An Integrated Entropy Weight and Grey Clustering Method–Based Evaluation to Improve Safety in Mines

A thorough improvement in mine safety is contingent on many factors that require scientific decision-making for selection and prioritizing of important factors for safety improvement. Their analysis using an established scientific method can improve mines’ safety conditions and assist the administration for informed decision-making. This research establishes a mine safety evaluation index and proposes an integrative model based on the entropy weight and grey clustering methods. In the evaluation index, 27 significant factors of safety are identified and categorized into 7 criteria. The proposed evaluation system analyzes all indicators according to Pakistan’s current mine safety situation and their significance for potential safety improvement in the future. Results reveal that current safety practices are outdated due to a lack of mechanization in the mining industry and minimal capability of the management to achieve the desired safety targets. Appropriate safety policies and safety education are missing in the daily working environment. The lack of overseeing the execution of safety operations and the lack of ambition and strong work ethic workers contribute to the paucity of mines’ safety conditions. The prevention and control systems to manage risks and hazard are not in place, and the working environment in mines remain difficult. Therefore, to have enhanced mine safety, important factors can be addressed in chronological order, as presented in this paper. The proposed methodological approach can be applied to other prioritization applications to analyze the indicators and determine the appropriate management strategies.