Journal of Health and Safety at Work

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Introduction: Safety in construction needs not only operating the executive laws and instructions but also the safety culture of all the workers in workshops. Safety culture is a factor by which all the workers are committed to having a positive share in the safety of their coworkers and themselves. Therefore, the aim of this study is identification of safety culture factors and ranking occupations in jobsites in order to improve the safety condition and promote the safety culture of high-rise projects.

Material and Method: In this descriptive-analytical study, first, safety culture criteria in construction projects are identified by a literature review. Then, 8 factors of safety culture are weighted using fuzzy analytic hierarchy process. Finally, 10 different occupations in high-rise workshops are ranked from the point of view of safety culture using fuzzy TOPSIS method.  

Result: Results of ranking the criteria show that the most important criteria are safety attitude, supervision, policy of organization and safety training. Moreover, in high-rise construction the site superintendent and supervisor occupations have the highest and laborers have the lowest safety culture.

Conclusion: In this study, after evaluating different construction occupations by hybrid FTOPSIS-FAHP method, the results are compared with the results of previous researches. Here, it was found that the findings are consistent with previous studies. On the other hand, low level of safety culture of construction laborers among the investigated occupations shows weakness in safety training and attitude; hence, the factors such as more safety supervision and training must be considered in order to create a positive safety culture for the workers.

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Introduction: Resilience engineering is a novel approache to risk management and is the inherent ability of a system to adapt their work before, during and after the changes and adverse events in such a way that maintain the system performance under predictable and unpredictable conditions. The aim of this study was to identify indicators of organizational resilience of refineries and ranking them using fuzzy TOPSIS technique.  

Material and Method: A qualitative study was done to identify organizational resilience indicators of refinery complex. The main method of data collection was semi-structured interviews. Indicators were determined using qualitative content analysis and literature review. Weighting and ranking identified indicators was performed using fuzzy TOPSIS technique.   

Result: Eleven indicators were identified as follows: management commitment, performance management system, flow of information/communication, involvement culture, error management culture, education, preparedness, flexibility, innovation culture, change management, and human resource management.

Conclusion: Critical sociotechnical organizations especially refinery complexes in order to improve safety management and resilience situation should focus on effective indicators. Monitoring and improving them will increase safety and the resilience level of organization

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Introduction: Choosing maintenance strategy is one of the most complex and essential processes that can affect the safety and cost of equipment. The main aim of this study was to determine a risk-based maintenance policy for improvement of the safety and maintenance indices.

Material and Method: According to literature reviews and constraints associated with the studied industry, a number of safety and maintenance indices were selected and their values were measured. Next, in order to promote the selected indices, the best policy was implemented on nine critical machines of the company based on criteria such as cost, risk and availability in the framework of the fuzzy network analysis process. Finally, after six months period, the indices were re-measured. The Wilcoxon test was used to assess the changes in the indices.

Result: In the implementation of the model, condition based maintenance was more effective than other strategies. Following the intervention, the improvement of safety and maintenance indices was statistically significant. The statistical analyses demonstrated that indices like reliability, availability, mean time between failures, and the number of dangerous failures all were improved significantly (P<0.05).

Conclusion: The results showed that the simultaneous use of three criteria, i.e. cost, risk and availability in maintenance planning could reduce equipment-related accidents. Finally, the recommended model can improve the efficiency and competitiveness of organizations by increasing availability and reducing equipment costs

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Introduction: Safety at workplaces is an important issue, and lack of effective and efficient safety management methods based on scientific studies could cost the construction industry. An effective accident investigation and root causes analysis could prevent the occurrence of incidents at minimum cost and time.
Material and Method: First, major accidents in road construction projects were identified by interview and literature reviewing techniques. Then, fault tree was drawn for each main risks. In the next step, in order to calculate occurrence probability and severity of each accident type, a questionnaire was designed and distributed among the participants. Fuzzy logic was employed in calculations, due to the existence of uncertainty in linguistic variables. Fuzzy Fault Tree Analysis (FFTA) was used for calculating the occurrence probability, and importance of each criterion was determined using Fuzzy Analytical Hierarchical Process (FAHP) to calculate the severity of each accident. Finally, risk factors for each accident type was calculated and the risks were ranked accordingly.
Result: Accidents related to crusher and asphalt plant, traffic accidents, heat exhaustion and lack of hygiene and hit by machinery were identified as the most important risks among the eleven investigated accident types.
Conclusion: The findings in this research and also implementing effective safety management techniques, could be helpful on reduction of the probability of accidents root causes and to mitigate related risks.

