Cause analysis of construction collapse accidents using association rule mining

事故(哲学) 关联规则学习 事故分析 风险分析(工程) 工程类 独创性 法律工程学 计算机科学 运筹学 数据挖掘 业务 心理学 社会心理学 哲学 认识论 创造力
作者
Lijia Shao,Shengyu Guo,Yimeng Dong,Hongying Niu,Pan Zhang
出处
期刊:Engineering, Construction and Architectural Management [Emerald Publishing Limited]
卷期号:30 (9): 4120-4142 被引量:20
标识
DOI:10.1108/ecam-11-2021-0991
摘要

Purpose The construction collapse is one of the most serious accidents since it has several attributes (e.g. accident type and consequence) and its occurrence involves various kinds of causal factors (e.g. human factors). The impact of causal factors on construction collapse accidents and the interrelationships among causal factors remain poorly explored. Thus, the purpose of this paper is to use association rule mining (ARM) for cause analysis of construction collapse accidents. Design/methodology/approach An accident analytic framework is developed to determine the accident attributes and causal factors, and then ARM is introduced as the method for data mining. The data are from 620 historical accident records on government websites of China from 2010 to 2020. Through the generated association rules, the impact of causal factors and the interrelationships among causal factors are explored. Findings Collapse accident is easily caused by human factors, material and machine condition and management factors. Furthermore, the results show a close interrelationship between many causal factors and construction scheme and organization. The earthwork collapse is greatly related to environmental condition and the scaffolding collapse is greatly related to material and machine condition. Practical implications This study found relevant knowledge about the key causes for different types of construction collapses. Besides, several suggestions are further provided for construction units to prevent construction collapse accidents. Originality/value This study uses data mining methods to extract knowledge about the causes of collapse accidents. The impact of causal factors on various types of construction collapse accidents and the interrelationships among causal factors are explained from historical accident data.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
上官若男应助yutos采纳,获得10
1秒前
2秒前
2秒前
Sslya完成签到,获得积分10
2秒前
爆米花应助番茄采纳,获得10
2秒前
4秒前
5秒前
发发完成签到,获得积分10
5秒前
xi发布了新的文献求助10
6秒前
6秒前
22222发布了新的文献求助10
7秒前
7秒前
不想晚睡发布了新的文献求助10
7秒前
乐乐应助zzt采纳,获得10
7秒前
realwd发布了新的文献求助10
8秒前
复杂的以丹完成签到,获得积分20
8秒前
9秒前
姿势发布了新的文献求助10
9秒前
tmnns完成签到,获得积分10
10秒前
fangsci发布了新的文献求助10
10秒前
Sh_Wen发布了新的文献求助10
11秒前
12秒前
棋士发布了新的文献求助10
12秒前
哆啦发布了新的文献求助10
14秒前
科研通AI6.1应助Zing采纳,获得10
14秒前
15秒前
烟花应助Jane采纳,获得10
15秒前
阳光的外套完成签到,获得积分10
15秒前
云落完成签到 ,获得积分10
16秒前
机灵大米完成签到,获得积分20
16秒前
16秒前
17秒前
稽TR发布了新的文献求助10
18秒前
fangsci完成签到,获得积分20
18秒前
19秒前
19秒前
机灵大米发布了新的文献求助10
20秒前
medai完成签到,获得积分10
21秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Digital Twins of Advanced Materials Processing 2000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6041710
求助须知:如何正确求助?哪些是违规求助? 7783195
关于积分的说明 16235335
捐赠科研通 5187649
什么是DOI,文献DOI怎么找? 2775847
邀请新用户注册赠送积分活动 1759092
关于科研通互助平台的介绍 1642520