Journal of Health and Safety at Work

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Introduction: Safety situation awareness is an important element affecting operator's reliability and safety performance, which is influenced by various variables. Identification of these variables and their relationship will play a major role in optimizing control measures. The present study was conducted for this purpose.
Material and Methods: This study was based on the situation awareness, expert’s opinions and use of a Fuzzy multi-criteria decision-making method. Triangular fuzzy numbers was used to quantify the experts' judgments and to reduce the errors that result from theirs’ subjective evaluation on the relationships between the variables.
Results: The results showed that the studied organizational variables together with "safety/g knowledge" and "experience in job/specific task” are the most important predictive variables of situation awareness. Among the organizational variables, "Organizational Safety Attitudes", "Safe System Design" and "Education" are the most important determinants of safety situation awareness.
Conclusion: Fuzzy logic was used to aggregate expert opinions to determine the most important variables affecting situation awareness and their cause-effect relationships. Organizational variables are the main determinants of situation awareness. To improve situation awareness, the best results are obtained by modifying effective root variables, i.e., organizational variables and some individual variables.

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Introduction: Chemical industries often have risks for the environment and communities, due to the use of complex facilities and processes. Also, in the ammonia tanks, the probability of risk of explosion is high, owing to their specific characteristics. The aim of this study is to evaluate the risks of explosion scenario at the ammonia tank in the Kermanshah petrochemical complex
Material and Methods: To achieve the purpose of this study, the Fuzzy Fault Tree Analysis (FTA) method was used to estimate the probability of reliability in the basic events. In this study, after drawing Fault Tree for identifying basic events, the probability of basic events was estimated by means of expert’s elicitation, and the probability of minimal cut sets was computed through Boolean logic gates.
Results: According to the results, the probability of occurrence of the top event was obtained equal to 0/054997. In the minimal cut set prioritizing, the failing of pressure safety valves identified as the most effective factor in the top event occurrence, and afterward failing the control valves and human errors were identified.
Conclusion: This study indicates that, based on expert elicitation, a fuzzy error tree method can be used to assess the risk of various scenarios in the industry. Overall, in assessing the risk of the explosion scenario in the ammonia reservoir, it was found that some minor defects, and even human error, could be considered as a major contributor to the explosion.
 

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Introduction: Oxygen-generating central plays a vital role in the continuous performance of hospitals. Any leakage or failure in this section can not only endanger the health and safety of patients but also cause fire and explosion. Probabilistic risk assessment is a useful tool for identifying the main root causes of leakage in oxygen-generating central. This study aimed at risk assessment of an oxygen-generating central in a hospital in Hamadan using fuzzy sets theory and Bayesian networks.
Material and Methods: First, all root causes supposed to contribute to oxygen leakage from any part of the oxygen-generating central were identified, and based on them a fault tree analysis (FTA) was constructed. Then, the FTA was mapped in a BN. The failure probability of root causes was calculated using fuzzy sets theory and experts’ opinions. Belief updating based on BN was utilized for subsequent analyses.
Results: According to this study, ignorance of labels on the oxygen generation and distribution system is the most important root cause leading to oxygen leakage. Moreover, removing masks from patient’s faces is the main cause of oxygen leakage in patient rooms. Once leakage occurred, the presence of an ignition source can lead to fire or explosion.
Conclusion: oxygen leakage can create considerable risks in hospitals. All staff should be provided with sufficient training regarding hazards of oxygen-generating and distributing systems and oxygen leakage. Particular attention should be paid to such leakages and their adverse consequence in emergency planning and hospital crisis management.