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Introduction: Noise is one of the most harmful industrial agents and there are different methods to control it. Fuzzy analytical hierarchy process is an appropriate technique for selecting the best choice among several control methods. Therefore, the aim of this study was the selection of the most suitable sound control method using fuzzy hierarchical analysis (FAHP) technique in a refinery plant.
Material and Method: The present study was a cross-sectional research in a refinery plant. After identifying the main sources of the noise in the studied palnt, five criteria and ten noise control methods were selected using a questionnaire and Delphi methods. Then, Fuzzy hierarchy analysis was applied for the selection of the best noise control alternative.
Result: The results showed that the performance with a final weight of 0.277 and the non-interference in the process with a final weight of 0.06 were most and least important criteria, respectively. Meanwhile, worker enclosure had highest score (0.207) of the control methods.
Conclusion: In general, the results showed that best criterion for selection of the suitable noise control method is performance. What’s more, based on the results, worker enclosure was selected as best noise control method in the refinery.

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Introduction: Working in hot and harsh weather conditions can cause heat related diseases and in some cases, even can lead to death. Risk assessment of heat stress in these environments is of particular importance. As there are many factors that could affect the heat stress, therefore, an index should be applied that could properly reflect the effect of all of these factors.
Material and Method: Initially a five-member expert team was established. Then, the weight of each variable was determined by the fuzzy analytical hierarchy process (FAHP) method. In next step, five work stations of the casting process evaluated applying fuzzy TOPSIS (FTOPSIS) method and the risk of heat stress prioritized in these stations. Lastly, the Pearson’s correlation coefficient was used to determine correlation between the results of proposed method with WBGT index.
Result:  The weights of three main variables including task characteristics, working environment, and worker characteristics was determined as 0.279, 0.526, and 0.195. The risk priority of the five work stations including, stocking, melting furnace, pouring and casting, polishing, and warehousing was established as S1= 4, S2= 2, S3= 1, S4= 3, and S5= 5. The Pearson’s correlation coefficient between the similarity index (CCi) and WBGT was 0.97.
Conclusion: From three main variables that can affect the heat stress, “Working Environment” has main impact in the risk assessment process; therefore, the most efforts must be focused on controlling this variable. The proposed method in this study has the capability of concurrent quantitative and qualitative assessment of factors that could affect the heat stress and can minimize the uncertainties in the risk assessment process relying upon the fuzzy sets.

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Introduction: Skin disease caused by exposure to the chemicals is one of the major health-related problems in the workplaces. This study has done to prioritize the strategies for skin exposure with chemical in a painting company.
Material and Method: In this analytical cross-sectional study 7 criteria and 8 selection control methods were initially chosen by surveying the existing records and through the assessments of expert panel and checking the result of previous studies. Subsequently, control strategies were prioritized according to the criteria using Fuzzy Analytical Hierarchy process & Topsis.
Result: Results of the paired comparison showed that the safety of the control strategy (w=0.298) and it’s up to date (w=0.0138) were the most and least important criteria for skin control strategies. Among skin control strategies for chemical exposure, the highest and lowest importance were for training (w=0.817) and work-rest schedule (w=0.233), respectively.
Conclusion: In this study, training was determined as the best control strategies for skin chemical exposure in the studied painting company according to the investigated criteria. This decision-making process could be applied for promotion of the skin health among workers.

