• Safety and Health at Work
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• v.5 no.3
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• pp.118-124
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• 2014

Background: Satisfactory completion of mine safety training is a prerequisite for being hired and for continued employment in the coal industry. Although training includes content to develop skills in a variety of mineworker competencies, research and recommendations continue to specify that specific limitations in the self-escape portion of training still exist and that mineworkers need to be better prepared to respond to emergencies that could occur in their mine. Ecological models are often used to inform the development of health promotion programs but have not been widely applied to occupational health and safety training programs. Methods: Nine mine safety trainers participated in in-depth semi-structured interviews. A theoretical analysis of the interviews was completed via an ecological lens. Each level of the social ecological model was used to examine factors that could be addressed both during and after mine safety training. Results: The analysis suggests that problems surrounding communication and collaboration, leadership development, and responsibility and accountability at different levels within the mining industry contribute to deficiencies in mineworkers' mastery and maintenance of skills. Conclusion: This study offers a new technique to identify limitations in safety training systems and processes. The analysis suggests that training should be developed and disseminated with consideration of various levels-individual, interpersonal, organizational, and community-to promote skills. If factors identified within and between levels are addressed, it may be easier to sustain mineworker competencies that are established during safety training.

• Safety and Health at Work
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• v.10 no.4
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• pp.461-469
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• 2019

Background: Collisions involving workers and mobile machines continue to be a major concern in underground coal mines. Over the last 30 years, these collisions have resulted in numerous injuries and fatalities. Recently, the Mine Safety and Health Administration (MSHA) proposed a rule that would require mines to equip mobile machines with proximity detection systems (PDSs) (systems designed for automated collision avoidance). Even though this regulation has not been enacted, some mines have installed PDSs on their scoops and hauling machines. However, early implementation of PDSs has introduced a variety of safety concerns. Past findings show that workers' trust can affect technology integration and influence unsafe use of automated technologies. Methods: Using a mixed-methods approach, the present study explores the effect that factors such as mine of employment, age, experience, and system type have on workers' trust in PDSs for mobile machines. The study also explores how workers are trained on PDSs and how this training influences trust. Results: The study resulted in three major findings. First, the mine of employment had a significant influence on workers' trust in mobile PDSs. Second, hands-on and classroom training was the most common types of training. Finally, over 70% of workers are trained on the system by the mine compared with 36% trained by the system manufacturer. Conclusion: The influence of workers' mine of employment on trust in PDSs may indicate that practitioners and researchers may need to give the organizational and physical characteristics of each mine careful consideration to ensure safe integration of automated systems.

• Safety and Health at Work
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• v.12 no.3
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• pp.317-323
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• 2021

Background: The surface mining industry has one of the highest fatality rates among private industries in the United States. Despite recent decreases in the fatality rates of comparable industries, the fatality rate in the surface mining industry has increased. Meanwhile, a lack of safety research in surface mining has hindered efforts to improve safety strategies in the surface mining workplace. Method: This study examined quarry workers' hazard identification skills by conducting a case study of a surface mining facility in the Mid-Atlantic region of the United States. Semistructured interviews were conducted with eight quarry workers who were employed at the mine facility. In addition to the interviews, data were collected through field notes, notes from an expert meeting with safety managers, and site photographs to explore quarry workers' safety behaviors in the workplace. Results: The results showed that quarry workers identified hazards and improved their safety performance by translating safety knowledge learned from training into practice, acquiring hands-on work experience, learning from coworkers, and sharing responsibilities among team members. Conclusion: This study contributes to understanding quarry workers' safe performance beyond what they have learned in safety training to include their interaction with other workers and hand-on experience in the workplace. This study informs practitioners in the surface mining industry to build a safe work environment as they design effective safety programs for employees.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2017.05a
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• pp.90-91
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• 2017

Military camps deal with various types of explosives. For instance, military engineering unit conducts education and training for laying landmines. However, in case of land mine craters installed with regular-level RC, structural safety may be in danger thus there is a necessity to utilize High Performance Fiber-Reinforced Cement Composites (HPFRCC), which has high functionality in protection and blast resistance. Therefore, in this research we conducted an field application of land mine crater of HPFRCC, using the existing optimal fiber mixing ratio and ERCO addition ratio.

• Tunnel and Underground Space
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• v.24 no.4
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• pp.282-288
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• 2014

Stoping method has been acknowledged as one of the typical metalliferous underground mining methods. Notwithstanding with the popularity of the method, the majority of stoping mines are suffering from excessive unplanned dilution which often becomes as the main cause of mine closure. Thus a reliable unplanned dilution management system is imperatively needed. In this study, reliable unplanned dilution prediction system is introduced by adopting artificial neural network (ANN) based on data investigated from one underground stoping mine in Western Australia. In addition, contributions of input parameters were analysed by connection weight algorithm (CWA). To validate the reliability of the proposed ANN, correlation coefficient (R) was calculated in the training and test stage which shown relatively high correlation of 0.9641 in training and 0.7933 in test stage. As results of CWA application, BHL (Length of blast hole) and SFJ (Safety factor of Joint orientation) show comparatively high contribution of 18.78% and 19.77% which imply that these are somewhat critical influential parameter of unplanned dilution.

• Safety and Health at Work
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• v.12 no.4
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• pp.479-489
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• 2021

Background: During 2011, a study was undertaken to assess safety conditions in Serbian underground coalmines by analysis of injury data. The study covered all Serbian coalmines, identified week spots from the aspect of safety, and recommended possible courses of action. Since then, Serbia has made changes to safety and health legislation; all coalmines introduced new preventive measures, adopted international standards, and made procedures for risk management. After 10 years a new study has been performed to analyze the impact of these changes. Materials and methods: In this study, the injuries that have occurred in the Serbian underground coal mines over the last 20 years were analyzed. Statistical data analysis was performed by IBM SPSS Statistics v23. The injuries that occurred in the last ten years were compared with the results of the previous study (2000-2009). The average values of injury rates for both periods were compared for each of the categories (severity, age, body part, qualification), and the results were presented as absolute difference or percentile difference. Results: The results showed reduction in the number of injuries in the category of 20-30 years old workers, where the new training procedures for workers, which were set by mandatory legal regulations, certainly contributed. They also showed an increase in the number of injuries in the category of old workers, which indicates that the law did not have a positive effect on this category. Conclusion: The total number of injuries is still high; therefore, it is necessary to introduce mechanization and automation in mines and have a better policy for older workers who retire later nowadays.

• Geomechanics and Engineering
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• v.23 no.1
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• pp.51-59
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• 2020

In this paper, the WEKA platform was used to mine and analyze measured data of floor failure depth and a prediction system of floor failure depth was developed with Java. Based on the standardization and discretization of 35-set measured data of floor failure depth in China, the grey correlation degree analysis on five factors affecting the floor failure depth was carried out. The correlation order from big to small is: mining depth, working face length, floor failure resistance, mining thickness, dip angle of coal seams. Naive Bayes model, neural network model and decision tree model were used for learning and training, and the accuracy of the confusion matrix, detailed accuracy and node error rate were analyzed. Finally, artificial neural network was concluded to be the optimal model. Based on Java language, a prediction system of floor failure depth was developed. With the easy operation in the system, the prediction from measured data and error analyses were performed for nine sets of data. The results show that the WEKA prediction formula has the smallest relative error and the best prediction effect. Besides, the applicability of WEKA prediction formula was analyzed. The results show that WEKA prediction has a better applicability under the coal seam mining depth of 110 m~550 m, dip angle of coal seams of 0°~15° and working face length of 30 m~135 m.