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论文中文题名:

 榆蓝线清坪川隧道项目施工阶段安全风险管理    

姓名:

 杨瑞娟    

学号:

 G2015261    

学生类型:

 工程硕士    

学位年度:

 2019    

院系:

 管理学院    

专业:

 项目管理    

第一导师姓名:

 孙林辉    

论文外文题名:

 Safety Risk Management in Construction Stage of Qingpingchuan Tunnel Project on Yulan Line    

论文中文关键词:

 隧道施工安全 ; WBS-RBS ; 事故树 ; 风险源辨识    

论文外文关键词:

 Tunnel Construction Safety ; WBS-RBS ; Fault Tree ; Risk Source Identification    

论文中文摘要:
摘要 近年来,我国交通事业发展飞快,尤其是公路建设。但公路运能在部分地区出现饱和,出现常态化拥堵,因此提高其服务水平,缩短运输距离,实现高效运输势在必行。但在高等级线路中,桥梁和隧道里程比例很大。而隧道施工属于地下作业,投资大,工期长,技术难度大,作业环境恶劣,风险因素错综复杂,事故频发。因此,隧道施工风险控制是控制投资与工期的必要手段。基于此,本文在以往研究的基础上,以榆蓝线清坪川隧道为研究对象,对整个隧道施工阶段进行风险识别、评价,最后针对不同等级的风险源,提出了相应的控制策略。 清坪川隧道位于陕北高原地区,是湿陷性黄土隧道,风险因素众多。首先,采用WBS-RBS法辨识风险源,按施工流程逐项分析每个分项工程存在的风险,形成耦合矩阵,全面识别风险源,并以此为基础建立了以洞口失稳和洞身塌方为顶事件的两个事故树。其次,本文采用专家打分法、问卷调查法、统计分析法获取各风险源的发生概率,运用事故树模型,通过编程计算清坪川隧道施工项目各个事故树顶事件发生概率,以及各基本事件对顶事件的影响大小,进而找到导致顶事件发生的最敏感因素,并按敏感度大小排序。最后,根据分析结果有针对性的提出控制措施,确保清坪川隧道的施工安全。 本文主要研究结论有以下几方面: 1)采用WBS-RBS耦合的方法全面分析清坪川隧道施工阶段的风险源,在此基础上建立事故树。 2)运用事故树模型和算法,通过计算机编程计算清坪川隧道施工阶段各个顶事件发生概率及基本事件对顶事件的敏感度。 3)根据计算结果,有针对性的提出清坪川隧道施工阶段安全控制措施。 关 键 词:隧道施工安全、WBS-RBS、事故树、风险源辨识 论文类型:应用研究
论文外文摘要:
ABSTRACT In recent years, China's transportation industry has developed rapidly, especially on highways. However, the traffic capacity is saturated in some areas, leading to Normalized traffic congestion. So it is imperative to improve service level, shorten the transportation distance, and realize the efficient transportation. But in high-level lines, the proportion of bridge and tunnel mileage in highway will be very large. Tunnel construction belongs to underground operation, with large investment, long construction period, great technical difficulty, bad working environment, complicated risk factors and frequent accidents. Therefore, the risk control of tunnel construction is the necessary means to control the investment and construction period. On the basis of the previous research, this paper takes Qingpingchuan tunnel of Yulan line as the research object, carries on the risk identification and evaluation to the whole tunnel construction stage, finally puts forward the corresponding control strategy to the risk source of different levels. Qingpingchuan Tunnel, located in the northern Shaanxi Plateau, is a collapsible loess tunnel with many risk factors. First of all, the WBS-RBS method is used to identify the risk source. According to the construction process, the risk existing in each sub-project is analyzed one by one, and the coupling matrix is formed to identify the risk source in an all-round way. On the basis of this, two fault trees are established, in which the Portal Instability and Collapse of tunnel body are the top events. Secondly, this paper adopts expert evaluation method, questionnaire survey method and statistical analysis method to obtain the occurrence probability of each risk sources. Meanwhile using fault tree model, this paper calculates the probability of each fault tree top event of Qingpingchuan tunnel construction project by programming, and the impact of each basic event on the top event. And then this paper finds the most sensitive factors leading to the occurrence of the top event according to the size of the sensitivity order. Finally, according to the results of the analysis, the control measures are put forward to ensure the construction safety of Qingpingchuan tunnel. The main conclusions of this paper are as follows: 1) the WBS-RBS coupling method is used to analyze the risk sources of Qingpingchuan tunnel construction stage, and on this basis, the fault trees are established. 2) by using the fault tree model and algorithm, the probability of the base events and the sensitivity of the basic events to the top events in the construction stage of Qingpingchuan Tunnel are calculated by the program. 3) according to the calculation results, the safety control measures of Qingpingchuan tunnel during construction stage are put forward. Key words: Tunnel Construction Safety, WBS-RBS, Fault Tree, Risk Source Identification Thesis : Application Research
中图分类号:

 TU714    

开放日期:

 2019-06-11    

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