Electrocution is one of the main causes of workplace deaths in the construction industry. This paper presents a framework for identifying electrocution risk factors and exploring the correlations between them, with the aim of assisting accident prevention research. Specifically, the Haddon Matrix is used to extract the risk factors from 193 investigation reports of electrical shock accidents from 2012-2019, and the Apriori algorithm is applied to examine the potential relationships between these factors. Based on association rules using three criteria: support (S), confidence (C) and lift (L), the betweenness centrality is then introduced to optimize association rules and find the most important rules though comparison. The results show that after optimization, some of these critical rules rise significantly in rank, such as Workplace: indoor → No CPR provided. Through these ranking changes, the focus of safety management is clarified, and finally, based on a comprehensive analysis of association rules, targeted accident prevention measures are suggested.
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The betweenness centrality is one of the basic concepts in the analysis of the social networks. Initial definition for the betweenness of a node in the graph is based on the fraction of the number of geodesics (shortest paths) between any two nodes that given node lies on, to the total number of the shortest paths connecting these nodes. This method has polynomial complexity. We propose a new concept of the betweenness centrality for weighted graphs using the methods of cooperative game theory. The characteristic function is determined by special way for different coalitions (subsets of the graph). Two approaches are used to determine the characteristic function. In the first approach the characteristic function is determined via the number of direct and indirect weighted connecting paths in the coalition. In the second approach the coalition is considered as an electric network and the characteristic function is determined as a total current in this network. We use the Kirchhoff's law. After that the betweenness centrality is determined as the Myerson value. The results of computer simulations for some examples of networks, in particular, for the popular social network "VKontakte", as well as the comparing with the PageRank method are presented.
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