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Introduction: One of the essential and critical elements for efficient and effective management of emergencies is anticipation and identification of possible types of emergencies. As such, a framework for anticipating and identifying emergencies was designed and tested in two process industries in the form of a case study.
Material and Methods: At first, methods for identifying emergency preparedness and their evaluation criteria were extracted and prioritized with a two-stage fuzzy approach. A fuzzy inference system was then used to calculate the weight of the experts’ opinions. To prioritize the methods, the inputs related to the second fuzzy system were estimated and the final score of the methods was calculated by entering the mentioned variables into the fuzzy system.
Results: The findings pertaining to the final ranking of the methods indicated that, “list of catastrophic accidents and near-misses of the organization’s lifespan”, “MIMAH” and “risk assessment and management” had the highest scores among the identified methods with the final scores of 0.754, 0.750 and 0.725, respectively.
Conclusion: Using this approach will help in more accurate identification of potential emergencies. Consequently, this will lead to the prevention of imposed damages caused by the situation as well as making the wrong investments by eliminating low-priority emergencies.

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Introduction: Failure Mode and Effects Analysis (FMEA) is a structured way to find and understand the states of a system’s failure and to calculate the resulting effects. In this method, which has been criticized by many researchers, the risk priority number is obtained for each failure mode based on the multiplication of the three parameters of occurrence (O), severity (S) and detection (D). In order to overcome the disadvantages of the traditional method of FMEA, such as ranking the failure and weighting the parameters, this research proposes a model in the fuzzy set.
Material and Methods: The model proposed in this paper is a nonlinear model for weighting the parameters of the FMEA and the revised TOPSIS method for ranking the failures, which is used for the first time to improve the FMEA method.
Results: The proposed model was presented in the Copper Complex of Shahr-e-Babak to assess safety risks. Based on the results of the study, it was found that in this proposed model, the weights of severity and detection were 0.479 and 0.186, respectively, and the results of the ranking showed that the risks of falling from height and getting stuck between objects had the highest and lowest priorities, respectively.
Conclusion: In the proposed model, based on Logarithmic Fuzzy Preference Programming and the revised TOPSIS method, the definite weights of the parameters were presented without any fuzzy number ranking and risk ranking with more criteria, respectively. Therefore, the proposed model has a higher ability compared to the traditional FMEA, and its application can be recommended to determine the ranking of risks.

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Introduction: Risk assessment is a scale for predicting reliability and can manage interactions between components and process variables. Moreover, the reliability of one component or barrier affects the overall risk of the system. Being one of the most critical safety barriers of the storage tank, the failures of Fixed Foam Systems (FFS) on demand can result in severe consequences. FFS, is of grave importance in decreasing the risks associated with fires and damages.
Material and Methods: This study aims to determine the probability of root causes related to FFS failure through Fuzzy Fault Tree Analysis (FFTA) to estimate system reliability. In conventional fault tree analysis, accurate data is usually used to assess the failure probability of basic events. Therefore, the introduced approaches were employed to quantify failure probabilities and uncertainty handling. Finally, system reliability was estimated according to the failure probability of the top event.
Results: The findings showed that 13 baseline events involved FFS performance. According to the results, failures of cable path and detection system (or resistance temperature detectors), set the activation switch (multi-position) incorrectly, and foam makers not continuously running are the three most critical basic events influencing the reliability of fixed foam systems. In addition, this paper estimated the system reliability at 0.8470.
Conclusion: The results showed that the FFTA could be used in matters such as reliability evaluation failure and risk assessment using experts’ judgment. This paper can also show the adaptation of the fuzzy approach to assess the failure probability of the basic event in the fault tree analysis (FTA).

