A sytemic study of mining accident causality : an analysis of 91 mining accidents from a platinum mine in South Africa
Master Thesis
2013
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University of Cape Town
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The mining industry is a very important sector of the South African national economy. A major factor threatening the sustainability of this industry is the worrying effect of mining accidents. These accidents usually lead to the destruction of property, injury/death of mine workers, and pollution of the environment. Although mining is generally seen as a hazardous operation worldwide, the accident rates in South African mines are still unacceptably high. Another worrying phenomenon is the fact that since 2003 reduction in fatalities and injuries has been 20– 25% short of annual targets set by stake holders. These factors make the safety of the industry a very important subject. The understanding of accident causality is a major step in the quest to reduce accidents. It is only with a good understanding of the accident process that effective remedies can be designed. Accident modelling techniques provide the necessary platform for the interpretation and understanding of accidents at workplaces. The Swiss Cheese Model of accidents has proven to be a very efficient way of analysing industrial accidents. In this model, an accident is seen as a combination of unsafe acts by front line operators and latent conditions in the organization. The model helps to identify factors in an organizational structure that influence human behaviour/performance at workplaces. This study is aimed at demonstrating how a systemic approach can be applied to the analysis of the causes of accidents in South African mines. In this study, an accident analysis framework has been developed from the Swiss Cheese Model, combining the Mark III version of the Swiss Cheese Model, the Nertney Wheel and safety management principles. The main section of the framework is made up of three layers of accident causality: proximal causes, workplace factors and systemic factors. The second section (metadata) of the framework incorporates contextual data pertaining to each accident such as age, experience, task being performed, and time of accident. These data enhance the understanding of accident causality. The third and final section of the framework incorporates information about accident causing agencies and the nature of barriers breached in the accident process.
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Bonsu, J. 2013. A sytemic study of mining accident causality : an analysis of 91 mining accidents from a platinum mine in South Africa. University of Cape Town.