Canning, Jennifer (2012) Fuzzy methodology for prediction of occupational accident rate. Masters thesis, Memorial University of Newfoundland.
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An occupational accident is defined as an unexpected and unplanned occurrence arising out of or in connection with work, resulting in personal injury, disease or death. The human cost of occupational accidents is vast and the economic burden of poor occupational health and safety practices is staggering, resulting in the loss of billions of dollars annually. -- Over the last 40 years, occupational safety has been regulated under various national legislative schemes to ensure a balanced approach to workplace health and safety issues and to minimize hazards and reduce risk in the workplace. Model development in the research of accidents is considered to be the most effective way of studying the occupational accident issue, providing a proactive approach to address occupational concerns. -- The majority of research directed towards occupational accidents is qualitative and relies on the opinions of experts in the ranking of risk. A key component in many occupational accident models lies in the derivation of qualitative data obtained through a survey of safety experts to propose graded or ranked causes of accidents. The subjective nature of expert opinion or judgements introduces a degree of uncertainty within the analytical process. This work focuses on the development of a fuzzy methodology which is aimed to enhance the effectiveness of accident models by providing a mathematical tool to account for vagueness and uncertainty associated with expert judgements and opinions and to capture this uncertainty within the analysis. The novelty of the proposed methodology lies in an approach that embraces uncertainty as an inseparable element of the system. The proposed methodology recognizes that uncertainty plays a role in decision making and uses fuzzy set theory to account for and minimize uncertainty associated with the subjective nature of expert opinions. The fuzzy methodology will be incorporated into a predictive model developed to predict the frequency of occupational accidents and associated costs within the oil and gas industry.
|Item Type:||Thesis (Masters)|
|Additional Information:||Includes bibliographical references (leaves 113-117).|
|Department(s):||Engineering and Applied Science, Faculty of|
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