Journal of Health and Safety at Work

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Introduction: One of the most important threats for employees working in chemical industries is exposing to the chemical materials. Lack of precaution and control regulations during working with chemicals can have irreparable consequences. So, in order to achieve an effective control program, it is necessary to have an appropriate assessment of the procedures involving exposure to the chemicals. William-fine method can provide an acceptable insight into hazard risk rate.

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Material and Method: In this study, a combination of AHP and William-fine adopted to achieve a model for chemicals exposure risk assessment in oil and gas industry. The numbers were extracted from questionnaire, and then, they weighted entered to assessment the model. Finally, they were presented quantitatively with protection layer.

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Result and Conclusion: The results revealed that the quantitative amount of consequence, probability and exposure was 83.2, 8.45, and 2.2, respectively. Generally, the chemical exposure risk number was 1546 which shows that reforming plans are in highly priorities from an economical point of view. William-fine method has the benefit of an accurate chemical exposure by combination of effect severity, exposure probability and detriment rate, and also minimization of personal judgments during the assessment.

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Introduction: Nowadays, using multi attribute decision making (MADM) techniques in HSE hazard rating have been widely increased. On the other hand, Analytical Network process (ANP) methods has a high flexibility and accuracy in decision ranking. Therefore, the combination of the two mentioned approaches can provide a more precise prioritization for HSE hazards. Using ANP techniques, in this research a method for accurate rating of HSE hazards in the construction procedure of combined cycle power plant was presented.

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Material and Method: In this study, first, the overall structure and components of ANP method were identified and the connections between its components were determined. Then, the determined structure and components were modeled, using super decision software. In the next stage, pair companions were performed between an criteria and sub-criteria with respect to HSE experts opinions, Finally, selected HSE hazards were prioritized and compared according to the presented ANP method and the common available method.

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Result: According to the results, common risk assessment method is not able to prioritized risk accurately. While the current method classified the ten main identified hazards in 4 general categories, the presented method in this study could prioritized then in 7 categories.

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Conclusion: The suggested ANP model could provide a more accurate prioritization for hazards in comparison with current methods because of considering five qualitative indices. This allows an optimal allocation of organizational resources for controlling hazards.

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Introduction: Biorhythm is one of the newest subject in the field of cognition of mental ergonomics which can be very effective in reduction of work-related accidents or mistakes with no apparent reason.
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Material and Method: This research is a cross-sectional, retrospective, practical and analytical-descriptive study. Delijan cement production company during 2010-2011. Census method was applied and totally 79 accidents (total of the accidents) were investigated. The required information was collected from available documents in HSE unit of the company biorhythm charts were drawn based on a dates of accidents and participants birthdays, using Natural Biorhythm Software V3.02. Human errors were classified according to reason model and were analyzed by SPSS-W software.
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Result: The results of analysis showed that 40% of accidents have been accrued in usual days and the other 60% in critical days of biorhythm cycle. Regarding errors leading to accidents, it was observed that 95% of accidents were related to human error and 5% related to equipment errors. Moreover, 65.8% of the human errors were associated with the slipping which happens during performing a task, according to reason model.
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Conclusion: Findings of this research showed that bad and critical days of individuals’ biorhythms cycle influence the occurrence of accidents. Therefore, by training and increasing the knowledge of workers regarding biological cycle and its effects on mental, emotional and physical status, each person effects can make some changes to theire work plans during days that they do not feel well, physically or mentally, in order to prevent the likely accidents.

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Introduction: Statistical Report of the Social Security Organization indicate that among the various industries, the construction industry has the highest number of work-related accidents so that in addition to frequency, it has high intensity, as well. On the other hand, a large number of human resources are working in this whish shows they necessity for paying special attention to these workers. Therefore, risk assessment of the safety in the construction industry is an effective step in this regard. In this study, a method for ranking safety risks in conditions of low number of samples and uncertainty is presented, using gray multi-criterion decision-making.

