用集成的机器学习方法对造船厂事故的因果关系进行分层方法
1Faculty of Health Sciences, Department of Occupational Health and Safety, Istanbul Yeni Yuzyil University, Istanbul, Türkiye.
Work (Reading, Mass.)
|February 19, 2026
概括
造船厂事故,特别是重大事故,往往是由于缺乏控制和审计等行政缺陷造成的. 许多严重的造船厂事故是可以预防的,之前的轻微事件表明员工的风险更高.
科学领域:
- 职业安全与健康问题 职业安全与健康问题
- 工业工程 工业工程 工业工程
- 风险管理 风险管理
背景情况:
- 造船厂事故带来重大风险,需要检查影响事故严重性的因素.
- 了解人口,工作场所和行为因素对于降低事故率至关重要.
研究的目的:
- 分析造船厂事故,识别导致小事故升级为重大事故的因素.
- 评估职业,行为和环境因素对事故后果的影响.
- 将预测的事故结果与实际结果进行比较,并揭示潜在原因.
主要方法:
- 在造船行业使用了多因素方法的事故因果关系模型.
- 运用后勤回归和机器学习技术进行事故因果关系分析.
- 进行了关联规则分析,以完善因果关系模型.
主要成果:
- 机器学习揭示了明显事故原因分布的显著偏差.
- 缺乏控制和审计被认为是造成重大事故的主要因素.
- 设计错误和不充分的培训是主要的行政贡献者;38.2%的重大事故是可以预防的,往往是小事故的先发.
结论:
- 行政上的缺陷是造成严重造船厂事故的主要原因.
- 工人和管理人员面临着更高的重大事故暴露风险.
- 安全培训的有效性必须适应不断变化的工作环境和技术.
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