction Accidents: Identification of the Main Associations between Causes, Mechanisms and Stages of the Construction Process. Int J Occup Saf Ergon. 2017; 23(2): 240-50. [Crossref]
11. 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): 1918-26. [Crossref]
12. 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. Arch Trauma Res. 2016; 5(3): e33595. [Crossref]
13. Chong HY, Low TS. Accidents in Malaysian Construction Industry: Statistical Data and Court CasesInt. J Occup Saf Ergon, 2014; 20(3): 503-13. [Crossref]
14. Mitropoulos P, Abdelhamid TS, Howell GA. Systems Model of Construction Accident Causation. J Constr Eng Manag. 2005; 131(7): 816-25. [Crossref]
15. Moghaddam FR, Afandizadeh S, Ziyadi M. Prediction of Accident Severity Using Artificial Neural Networks. Int J Civ Eng, 2011; 9(1): 41-8.
16. OSHA. Safety and Health Managment System eTool. OSHA. 2012.
17. Hinrichs A, Novak E, Ullrich M, Woźniakowski H. The Curse of Dimensionality for Numerical Integration of Smooth Functions. Math Comput. 2014; 83(290): 2853-63. [Crossref]
18. Biesiada J, Duch W. Feature Selection for High-Dimensional Data—A Pearson Redundancy Based Filter, InComputer Recognition Systems 2. Springerlink. 2007: 242-9. [Crossref]
19. Awodele O, Jegede O. Neural Networks and Its Application in Engineering. Sci IT, 2009: 83-95. [Crossref]
20. Czarnecki WM, Osindero S, Jaderberg M, Swirszcz G, Pascanu R. Sobolev Training for Neural Networks. arXiv Preprint arXiv:1706.04859; 2017.
21. Berry MJ, Linoff GS. Data Mining Techniques: for Marketing, Sales, and Customer Relationship Management. 2nd ed. USA: John Wiley & Sons; 2011.
22. Soltanzadeh A, Mohammadfam I, Moghimbeygi A, Ghiasvand R. Exploring Causal Factors on the Severity Rate of Occupational Accidents in Construction Worksites. Int J Civ Eng, 2017; 15(7): 959-65. [Crossref]
23. Mohammadfam I, Soltanzadeh A, Moghimbeigi A, Akbarzadeh M. Confirmatory Factor Analysis of Occupational Injuries: Presenting an Analytical Tool. Trauma Mon, 2017; 22(2): e33266. [Crossref]
24. 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. Electron Physician, 2015; 7(7): 1515-22. [Crossref]
25. Soltanzadeh A, Mohammadfam I, Moghimbeigi A. P153 Predicting and Determining Factors of Occupational Accidents Severity Rate (ASR) Using Artificial Neural Networks (ANN); A Case Study in Construction Industry. BMJ Publishing Group Ltd. 2016; 73(1):A171-2. [Crossref]
26. Piranveyseh P, Motamedzade M, Osatuke K, Mohammadfam I, Moghimbeigi A, Soltanzadeh A, et al. Association between Psychosocial, Organizational and Personal Factors and Prevalence of Musculoskeletal Disorders in Office Workers. Int J Occup Saf Ergon. 2016; 22(2): 267-73. [Crossref]
27. Samadi H, Rostami M, Bakhshi E, Garosi E, Kalantari R. Can Educational Intervention be Useful in Improvement of Body Posture and Work Related Musculoskeletal Symptoms? J Hum Environ Health Promot. 2018; 4(2): 81-6. [Crossref]
28. Alizadeh SS, Mortazavi SB, Mehdi Sepehri M. Assessment of Accident Severity in the Construction Industry Using the Bayesian Theorem. Int J Occup Saf Ergon. 2015; 21(4): 551-7. [Crossref]
29. Seifi Azad Mard HR, Estiri A, Hadadi P, Seifi Azad Mard M. Occupational Risk Assessment in the Construction Industry in Iran. Int J Occup Saf Ergon. 2017; 23(4): 570-7. [Crossref]
30. Baratchi M, Mansouri N, Ahmadi A. Hazard Assessment Matrix; Results of a Delphi Study. J Hum Environ Health Promot. 2018; 4(3): 121-5. [Crossref]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
© 2024 The Author(s)
