LAUSR

Underground coal mining is one of the most hazardous activities in all around the word. Therefore, risk analysis has a remarkable role in the coal mining works. In this study, a new probabilistic approach is developed to evaluate the most important hazard of coal mining. For this aim, at first a fuzzy TOPSIS model is applied to rank the risks of the mining. By this way, it is possible to overcome the existing uncertainty of the risk ranking process. Application of the proposed procedure shows that the roof fall is the most important hazard in the Tabas Coal Mine in Iran. Afterward, this study tried to quantify the roof fall risk as the most important hazard in underground coal mining. Due to the related uncertainties associated with every mine, it is very difficult to predict the roof fall. As a result, development of a methodology for evaluation of roof fall risk under uncertainty condition has a key role in safety of underground coal mines. In this paper, a new approach for analyzing the risk of roof fall is presented. For this aim, the major factors influencing the stability of the roof are utilized in a Bayesian network-based model. The proposed method is illustrated with an application in Tabas Coal Mine. The results show that that BN-based model is a capable method for adjusting to uncertainties in the roof fall risk evaluation.