东北大学学报:自然科学版 ›› 2018, Vol. 39 ›› Issue (7): 1048-1052.DOI: 10.12068/j.issn.1005-3026.2018.07.027

• 资源与土木工程 • 上一篇    下一篇

基于复合模型的轨道运输事故风险分析

徐青伟, 许开立   

  1. (东北大学 资源与土木工程学院, 辽宁 沈阳110819)
  • 收稿日期:2017-03-26 修回日期:2017-03-26 出版日期:2018-07-15 发布日期:2018-07-11
  • 通讯作者: 徐青伟
  • 作者简介:徐青伟(1990-),男,河南民权人,东北大学博士研究生; 许开立(1965-),男,山东郓城人,东北大学教授,博士生导师.冯明杰(1971-), 男, 河南禹州人, 东北大学副教授; 王恩刚(1962-), 男, 辽宁沈阳人, 东北大学教授,博士生导师.
  • 基金资助:
    国家重点研发计划项目(2017YFC0805100).国家自然科学基金资助项目(51171041).

Risk Analysis of Rail Haulage Accident Based on Composite Model

XU Qing-wei, XU Kai-li   

  1. School of Resources & Civil Engineering, Northeastern University, Shenyang 110819, China.
  • Received:2017-03-26 Revised:2017-03-26 Online:2018-07-15 Published:2018-07-11
  • Contact: XU Kai-li
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摘要: 为了系统化地分析斜巷轨道运输事故,提出了在贝叶斯网络的基础上融合预先危险性分析-保护层分析(PHA-LOPA)、蝴蝶结分析于一体的复合模型风险分析方法.首先,借助于GeNIe软件实现贝叶斯网络的双向推理能力,辨识出风险贝叶斯事故节点;然后,对事故节点进行PHA-LOPA研究,确定引起事故的原因、造成的结果,设置独立保护层降低事故节点的危险性等级;最后,对剩余危险性等级仍然较高的事故节点进行蝴蝶结分析,设置安全屏障,进一步控制事故的发生.以某矿斜巷轨道运输事故为例,应用该复合模型风险分析方法,结果验证了所提方法的正确性和可行性.

关键词: 贝叶斯网络, 预先危险性分析, 保护层分析, 蝴蝶结分析, 轨道运输事故

Abstract: A composite risk analysis model was put forward to analyze the rail haulage accident in inclined tunnel systematically. The model combines preliminary hazard analysis-layer of protection analysis (PHA-LOPA) with bow-tie analysis together based on Bayesian network. First, taking advantage of two-way reasoning ability of Bayesian network identified the risk Bayesian accident nodes with the help of GeNIe software. Second, the accident nodes was studied using PHA-LOPA to determine the causes and results of the accident, and to set independent protection layer to reduce the risk level of the accident nodes. Third, bow-tie analysis was performed on the accident node where the residual risk level was still high, safety barriers were set and the occurrence of the accident was further controlled. The proposed composite risk analysis model was applied to a rail haulage accident in inclined tunnel in a mine, and the validity and feasibility was verified by the results.

Key words: Bayesian network, preliminary hazard analysis, layer of protection analysis, bow-tie analysis, rail haulage accident

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