Volume 21, Issue 2 (2025)
ioh 2025, 21(2): 52-72 |
Back to browse issues page
Research code: مقاله حاصل از پایانامه کارشناسی ارشد hse موسسه عالی انرژ
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
zokaie M, abbasi M, falahati M, abbasi J, zand salimi F, Zarie M. Safety risk assessment by creating a combined model of FMEA, FTA and AHP fuzzy methods Case study: steel industry exploitation phase. ioh 2025; 21 (2) :52-72
URL: http://ioh.iums.ac.ir/article-1-3676-en.html
URL: http://ioh.iums.ac.ir/article-1-3676-en.html
Mojtaba Zokaie 
, Milad Abbasi , Mohsen Falahati * , Jalal Abbasi , Fardin Zand salimi , Mohamad Zarie
, Milad Abbasi , Mohsen Falahati * , Jalal Abbasi , Fardin Zand salimi , Mohamad Zarie
Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran , falahati.mohsen64@gmail.com
Abstract: (1477 Views)
Background and aims: One of the main factors in occupational health and safety management is the formulation and implementation of risk assessment. Applying a precise and appropriate approach in the implementation of risk assessment plays an important role in the organization's risk management.
Methods: In this research, five incidents were selected as the most important incidents in the exploitation phase of the steel industry. Estimating the probability of occurrence and discovering the root causes using fuzzy FTA was calculated and the severity of the consequence of the risk of accidents among 14 industry experts was collected and analyzed. Finally, the variables obtained in the previous steps were considered as inputs to the fuzzy inference system designed in MATLAB, and the final value of RPN was calculated using defined if-then rules.
Result: According to the obtained results, the probability of falling from a height, falling of objects, and electrocution were higher than other accidents, respectively. Finally, the risk of falling from a height and electrocution with RPN values of 0.665 and 0.563 are in the medium-high risk range, respectively. The risk of falling objects is also in the range of low-medium risks with RPN value of 0.37.
Conclusion: This model has the ability to be used in systems where there is no sufficient and reliable quantitative data to assign a rating to the input variables of probability of occurrence, probability of discovery and severity of the consequence, and the use of linguistic variables enables industry experts to make real judgments. have more than the system under study.
Methods: In this research, five incidents were selected as the most important incidents in the exploitation phase of the steel industry. Estimating the probability of occurrence and discovering the root causes using fuzzy FTA was calculated and the severity of the consequence of the risk of accidents among 14 industry experts was collected and analyzed. Finally, the variables obtained in the previous steps were considered as inputs to the fuzzy inference system designed in MATLAB, and the final value of RPN was calculated using defined if-then rules.
Result: According to the obtained results, the probability of falling from a height, falling of objects, and electrocution were higher than other accidents, respectively. Finally, the risk of falling from a height and electrocution with RPN values of 0.665 and 0.563 are in the medium-high risk range, respectively. The risk of falling objects is also in the range of low-medium risks with RPN value of 0.37.
Conclusion: This model has the ability to be used in systems where there is no sufficient and reliable quantitative data to assign a rating to the input variables of probability of occurrence, probability of discovery and severity of the consequence, and the use of linguistic variables enables industry experts to make real judgments. have more than the system under study.
Type of Study: Research |
Subject:
Safety
Received: 2024/09/3 | Accepted: 2025/01/8 | Published: 2024/05/30
Received: 2024/09/3 | Accepted: 2025/01/8 | Published: 2024/05/30
References
1. Jouzi SA, Kabzadeh S, Irankhahi M. Safety, Health & Environmental Risk Assessment And Management Of Ahwaz Pipe Manufacturing Company Via "William Fine" Method. 2010.
2. Yakut M, Kaya I, Bozkus E, editors. A Two-Dimensional Fuzzy Risk Assessment Model for Occupational Health and Safety Evaluations. 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA); 2022: IEEE. [DOI:10.1109/HORA55278.2022.9799805]
3. Lee E, Park Y, Shin JG. Large engineering project risk management using a Bayesian belief network. Expert Systems with Applications. 2009;36(3):5880-7. [DOI:10.1016/j.eswa.2008.07.057]
4. Pokoradi L. Fuzzy logic-based risk assessment. AARMS, Academic and Applied Research in Military Science. 2002;1(1):63-73.
