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数据驱动的贝叶斯网络用于风险场景映射高空落事故的风险场景映射
1School of Transportation, Changsha University of Science & Technology, Changsha, China.
PloS one
|October 14, 2025
概括
从高处落 (FFH) 是一个主要的建筑危险. 这项研究使用风险场景和贝叶斯网络来确定关键因素和概率,改善FFH事故的安全管理和预防策略.
科学领域:
- 建筑工程安全 建筑工程安全
- 职业健康 职业健康 职业健康
- 风险管理 风险管理
背景情况:
- 从高处落 (FFH) 是建筑行业事故的主要原因,导致重大经济损失和工人的安全问题.
- 现有的风险分析方法需要改进,以充分捕捉FFH事故因素的复杂性和不确定性.
- 更深入地了解潜在的机制和驱动因素对于有效预防事故至关重要.
研究的目的:
- 通过风险场景方法调查从高处落 (FFH) 事故的潜在机制和驱动因素.
- 发展对建筑安全风险复杂性和不确定性的全面理解.
- 为加强FFH事故预防战略提供数据驱动的见解.
主要方法:
- 从2014年到2024年收集和分析了368份FFH事故报告.
- 基于经过验证的事故报告数据开发了贝叶斯网络模型.
- 通过开发的模型,系统地探索了FFH事故的各种风险场景.
主要成果:
- 确定了影响FFH事故发生的五个核心维度.
- 在这些维度内确定了五个高风险变量.
- 量化了FFH事故的概率在不同的确定风险场景.
结论:
- 基于场景的方法为建筑安全管理提供了新的见解.
- 这些发现为改善FFH在建筑项目中的事故预防策略提供了有价值的启示.
- 对风险因素和情景的更好理解可以显著降低FFH事故率.
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