地铁施工工人不安全行为关联规则研究

当前工人不安全行为研究多侧重从理论和方法的角度出发，而通过数据挖掘探究规律性方面的研究存在不足，因此，提出了地铁施工工人不安全行为关联规则研究。首先，构建关联规则挖掘数据库，以大量反映现场不安全行为的照片为数据来源。然后，利用Apriori算法，通过SPSS Modeler软件建模，以地铁车站施工机械操作人员为例介绍关联规则挖掘过程和结果。结果表明:机械操作人员存在有效强关联项为“开挖降水→挖土机作业时周围区域内有其余工人作业活动”。说明针对不同工种岗位的工人，在不同施工阶段存在易出现的不安全行为，可以有针对性地进行控制与管理，从而降低事故率。

Research on association rules of unsafe behavior for metro construction workers

Based on the present situation that the current study on unsafe behavior of workers emphasizes to proceed from the perspective of theory and method, but the study to explore the regularity through data mining is unsufficient, a study on association rules of unsafe behavior for metro construction workers was put forward. Firstly, a mining database of association rules was built, which took a large number of photos about field unsafe behavior as data source. What’s more, by using Apriori algorithm, a model was established through SPSS Modeler software, and the data mining process and result of association rules were introduced by taking machine operators in metro station construction as example. The results showed that the machine operators have one effective strong association term, which is excavation and precipitation work→the excavator is operating when other workers work at the surrounding area. Aiming at workers in posts of different work type, the common unsafe behavior exist in different construction stages, which can be pertinently controlled and managed to reduce the accident rate.