5. Chia ES, editor Risk assessment framework for project management. 2006 IEEE International Engineering Management Conference; 2006. [DOI:10.1109/IEMC.2006.4279889]
6. Tadic D, Djapan M, Misita M, Stefanovic M, Milanovic DD. A fuzzy model for assessing risk of occupational safety in the processing industry. International journal of occupational safety and ergonomics. 2012;18(2):115-26. [DOI:10.1080/10803548.2012.11076922] [PMID]
7. Binaghi E, Madella P. Fuzzy Dempster-Shafer reasoning for rule‐based classifiers. International Journal of Intelligent Systems. 1999;14(6):559-83.
https://doi.org/10.1002/(SICI)1098-111X(199906)14:6<559::AID-INT2>3.0.CO;2-# [DOI:10.1002/(SICI)1098-111X(199906)14:63.0.CO;2-#]
8. Costigan A, Gardner D. Measuring Performance in OHS: an Investigation into the use of Positive Performance Indicators. Journal of Occupational Health & Safety (Australia/New Zealand). 2000;16(1):55-64.
9. Commission NOHS. OHS performance measurement in the construction industry. Canberra, National Occupational Health and Safety Commission; Commonwealth of Australia 1999.
10. Scotney V. Development of a health and safety performance measurement tool. HSE CONTRACT RESEARCH REPORT. 2000.
11. Shahin A. Integration of FMEA and the Kano model: An exploratory examination. International Journal of Quality & Reliability Management. 2004;21(7):731-46. [DOI:10.1108/02656710410549082]
12. Wang Y-M, Chin K-S, Poon GKK, Yang J-B. Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert systems with applications. 2009;36(2):1195-207. [DOI:10.1016/j.eswa.2007.11.028]
13. Chin K-S, Chan A, Yang J-B. Development of a fuzzy FMEA based product design system. The International Journal of Advanced Manufacturing Technology. 2008;36(7-8):633-49. [DOI:10.1007/s00170-006-0898-3]
14. Chang K-H, Cheng C-H, Chang Y-C. Reprioritization of failures in a silane supply system using an intuitionistic fuzzy set ranking technique. Soft Computing. 2010;14(3):285-98. [DOI:10.1007/s00500-009-0403-7]
15. Jamshidi A, Kazemzadeh RB, editors. A Fuzzy Cost-based FMEA Model. International Conference on Industrial Experting and Operations Management; 2010: Citeseer.
16. Clemen RT, Winkler RL. Combining probability distributions from experts in risk analysis. Risk analysis. 1999;19(2):187-203.
https://doi.org/10.1023/A:1006917509560 [DOI:10.1111/j.1539-6924.1999.tb00399.x]
17. Zadeh LA. Fuzzy sets. Information and control. 1965;8(3):338-53. [DOI:10.1016/S0019-9958(65)90241-X]
18. Zimmermann H-J. Fuzzy sets, decision making, and expert systems: Springer Science & Business Media; 2012.
19. Vidal L-A, Marle F, Bocquet J-C. Using a Delphi process and the Analytic Hierarchy Process (AHP) to evaluate the complexity of projects. Expert systems with applications. 2011;38(5):5388-405. [DOI:10.1016/j.eswa.2010.10.016]
20. Ardeshir A, Mohajeri M, Amiri M. Safety assessment in construction projects based on analytic hierarchy process and grey fuzzy methods. Iran Occupational Health. 2014;11(2).
21. Mete S, Oz NE, Gul M, Serin F, Celik E, editors. A Risk Assessment Approach Using Both Stochastic Data and Subjective Judgments. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019; 2020: Springer. [DOI:10.1007/978-3-030-23756-1_130]
22. Ardeshir A, Amiri M, Mohajeri M. Safety risk assessment in mass housing projects using combination of fuzzy FMEA, fuzzy FTA and AHP-DEA. Iran Occupational Health. 2013;10(6).
23. Xiang Y, Liu C, Chao C, Liu H. Risk analysis and assessment of public safety of Submerged Floating Tunnel. Procedia Engineering. 2010;4:117-25.
https://doi.org/10.1016/j.proeng.2010.08.014 [DOI:10.1016/j.proeng.2010.08.013]
24. Yang, Zh., Xu, B ., Chen, F ., Hao, Q ., Zhu, X ., Jia, Y ., (2010). "A New Failure Mode and Effects Analysis Model of CNC Machine Tool using Fuzzy Theory", P roceedings o f t he 2010 I EEEInternational Conference on Information and Automation, Harbin, China, pp. 582-587. [DOI:10.1109/ICINFA.2010.5512403]
25. Deshpande A, Khanna P. Fuzzy fault tree analysis: case studies. Reliability and Safety Analyses Under Fuzziness: Springer; 1995. p. 126-41. [DOI:10.1007/978-3-7908-1898-7_8]
26. Pan N-F, Wang H, editors. Assessing failure of bridge construction using fuzzy fault tree analysis. Fuzzy Systems and Knowledge Discovery, 2007 FSKD 2007 Fourth International Conference on; 2007: IEEE. [DOI:10.1109/FSKD.2007.193] [PMID]
27. Falahati M, Karimi A, Mohammadfam I, Mazloumi A, Reza Khanteymoori A, Yaseri M. Multi-dimensional model for determining the leading performance indicators of safety management systems. Work. 2020;67(4):959-69. [DOI:10.3233/WOR-203346] [PMID]
28. Falahati M, Karimi A, Zokaie M, Biabani A, Faghihnia Torshizi Y. Development and validation of active performance indicators of electrical safety using bow-tie and bayesian network techniques case study: Oil and gas industries construction projects. Iran Occupational Health. 2019;16(4):22-34.
