• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Symposium on Automation and Robotics in Construction and Mining >Leading Safety Indicators: Application of Machine Learning for Safety Performance Measurement
【24h】

Leading Safety Indicators: Application of Machine Learning for Safety Performance Measurement

机译：领先的安全指标：机器学习在安全性能测量中的应用

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Proactive approaches designed to prevent incidents before they occur are essential for achieving effective safety management. Emerging as an important component of proactive safety management, leading indicators are used to assess and control safety performance. With the aim of reducing the number or severity of worksite accidents, methods capable of predicting future safety performance using leading safety indicators have been developed. However, these methods have been developed for a specific set of leading indicators. This has substantially limited their application in practice, as leading indicators with the greatest impact on safety performance vary considerably between organizations and projects. An approach for predicting accident occurrence on construction sites that can be applied to any combination of leading indicators is proposed to address these limitations. Data used to develop the proposed approach were collected by a construction company from eight construction projects over a period of two years. Feature selection techniques were used to filter the original factors into the most critical subset, which were then used as inputs. Various supervised learning algorithms, namely support vector machine (SVM), logistic regression, and random forest, were then tested to determine which algorithm(s) yielded the highest prediction accuracy. The results demonstrate that the proposed procedure can be used for early recognition of potentially hazardous project characteristics and site conditions regardless of the number or type of leading indicators available within an organization. Research in this area is expected to facilitate the implementation of targeted safety management controls and to improve safety performance.
机译：主动方法旨在防止发生事件，以便在实现有效的安全管理方面是必不可少的。作为主动安全管理的重要组成部分，主要指标用于评估和控制安全性能。旨在减少工地事故的数量或严重程度，已经开发了使用领先的安全指标预测未来安全性能的方法。但是，已经为特定的领先指标制定了这些方法。这在实践中基本上限制了其申请，因为对安全性能影响最大的领先指标在组织和项目之间有很大差异。提出了一种预测可以应用于任何领先指标组合的建筑地点的事故发生的方法，以解决这些限制。用于开发所提出的方法的数据由建筑公司从八个建筑项目收集，超过两年的建筑项目。特征选择技术用于将原始因素过滤到最关键的子集中，然后将其作为输入用作输入。然后测试各种监督学习算法，即支持向量机（SVM），逻辑回归和随机森林，以确定哪些算法产生了最高的预测精度。结果表明，拟议的程序可用于早期识别潜在危险的项目特征和现场条件，而不管组织内可用的领先指标的数量或类型。该领域的研究有望促进目标安全管理管制的实施，并提高安全性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号