一个基于PDCA和数据挖掘方法的模型,用于预防建筑行业的水管业务中的职业事故
Rodolfo Mosquera1, Ileana G Pérez Vergara2, Orlando E Contreras-Pacheco1
1Escuela de Estudios Industriales y Empresariales, Universidad Industrial de Santander, Bucaramanga, Colombia.
Work (Reading, Mass.)
|January 26, 2024
概括
一个使用数据挖掘和"计划-执行-检查-采取行动"循环的新模型有效地预测了建筑中的管道事故. 这有助于减少缺勤率,并通过识别生物和化学因素等风险来提高生产率.
科学领域:
- 职业健康和安全问题 职业健康和安全问题
- 数据挖掘和机器学习
- 建筑工程工程 建筑工程
背景情况:
- 发展中国家建筑行业的水管工程职业事故导致大量的缺勤率和企业生产率的降低.
- 识别和减轻这些风险对于工人的福利和经济效率至关重要.
研究的目的:
- 开发和提出一个在管道活动中的职业事故预测模型.
- 该模型将计划-执行-检查-行动 (PDCA) 周期与数据挖掘技术相结合,以加强预防.
主要方法:
- 一项涉及200名男性管道工人的横截面研究.
- 对生物,生物机械,化学和物理风险因素的分析.
- 逻辑回归,天真贝斯和决策树 (J48) 算法的对比用于事故分类.
- 模型验证使用20%的收集数据和应用到哥伦比亚建筑公司17年的事故数据.
主要成果:
- 决策树 (J48) 分类器在识别潜在的职业事故时实现了90.5%的准确性.
- 该模型有效地利用生物,化学和生物机械风险因素进行预测.
- 该研究证明了该模型在预测事故发生方面的效率.
结论:
- 拟议的数据挖掘和PDCA模型是预测和预防建筑管道行业职业事故的有希望的工具.
- 这些发现使得基于已识别的事故原因,能够制定有针对性的风险减轻策略.
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