Abstract
This study contributes to the on-going efforts to improve occupational safety in the mining industry by creating a model capable of predicting the continuous risk of occupational accidents occurring. Contributing factors were identified and their sensitivity quantified. The approach included using an Artificial Neural Network (ANN) to identify patterns between the input attributes and to predict the continuous risk of accidents occurring. The predictive Artificial Neural Network (ANN) model used in this research was created, trained, and validated in the form of a case study with data from a platinum mine near Rustenburg in South Africa. This resulted in meaningful correlation between the predicted continuous risk and actual accidents.
Highlights
In all forms of industry, employee safety is an essential aspect of the organisation’s operations, regardless of the risks to which employees are exposed
Grimaldi and Simonds [1] state that not all injuries can be prevented since they are unpredictable in nature, and that accidents do occur at times for reasons that are unpredictable
This study has attempted to assist in highlighting high-risk time periods, where caution can be exercised in order to reduce the number of accidents occurring
Summary
In all forms of industry, employee safety is an essential aspect of the organisation’s operations, regardless of the risks to which employees are exposed. There is a large knowledge gap in the field of predictive safety modelling, and this is the focus of this research. The purpose of this research is to develop a model in the field of predictive modelling in a safety environment, dealing with the exposure of employees to known risks. This will add to the current lack in the body of knowledge about predictive safety modelling. A multivariate mathematical model cannot be used to link circumstantial variables to estimate the continuous risk of accidents occurring
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