Electric shock accident is one of the main causes of fatal construction accidents. In this study, 101 electric shock accidents are analyzed to mine the potential associations of human errors. The modified Human Factors Analysis and Classification System (HFACS) is used to classify human factors of accident causes. Characteristics and potential causes of the accidents are identified by employing frequency analysis. Chi-square test and Apriori algorithm are utilized to explore the associations among the causes. Some significant association between any of two factors are shared. According to association rules using three criteria: support ( S), confidence ( C) and lift ( L), the two key paths are extracted based on the hierarchy of the HFACS. One is: organizational process loopholes → failed to correct problem → perceptual and decision errors ( S = 0•11, C = 0•423, L = 1•02), and the other is: organizational process loopholes!poor skill level ofworkers!routine violation ( S = 0•149, C = 0•789, L = 1•945). Managerial implications are proposed to prevent or reduce accidents based on interconnections of factors and key paths.

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.