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基于AHP和异常检测算法模型的道建设工人安全状态预测和管理系统
1School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, Hubei, China.
Heliyon
|September 12, 2024
概括
本研究介绍了道工人安全的道-APH-AD模型,使用机器学习来分析生理和环境因素. 集体学习提高了不安全条件的检测,提高了整体建筑安全管理.
科学领域:
- 土木工程 土木工程是指土木工程.
- 职业安全与健康问题 职业安全与健康问题
- 机器学习应用 机器学习应用
背景情况:
- 由于复杂的环境和苛刻的条件,道建设存在重大安全风险.
- 基于静态因素的传统安全分析对于现代道工程来说是不够的.
- 工人的安全受到生理指标和内部环境条件的影响.
研究的目的:
- 综合分析影响道工人安全的因素.
- 开发一个新的安全评估模型,道-APH-AD,纳入关键的确定因素.
- 提高道施工中的安全警报的准确性和可靠性.
主要方法:
- 利用分析层次过程 (AHP) 识别了七个关键安全因素.
- 采用数据增强和四种不同的机器学习模型来检测异常.
- 应用集体学习技术来汇总模型预测,以改进安全状态检测.
主要成果:
- 集体学习模型在检测安全状态方面表现优于单个模型.
- 模型在分布之外的样本上进行了评估,显示出稳定性和通用性.
- 实验验证证证实了模型的可解释性,可扩展性和科学概括性.
结论:
- 集体学习有效地提高了道工人安全警报的准确性和可靠性.
- 道-APH-AD模型为主动安全管理提供了机器学习的科学概括应用.
- 这些发现有助于推进道工程的安全实践,确保工人福祉.
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