关联规则学习
Lift(数据挖掘)
计算机科学
风险分析(工程)
过程(计算)
风险管理
交叉验证
风险评估
工作(物理)
可靠性工程
运筹学
数据挖掘
工程类
人工智能
计算机安全
业务
机械工程
操作系统
财务
作者
Qianqian Chen,Zhen Tian,Tian Liu,Shenghan Huang
出处
期刊:Engineering, Construction and Architectural Management
[Emerald (MCB UP)]
日期:2022-08-12
标识
DOI:10.1108/ecam-09-2021-0792
摘要
Purpose Cross operation is a common operation method in the building construction process nowadays. Due to the crossover, each other's operations are disturbed, and risks also interact. This superimposed relationship of risks is worthy of attention. The study aims to develop a model for analyzing cross-working risks. This model can quantify the correlation of various risk factors. Design/methodology/approach The concept of cross operation and the cross types involved are clarified. The risk factors were extracted from cross-operation accidents. The association rule mining (ARM) was used to analyze the results of various cross-types accidents. With the help of visualization tools, the intensity distribution and correlation path of the relationship between each factor were obtained. A complete cross-operation risk analysis model was established. Findings The application of ARM method proves that there are obvious risk correlation deviations in different types of cross operations. A high-frequency risk common to all cross operations is on-site safety inspection and process supervision, but the subsequent problems are different. Cutting off the high-lift risk chain timely according to the results obtained by ARM can reduce or eliminate the danger of high-frequency risk factors. Originality/value This is the first systematic analysis of cross-work risk in the construction. The study determined the priority of risk management. The results contribute to targeted cross-work control to reduce accidents caused by cross-work.
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