cause of object-striking incident;
Bayes;
Apriori algorithm;
FP-Growth algorithm;
association rules;
D O I:
10.3390/su152115609
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
Incidents are caused by a variety of factors, and there are correlations between incident causative factors. How to effectively clarify the importance of incidental injury-causing factors and their correlations is the current technical challenge in the field of incident causation analysis. This paper takes the study of injury-causing factors and their relationships between object-striking incidents in the process of construction as an example, and it statistically analyzes the incident investigation reports of 126 cases of object-striking incidents in construction projects in China from 2016 to 2022; it screens out 52 categories of incident-causing factors. The Apriori algorithm and FP-growth algorithm are used to data mine the influencing factors obtained from the 126 object-striking incidents: 28 main incident causative items of object-striking incidents and the respective correlation degree between each factor are obtained. By analyzing the support degree of the main incident causation items, as well as comparing and analyzing the results of the incident causation support degree and association rules with Bayesian inference, 9 key injury-causing factors of object-striking incidents are identified. The research results put forward a new research idea for the analysis of the injury factors of object-striking incidents in construction, which can provide theoretical reference for improving the pertinence and effectiveness of incident prevention measures.
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页数:24
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