• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Reliability Engineering & System Safety >Understanding complex blasting operations: A structural equation model combining Bayesian networks and latent class clustering
【24h】

Understanding complex blasting operations: A structural equation model combining Bayesian networks and latent class clustering

机译：了解复杂的爆破作业：结合贝叶斯网络和潜在类聚类的结构方程模型

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A probabilistic Structural Equation Model (SEM) based on a Bayesian network construction is introduced to perform effective safety assessments for technicians and managers working on-site. Using novel AI software, the introduced methodology aims to show how to deal with complex scenarios in blasting operations, where typologically different variables are involved. Sequential Bayesian networks, learned from the data, were developed while variables were grouped into different clusters, representing related risks. From each cluster, a latent variable is induced giving rise to a final Bayesian network where cause and effect relationships maximize the prediction of the accident type. This hierarchical structure allows to evaluate different operational strategies, as well as analyze using information theory the weight of the different risk groups. The results obtained unveil hidden patterns in the occurrence of accidents due to flyrock phenomena regarding the explosive employed or the work characteristics. The integration of latent class clustering in the process proves to be an effective safeguard to categorize the variable of interest outside of personal cognitive biases. Finally, the model design and the software applied to show a flexible workflow, where workers at different corporate levels can feel engaged to try their beliefs to design safety interventions.
机译：介绍了一种基于贝叶斯网络构造的概率结构方程模型（SEM），以对现场工作的技术人员和管理人员进行有效的安全评估。引入的方法使用新颖的AI软件，旨在展示爆破作业中涉及类型不同的变量的复杂情况。从数据中学到的顺序贝叶斯网络得到了发展，同时变量被分组到不同的集群中，代表了相关的风险。从每个聚类中诱发一个潜在变量，产生最终的贝叶斯网络，在该网络中因果关系使事故类型的预测最大化。这种层次结构允许评估不同的操作策略，以及使用信息论分析不同风险组的权重。所获得的结果揭示了由于所使用的炸药或工作特性而产生的飞石现象导致的事故隐患。潜在类别聚类在该过程中的整合被证明是一种有效的保护措施，可以将兴趣变量分类为个人认知偏差之外的事物。最后，模型设计和软件用于显示灵活的工作流程，使公司各个级别的工人都可以参与其中，以尝试自己的信念来设计安全性干预措施。

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