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研究如何对铁路黄土隧道塌方风险事故进行管理控制,先从地质因素、施工方法因素、监控量测因素和施工管理因素分析了黄土隧道塌方事故的成因。接着依据铁路行业隧道风险评估指南矿山法隧道施工风险因素列表,识别影响兰渝铁路某黄土隧道项目塌方的18项主要风险因素。然后运用系统工程解释结构模型方法(ISM)分析项目各风险因素间的相互关系,得出影响该隧道项目塌方的直接原因为施工扰动过大、支护不及时、拱脚悬空时间过长和信息反馈处理不及时。底层原因则为开挖方式、特殊地质条件。在上述基础上建立项目塌方事件模糊故障树模型,进行模糊定量分析,得到项目塌方事件发生的模糊概率和关键风险因素的模糊重要度。结果表明,实例黄土隧道项目塌方事故风险概率很大,地质因素、开挖方法等对塌方事故影响最大。解释结构模型方法能揭示各风险要素间的不确定性因果关系,有利于把握事故发生机理,模糊数能客观地描述风险事件的发生概率,增强故障树诊断方法的可靠性。
This paper studies how to manage and control the risk of landslide accidents in railway loess tunnel. The causes of landslip accidents in loess tunnel are analyzed firstly from geological factors, construction method factors, monitoring and measuring factors and construction management factors. Then, based on the list of risk factors for construction of tunnels in the Mines and Laws of the tunnel industry risk assessment guideline, 18 key risk factors affecting the landslip of a loess tunnel project on the Lanzhou-Chongqing Railway are identified. Then the ISM method is used to analyze the relationship between the risk factors of the project. The direct causes of the landslides affecting the tunnel project are as follows: the construction disturbance is too large, the support is not timely, the hanging time of the arch foot is too long and the information Feedback processing is not timely. The underlying reason is the way of excavation, special geological conditions. Based on the above, the fuzzy fault tree model of project collapse event is established and the fuzzy quantitative analysis is carried out to get the fuzzy importance and the fuzzy importance of the key risk factors of the project. The results show that the probability of landslip accidents in case of loess tunnel projects is large, and geological factors and excavation methods have the greatest impact on landslip accidents. Interpretation of the structural model can reveal the causality of uncertainty between the various risk factors, is conducive to grasp the mechanism of accident, fuzzy number can objectively describe the probability of occurrence of risk events, and enhance the reliability of fault tree diagnosis method.