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Material and Method: In this study, we first identified the factors affecting the occurrence of hazards in the construction industry. Then, appropriate for ranking the risks were determined and the problem was defined as a multi-criterion decision-making. In order to weight the criteria and to evaluate alternatives based on each criterion, gray numbers were used. In the last stage, the problem was solved using the gray possibility degree.

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Results: The results show that the method of gray multi-criterion decision-making is an effective method for ranking risks in situations of low samples compared with other methods of MCDM.

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Conclusion: The proposed method is preferred to fuzzy methods and statistics in uncertain and low sample size, due to simple calculations and no need to define the membership function.

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Introduction: Occupational accidents are of the main issues in industries. It is necessary to identify the main root causes of accidents for their control. Several models have been proposed for determining the accidents root causes. FTA is one of the most widely used models which could graphically establish the root causes of accidents. The non-linear function is one of the main challenges in FTA compliance and in order to obtain the exact number, the meta-heuristic algorithms can be used.

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Material and Method: The present research was done in power plant industries in construction phase. In this study, a pattern for the analysis of human error in work-related accidents was provided by combination of neural network algorithms and FTA analytical model. Finally, using this pattern, the potential rate of all causes was determined.

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Result: The results showed that training, age, and non-compliance with safety principals in the workplace were the most important factors influencing human error in the occupational accident.

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Conclusion: According to the obtained results, it can be concluded that human errors can be greatly reduced by training, right choice of workers with regard to the type of occupations, and provision of appropriate safety conditions in the work place.

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Introduction: Electrical industries are among high risk industries. The present study aimed to assess safety risk in electricity distribution processes using  ET&BA technique and also to compare with both VIKOR & TOPSIS methods in fuzzy environments.
 

Material and Methods: The present research is a descriptive study and ET&BA worksheet is the main data collection tool. Both Fuzzy TOPSIS and Fuzzy VIKOR methods were used for the worksheet analysis.
 

Result: Findings revealed that overhead low and medium voltage power distribution networks had the highest risk among the other network types of transmission and distribution of electricity. Also, it was found that TOPSIS and VIKOR methods are appropriate to rank the safety risks with somehow similar results.
 

Conclusion: The height and electricity are of the main causes of accidents in electricity transmission and distribution industry which caused the overhead power networks to be ranked as high risk. Application of decision-making models in fuzzy environment minimizes the judgment of assessors in the risk assessment process.

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Introduction: Resilience engineering (RE), as a new approach in the system safety domain, is intended to preserve the performance of socio-technical systems in various conditions; and accentuates the positive activities instead of the failure modes. The aim of this study was to develop a new framework for safety assessment on the basis of RE, using the Fuzzy Analytical Hierarchy Process (AHP) method.  

Material and Method: Current study is an analytical cross-sectional survey performed in a petrochemical industry. Initially, six RE indicators were selected, including top management commitment, just culture, learning culture, awareness, flexibility and emergency preparedness and accordingly an assessment framework was established. Then, the selected RE indicators were evaluated and validated by experts in a specialized panel. Following, an indicator was proposed named “resilience early warning indicator”. Finally, the RE indicator score of the total process was determined using the fuzzy evaluating vector.   

Result: Findings revealed that top management commitment and learning indicators have the most and the least effects on the RE level of the process, respectively. Besides, the flexibility (C3) indicator was located in orange early warning zone (OEWZ) while other indicators were positioned in the no early warning zone (NEWZ). Furthermore, the overall resilience level of the process was evaluated as level III (NEWZ).

Conclusion: Management commitment and emergency preparedness are two main indicators of RE and can carry out the most important effect for remaining the RE in the NEWZ level.

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Introduction: Heat stress is one of the harmful risks in casting and die casting industries, which can not only cause work-related diseases but also can impair the performance and safety of workers. Since the indicators that are used to evaluate heat stress are very different, it is very difficult to choose a suitable index. The aim of this study was to determine the optimum heat stress index in foundry, die-cast, and road construction industries using FAHP and Topsis methods.