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Introduction: Pipelines are widely used to transport large volumes of oil and gas over long distances. Risk assessment can help identify risk factors and create an appropriate action plan and strategy to reduce or eliminate them. The main goal of this research is to provide a method for assessing the risk of pipelines based on the Fuzzy Inference System (FIS), creating a systematic format that is expected to be a more effective, accurate, and reliable model for controlling risks related to oil and gas pipelines.
Material and Methods: In this article, fuzzy logic is used to model uncertainty and present a model for assessing pipeline risk. The Muhlbauer method, one of the most common risk assessment methods for oil and gas pipelines, has been employed to determine critical factors affecting the lines. This method has been implemented using the Mamdani algorithm and based on expert knowledge in the fuzzy logic toolbox of MATLAB software. To validate the results of the proposed model, data from the interphase pipelines of the fifth refinery of the South Pars Gas Field have been used as a study sample.
Results: The findings from the implementation of the model created in South Pars Phases 9-10 pipelines (on shore) show that the studied pipelines are divided into three parts (A, B, and C) based on indicators such as population density and equipment deployment. Part C of the pipeline has the highest risk, with third-party damage and design being the most important factors affecting it. Part B has the lowest level of risk and results in the fewest consequences for human accidents. It was also observed that corrosion is essential in increasing leakage and risk in all three pipeline parts.
Conclusion: To verify the developed model, the inter-phase shore pipe of phase 9-10 refinery in the South Pars Gas Field was considered as a case study. The findings indicate that the proposed method provides more accurate and reliable results than traditional methods. Factors such as improper operation, dispersion, receptors, leakage volume, and product risk, which are other factors affecting pipeline risk, were not considered in traditional methods. Therefore, the risk level of oil and gas pipelines can be calculated using this model as a comprehensive and intelligent tool.

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Introduction: The accurate evaluation of error probability and risk is important. Accordingly, this Comparative study was conducted to evaluate the risk of human error in emergency situations using SLIM and Fuzzy SLIM techniques in fierfighting tasks.
Material and Methods: This cross-sectional and descriptive-analytical study was conducted among 12, using Fuzzy SLIM and SLIM techniques. 39 sub-tasks were studied in 4 phases (Awareness, Evaluation, Egress and Recovery). Considering the advantages of the Fuzzy SLIM method, fuzzy logic was used in weighting of performance shaping factors (PSF). Excel software was used to calculate the probability of error. Also, correlation and kappa statistical tests were used for data analysis in SPSS software.
Results: The mean and standard deviation of human error probability in different sub-tasks of firefighting in SLIM and Fuzzy SLIM methods were 0.095357 ± 0.026193 and 0.06490 ± 0.051748, respectivly. In 48.7 percent of the sub-tasks, the probability category of human error and the assessed risk were the same; however, in 89.7 percent of the sub-tasks, the estimated level of risk was the same in both methods. Correlation test showed that the correlation coefficient of error probability values between the two methods was 0.32, which indicated a moderate correlation in this regard. Additionally, the results of kappa statistical test for the estimated level of risk showed that there is a high agreement between Fuzzy SLIM and SLIM (P value <0.05).
Conclusion: The results of the study indicated meaningful agreement and a moderate correlation between Fuzzy SLIM and SLIM. Therefore, due to the relatively high accuracy of Fuzzy logic methods, and also the long steps of implementing the SLIM method, the Fuzzy SLIM method can be a good alternative to this method.

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Introduction: Identifying and modeling the root causes of accidents can play an important role in preventing them. The purpose of this study is to identify and model the causes of gas pipeline excavation and piping operation accidents using the Bayesian network (BN) and fuzzy DEMATEL.
Material and Methods: In this study, industrial accidents during gas pipeline excavation and piping operations were analyzed using the Bowtie method. The fuzzy DEMATEL method was employed to determine relationships between accident root causes, and the fuzzy AHP method was used to compare pairs of causes and determine their weight. Finally, Bowtie and DEMATEL outputs were mapped in Bayesian networks to determine the important risk factors for accidents.
Results: The most important risk factors for trench collapse accidents were as follows: risk management (16% impact weight), competency assessment (14.2% impact weight), supervision (13.8% impact weight), work permit system (13.7% impact weight), compliance with requirements and guidelines (13.4% impact weight), training (11.4% impact weight), HSE system (9.5% impact weight), and contractor management (8% impact weight).
Conclusion: Based on the results, it was demonstrated that risk management and competency assessment, having the highest weight percentages, play the most significant roles in the occurrence of trench collapse accidents. The findings of this study can inform the prioritization of corrective measures to prevent trench collapse accidents in gas pipeline excavation and piping operations.