29. Zokaee M, Falahati M, Asady H, Rafee M, Najafi M, Biabani A. Development and validation of a practical model for quantitative assessment of HSE performance of municipalities using the impact of urban management system components. Journal of Health & Safety at Work. 2019;9(2).
30. Nordlöf H, Wiitavaara B, Winblad U, Wijk K, Westerling R. Safety culture and reasons for risk-taking at a large steel-manufacturing company: Investigating the worker perspective. Safety science. 2015;73:126-35. [DOI:10.1016/j.ssci.2014.11.020]
31. Onisawa T. An approach to human reliability in man-machine systems using error possibility. Fuzzy Sets and Systems. 1988;27(2):87-103. [DOI:10.1016/0165-0114(88)90140-6]
32. Mahmood Y, Ahmadi A, Verma AK, Srividya A, Kumar U. Fuzzy fault tree analysis: A review of concept and application. International Journal of System Assurance Engineering and Management. 2013;4(1):19-32. [DOI:10.1007/s13198-013-0145-x]
33. Saaty TL. Decision making with the analytic hierarchy process. International journal of services sciences. 2008;1(1):83-98. [DOI:10.1504/IJSSCI.2008.017590]
34. Martin JE, Rivas T, Matías J, Taboada J, Argüelles A. A Bayesian network analysis of workplace accidents caused by falls from a height. Safety Science. 2009;47(2):206-14. [DOI:10.1016/j.ssci.2008.03.004]
35. Mohajeri M, Amiri M. Ranking Main Causes of Falling from Height Hazard in High-Rise Construction Projects. Iran Occupational Health. 2014;11(5):53-64.
36. Montero-Odasso M, Van Der Velde N, Martin FC, Petrovic M, Tan MP, Ryg J, et al. World guidelines for falls prevention and management for older adults: a global initiative. Age and ageing. 2022;51(9):afac205.
37. Fang W, Ma L, Love PE, Luo H, Ding L, Zhou A. Knowledge graph for identifying hazards on construction sites: Integrating computer vision with ontology. Automation in Construction. 2020;119:103310. [DOI:10.1016/j.autcon.2020.103310]
38. Zeng S, Tam VW, Tam CM. Towards occupational health and safety systems in the construction industry of China. Safety science. 2008;46(8):1155-68. [DOI:10.1016/j.ssci.2007.08.005]
39. Gürcanli GE, Müngen U. An occupational safety risk analysis method at construction sites using fuzzy sets. International Journal of Industrial Ergonomics. 2009;39(2):371-87. [DOI:10.1016/j.ergon.2008.10.006]
40. Liu H-T, Tsai Y-l. A fuzzy risk assessment approach for occupational hazards in the construction industry. Safety science. 2012;50(4):1067-78. [DOI:10.1016/j.ssci.2011.11.021]
41. Bentley TA, Hide S, Tappin D, Moore D, Legg S, Ashby L, et al. Investigating risk factors for slips, trips and falls in New Zealand residential construction using incident-centred and incident-independent methods. Ergonomics. 2006;49(1):62-77. [DOI:10.1080/00140130612331392236] [PMID]
42. Mortazavi S, Asilian H, Avestakhan M. Relationship between safety climate factors and the risk of dangerous situations in height among construction workers. 2011.
43. Amouei M, Barzegar A, et al. Electric shock, a sad ending (study of electric shock resulting in death referred to Legal Medicine Organization during one and a half years (from 1999 to october 2000), Scientific Journal Of Forensic Medicine.
44. Abdelgawad M, Fayek AR. Risk management in the construction industry using combined fuzzy FMEA and fuzzy AHP. Journal of Construction Engineering and Management. 2010;136(9):1028-36. [DOI:10.1061/(ASCE)CO.1943-7862.0000210]
45. Shafiee M, Enjema E, Kolios A. An integrated FTA-FMEA model for risk analysis of engineering systems: a case study of subsea blowout preventers. Applied Sciences. 2019;9(6):1192. [DOI:10.3390/app9061192]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
