期刊论文

Analysis of causes and association rules for falling accidents based on text mining method

中文

长沙理工大学学报(自然科学版)

1672-9331

2020

17

2

61-67+74

10.3969/j.issn.1672-9331.2020.02.010

国家自然科学基金资助项目（71371036）； 湖南省研究生科研创新项目（CX20190669）)；

本校为第一机构

高处坠落事故是建筑业中最常见的事故之一。为明确高处坠落事故致因,找到有效的事故预防措施,搜集了2012~2017年国内528例高处坠落事故调查报告,以R语言为平台,结合文本挖掘技术确定了事故中的32项事故致因,其中包括7项关键事故致因。利用Apriori算法挖掘出事故致因之间的关联规则,通过可视化关联规则结果,得到了高处坠落事故发生过程中的事故致因因果关系,为高处坠落事故的预控提供参考。

Falling accidents is one of the most common accidents in construction industry.In order to clarify causes of falling accidents and find effective prevention measures,528 domestic investigation reports of falling accidents from 2012 to 2017 were collected.Based on R language and text mining technology,32 causes of accident were identified,including 7 key causes.Apriori algorithm was used to mine association rules among the accident causes.By visualizing the results of the association rules,the causalities of the accident causes during the falling accidents were obtained,...