Material and Method: In order to determine optimum heat stress index in foundry, die-cast, and road construction industries, first, the prioritization criteria were defined by experts (including ease of measurement, measurement accuracy, comprehensiveness, time, cost, and correlation). Then, considering these criteria, the best heat stress index was determined based on experts’ opinions and using FAHP and Topsis methods.

Result: The results of this study suggest that given the current conditions and criteria, WBGT and P4SR is the best indices for foundry, die casting and construction.

Conclusion: The results showed that according to comprehensiveness, accuracy and correlation criteria, the WBGT index is considered as the best indicator of heat stress assessment in foundry, die-cast and road construction industries. Moreover, the HSI ranked in the last place due to the complexity and cost of its calculation.

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Introduction: FMEA method is one of the most used techniques in risk assessment and prioritization. But, due to several reasons, its application has been limited to the real-world settings. The aim of this study was to deal with these restrictions using the combined fuzzy (in terms of the Z-numbers) and grey (in terms of the grey relational analysis) theories.

Material and Method: The current study is an analytical cross-sectional survey that was performed to prioritize the failure modes of the overhead cranes. Initially, an FMEA team including 4 specialists was established. Then, the opinions of the team members were gathered in terms of the Z-numbers and the weights of the risk factors (O، S, and D) were determined using the fuzzy AHP method. Finally, the failure modes were prioritized using the GRP method.

Result: From 13 cases of the identified failure modes, the conventional FMEA was assigned equal priority to the 7 cases and as a result 9 risk priorities were determined. But, in the proposed method, because of the elimination of the restrictions of conventional FMEA, 13 risk priorities were assigned to the failure modes.

Conclusion: Relying upon the fuzzy AHP, Z-numbers and GRP method, the proposed method dealt with the equal weights of the risk factors, fuzziness of the data (expert’s judgments) and the prioritization of the failure modes. The proposed method has more capabilities in relation to the conventional FMEA for prioritization of the failure modes.

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Introduction: Risk assessment is a main tool in safety management process as it can help managers to choose corrective actions by providing appropriate information. The purpose of this paper was to select the optimal corrective actions among the proposed ones by the experts based on mathematical modeling, taking into account the standards and also the limitations including the cost.

Material and Method: In this paper, a model was presented to find the optimal corrective actions regarding the organization goals (maximum in risk reduction value) and the limitations such as cost and level of acceptable risk. Due to extensive number of solutions, Genetic Algorithm (GA) is used for solving the problem.

Result: To show the capability of this method in an industrial environment, a power generation industry with 40 hazards was considered as the case study. Then, the risk of hazards was estimated and corrective actions were determined for each of them. Using the proposed model, corrective actions were selected optimally, with the least possible cost; all risks were reduced below the level of organizational acceptable risk.

Conclusion: It was shown that the optimal corrective actions using mathematical modeling are selected with high precision in acceptable time. This method is suggested as an alternative for conventional qualitative methods based on expert’s opinions.

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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: The main purpose of this study was to identify and evaluate environmental problems and their ranking and to determine the conceptual model of environmental impact assessment (EIA) in industrial parks. Accordingly.
Material and Methods: In this study, environmental infrastructure issues were classified into five sections. The decision-making trial and evaluation laboratory (DEMATEL) technique was also employed to establish the relationships between the criteria. Moreover, the analytic network process (ANP) was utilized to determine their weight.
Results: Examining the internal relationships between the variables correspondingly revealed that cultural and economic criteria were the most influential. On the other hand, the biological criterion was very effective. The ANP results also showed that the “low share of environmental investment” as an indicator was an economic criterion with a normal weight of 0.80, which was of utmost importance among the other defined cases.
Conclusion: It was concluded that the definition of conceptual models in EIA processes can make it possible to examine and analyze the criteria and indicators affecting evaluation processes.