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Introduction: Todays, the improvement and optimization of the safety performance are essential and important for control and prevention of accidents in the industry. The purpose of this research was to assist a hybrid cycle power plant for identification and prioritization of the influencing factors in enhancing safety in order to reduce risks and to improve system performance.  
Material and Methods: In this descriptive-analytical study, influencing factors were classified within three main categories of human, equipment, and managment, and 14 sub-factors was screened and selected by experts using Fuzzy Delphi method. Then, fuzzy DEMATEL method was adopted to determine the relationships, the intensity of affecting and being affected factors, and the analytical network process method for weighting and prioritization the factors.
Results: The findings of the fuzzy DEMATEL method showed that “Managerial”, “equipment” and “human” factors are respectively influencing factors on the improvement of safety performance. “Managerial factors” is the most influencing and “Human factors” is the most influenced one. Based on the results of fuzzy analysis network process method, “human factors” is the first priority among the main factors, and “employee motivation”, “system of control and prevention”, “work team spirit”, “individual skills” and “Individual protection equipment” sub-factors are respectively the first to fifth priorities were according to their weight.
Conclusion: “Human factors” re the most influenced factor and the main problem of the organization, which can be improved by the most influencing “managerial” factor. The success or failure of the safety performance in the power plant depends on better management of the “human factors” and managers need to motivate employees to improve safety performance.

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Introduction: Evaluating environmental risks in the oil and gas industry is essential to prevent irreparable damage to the environment. Using classical methods for prioritizing environmental risks does not achieve high-reliable results. Therefore, the aim of this study is to minimize the limitations of classical methods in a typical oil and gas production zone, by using fuzzy logic and Multi Attribute Decision Making (MADM) approach.  
Material and Methods: After forming an identification and assessment team including experienced experts from different organizational units in a region of exploitation of oil and gas, values of each risk factor (likelihood of occurrence, severity and detectability) related to identified environmental risks are determined according to their qualitative opinions represented by linguistic variables. Relative weights of the risk factors are calculated by applying the group Analytic Hierarchy Process (AHP) in a fuzzy environment on expert opinions. Then, fuzzy aggregation in the linear form by considering the weight of the risk factors and a method that is developed based on the center of gravity are employed in evaluation and ranking of the risks.
Results: In this study, the severity factor has the most important contribution in risk assessment compared to the other risk factors, since it has the highest relative weight. Raw sewage aspect resulted from absence of appropriate treatment system has the highest priority and spilling over of acid that is caused by chiller cleaning stands at the second position in the identified environmental risks.
Conclusion: The results demonstrate that although the proposed methodology requires greater time than classical methods, it is able to determine the risk ranking more practically because of minimizing the limitations of classical methods: high sensitivity to judgmental errors, considering some risks in the same index group and ignoring uncertainty in experts’ opinions. Proposed method is a proper alternative for classical environmental risk assessment technique, and capable of prioritization and evaluation risks in terms of safety and health.

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Introduction: Damage to occupational health is one of the major challenges in the industry. Various studies have shown that productivity in industries has a significant relationship with occupational health. In addition, employee’s health in the workplace can be affected by a variety of variables i.e., job stress, job satisfaction, and work in unconventional shifts. Therefore, the purpose of this study was to evaluate the causal relationships of shift work, job stress, job satisfaction with the occupational health level in a petrochemical industry.
Material and Methods: This cross-sectional study was implemented in 2017. The study sample consisted of 20 HSE experts selected using purposeful judgment sampling. A 4×4 matrix questionnaire consisting of four main parameters i.e., shift work, job stress and job satisfaction with occupational health level, was used for data acquisition. Data analysis was done using MATLAB software and Fuzzy DEMATEL method. Also, for each variable, two key values of D+R and D-R were calculated. These values show the degree of interaction and the type of interaction of the variable with other variables, respectively.
Results: Sixty percent of the experts participating in this study were male and 40.0% were female. Only 10.0% of the participants had a PhD degree. In addition, the mean age and the mean work experience of the subjects were 39.64±9.34 and 10.22±7.10 years, respectively. The parameters of shift work and job satisfaction were identified as an effective factor due to the positive values of D-R while occupational health variable with negative value of D-R, was considered as an affected factor. In addition, these results showed that the effect of these parameters on health is different with both direct and indirect mechanisms.
Conclusion: Using the Fuzzy DEMATEL method, our findings indicated that occupational health in the large industries can be influenced by different parameters with different sizes. Considering the interactions among these parameters in health analysis and the affecting factors, therefore, is very important. So, the health level in challenging industrial environment such as petrochemical industries can be affected by shift work as a root cause. This root cause, along with job satisfaction, has a significant effect on increasing stress levels and reducing health levels. Accordingly, any action to increase the health level should focus on improving shift patterns and increasing the level of job satisfaction of employees as a pivotal root and affecting causes on health level.

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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.