Journal of Health and Safety at Work

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Introduction: Manual material handling activities in long and short periods may lead to complications such as laceration, fracture, cardiovascular stress, muscle fatigue, and musculoskeletal disorders especially in the vertebrae column. The purpose of this study was to assess manual handling of oxygen cylinders by casting workers and to implement ergonomic intervention to reduce the risk of musculoskeletal disorders.

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Material and Method: This is an interventional study conducted among 18 male workers of a steel casting unit. Assessment of manual handling of oxygen cylinders, was done using in order Snook tables. The manual handling of oxygen cylinders was changed to mechanical handling and making a box with the capacity of 16 oxygen cylinders which can be moved by crane.

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Result: According to the results, lifting and lowering cylinders was not suitable for most of the workers. Moreover, caring, pulling and pushing was suitable for less than 10 percent of the workers. Condition of lifting cylinders by fire workers was suitable only 25 percent of them. According to the snook tables material handling activities must be suitable for at least 75 percent of workers.

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Conclusion: With the implementation of ergonomic intervention is casting unit, the risk of exposure to musculoskeletal disorders caused by manual handling of oxygen cylinders was eliminated and safety of employees against the risk of explosion of the cylinders in comparison with before the intervention was improved.

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Introduction: Safety culture is considered as the core of an organization’s safety management system. Safety culture is an organization ability to achieve higher standards of safety. The aim of this study was to investigate safety culture and its influencing factors and relation to the accident in a dam construction project.

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Material and Method: This cross-sectional study was conducted among 130 workers at a dam construction project. A standardized questionnaire included 59 questions was used to determine the level of safety culture. The accidents occurred in the project during the year were collected based on demographic characteristics. The collected data were analyzed using SPSS version 19.

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Result: The mean age of the subjects, their work experience and score of safety culture were 35.05, 7.5 Years and 183.2, respectively. Twenty seven accidents were recorded during the year in project. The most common cause of the accidents was indiscretions (33.3%). There was a statistically significant correlation between safety culture to occurred accidents and history of accident (P<0.05). The percentage of a positive safety culture of workers with an experience of accident (71.8%) was more than that of those with no experience of accident (45.1%). There was not a statistically significant correlation between safety culture and age, work experience, education, and marital status.

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Conclusion: It seems that safety culture on the project is influenced by the experience of accident and also it was strongly significant with the occurred accidents. Consequently, in order to create a positive safety culture in the workplace many factors including safety education program, work experience and accidents analysis should be considered.

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Introduction: Manual material handling (MMH) is the most common cause of work-related Musculoskeletal Disorders (MSDs). Prevention of MSDs is highly critical. The aim of this study was to assess risk of carpets manual handling by retail workers, and to implement ergonomic interventions in order to reduce risk of MSDs.

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Material and Method: This interventional study was conducted among 36 workers in 19 retailer sites of a textile corporation. Ergonomics assessment of the retailers was done using the comprehensive risk assessment model of the British Carpet Foundation. Moreover, the Nordic Musculoskeletal Questionnaire was used to determine the prevalence of workers’ MSDs. Reassessment was conducted after implementation of the ergonomics interventions.

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Result: The mean age and work tenure of participants were 36.28 and 16.2 years, respectively. The results of Nordic Questionnaire before intervention showed that overall 37.8% of the workers had experienced pain at least once during the past year, with the highest frequency belonged to the lower back (75%), shoulder (61%), and neck and upper back (55%), respectively. After implementing the interventions, the prevalence of MSDs reduced to 23.5%. The results of risk assessment before and after the interventions showed that of the 19 retailer sites, six sites were improved from poor to average state, and one site showed improvement from average to good condition.

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Conclusion: By implementing ergonomics interventions in carpet delivery sites, the risk factors of MSDs, induced by manual carpet handling, were reduced and safety and ergonomic conditions of the retailers were improved, compared to the previous conditions.

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Introduction: Physical fatigue is one of the major risk factors for work-related musculoskeletal disorders and has many life and financial costs. The impact of physical/biomechanical, psychosocial, environmental, and individual risk factors on muscle fatigue is undeniable. The aim of this study is to model the phenomenon of muscle fatigue (as output) in the hand in work environments based on these risk factors (as input) using soft computing methods.
Material and Methods: In the first step, associated risk factors of fatigue for 156 subjects (in three job categories) were assessed using Copenhagen environmental, psychosocial, demographic, and Man-TRA tools. Then, the Roman-Liu equation and mean square amplitude of acceleration waves were used to measure fatigue with a dynamometer and a three-axis accelerometer, respectively. Finally, according to the nature of risk factors and the phenomenon of fatigue, six categories (24 methods) of supervised machine learning (SML) based on classification were selected. MatLab software (MatLab R2017b, The Mathworks Inc., MA, U.S.A.) was used to fit the models using SML.
Results: The best-fitted models in the first and second half of the work shift were obtained using support vector machine methods. Physical risk factors had a significant impact on physical fatigue. After filtering low-priority risk factors, in the first half of the work shift, the most optimal model had an accuracy of 71.8%, precision of 72.5%, sensitivity of 76.9%, specificity of 70.8%, and discrimination power equal to 73%. In the second half of the work shift, the accuracy, precision, sensitivity, and specificity of the optimized model were 60.3%, 57.5%, 50%, and 46.9%, respectively, and the discrimination power was obtained at about 62%.
Conclusion: The fitted models for hand fatigue had acceptable performance in both sections of the shift but can still be optimized. Therefore, it is necessary for future studies to improve the quality of input and output data and include other dimensions affecting fatigue such as cognitive workload and type of work shift in future models.

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Introduction: The goal of this study is to investigate how the development of technology has affected the industry (especially the mining industry). For this purpose, this paper examines the impact of intelligent mining machinery systems, including tire pressure monitoring systems (TPMS), dispatching systems, and vehicle health monitoring systems (VHMS), on health, safety, and environmental parameters and preventative maintenance.
Material and Methods: This study is descriptive-analytical research that was conducted between time intervals before and after employing the intelligent mining machinery systems. Initially, parameters were identified using the Delphi method. These parameters include human accidents, equipment accidents, environmental incidents, warnings and fines in the domains of health, safety, and the environment, tire usage parameters, the shelf life of the tire, oil overfill, fuel consumption, failure rate, mean time between failures, and preventive maintenance compliance schedules in the domain of preventative maintenance. The effectiveness of using these systems was then assessed by comparing the state of the specified parameters before and after the introduction of the intelligent mining machinery systems.
Results: The findings of this research indicate that using intelligent mining machinery systems will decrease equipment accidents by 33.3%, extend the useful life of tires by 7.1%, reduce fuel consumption by 14.6%, cut the mean time required to repair by 25.5%, and enhance preventive maintenance compliance schedules by 5.7%.
The findings showed the effectiveness of the use of intelligent systems of mining machines was obtained as follows: reduction of equipment accidents by 33.3%, increasing the useful life of tires by 7.1%, reducing fuel consumption by 14.6%, reducing the average downtime of the car for repair by 25.5% and increasing compliance with the maintenance program by 5.7%.
Conclusion: Utilizing intelligent mining machinery systems might have a positive impact on the safety of machines, reduce negative environmental effects like fuel consumption, and improve the maintenance of heavy machinery, which would lead to better mining conditions and lower costs.