公路隧道新奥法施工全过程风险管理研究

发布时间：2018-02-16 02:51

本文关键词： 公路隧道新奥法风险管理模糊理论 BP神经网络　出处：《青岛理工大学》2015年硕士论文　论文类型：学位论文

【摘要】：公路隧道是公路网中不可或缺的重要组成部分,已成为公路工程建设中重点管控对象之一。其技术含量高、成本集中密、安全隐患多等特性,决定了在隧道项目建设中,必须详细的分析其各阶段、各过程的致险因子,通过及时有效的经济技术等手段,才能保证它的有效投资及质量安全。新奥法在我国公路隧道建设过程中,经过不断的发展和完善,目前已成为我国公路、铁路山岭隧道施工建设的主要方法之一。虽然现阶段国内外有许多专业人士、学者对新奥法施工中的风险管理进行了不同侧重的研究,但大都集中在较笼统的对隧道全长范围内的风险进行评价,忽视了复杂地质段中施工致险因素非线性变化的事实。本文立足福建某高速公路中的ZY隧道左洞为例,依据AHP-Fuzzy-BP神经网络的结构模型对隧道全长、全过程范围内的风险进行“分-总”式的评价。主要研究内容如下:(1)结合国内外对于新奥法隧道施工技术的实践经验以及相关学者的研究文献,本文采用因果分析图法(鱼刺图)对隧道施工全过程的危险因素进行识别。(2)对识别出的致险因素归纳总结、分类,建立风险评价指标体系,利用层次分析法(AHP)确定系统指标权重。(3)运用模糊理论(Fuzzy)中的主观理想点法,将隧道施工全过程中的定性、定量指标参考化,得出此风险评价指标体系的相应标准参考值表。并通过线性内插足够的数据作为BP神经网络的输入训练数据向量,将对应的AHP-Fuzzy系统得出的模糊综合评价值作为输出向量训练数据,通过训练神经网络并检验就建立起了AHP-Fuzzy-BP神经网络评价模型。(4)以ZY隧道左洞为例,将隧道按照不同地质构造或主、客观施工条件分为若干“单元段”,运用建立的AHP-Fuzzy-BP评价模型,将各单元段对应风险指标值作为BP神经网络测试输入向量,可得出各“单元段”的风险评价结果。通过与隧道风险分析报告对比证明该模型在隧道施工风险评价中的正确性与适用性。(5)对上述评价结果进行分析,列出在公路隧道新奥法施工全过程中主要致险因子的应对措施及控制方法,为今后的新奥法隧道施工风险管理提供一定的指导与借鉴。
[Abstract]:Highway tunnel is an indispensable part of highway network, and has become one of the key control objects in highway engineering construction. In order to ensure its effective investment and quality safety, it is necessary to analyze the risk factors of each stage and process in detail, and to ensure its effective investment and quality safety by means of timely and effective economic and technological means. Through continuous development and improvement, it has now become one of the main methods for the construction of highway and railway mountain tunnels in China. Although there are many professionals at home and abroad at the present stage, Scholars have carried out different studies on risk management in the construction of the New Olympic method, but most of them are focused on the evaluation of the risk in the full-length scope of the tunnel. The nonlinear variation of construction risk factors in complex geological section is ignored. Based on the example of the left tunnel of ZY tunnel in a highway in Fujian Province, the length of the tunnel is analyzed according to the structural model of AHP-Fuzzy-BP neural network. The main research contents are as follows: (1) combining the domestic and foreign practical experience on the construction technology of the New Austrian tunneling and the research literature of relevant scholars, In this paper, the causality analysis diagram (fishthorn chart) is used to identify the risk factors in the whole process of tunnel construction. The risk factors identified are summarized, classified, and the risk evaluation index system is established. Using the analytic hierarchy process (AHP) to determine the index weight of the system, the subjective ideal point method in fuzzy theory is used to refer the qualitative and quantitative indexes in the whole process of tunnel construction. The corresponding standard reference value table of the risk evaluation index system is obtained, and the input training data vector of BP neural network is obtained by linear interpolation enough data. Taking the fuzzy comprehensive evaluation value obtained from the corresponding AHP-Fuzzy system as the training data of the output vector, the evaluation model of AHP-Fuzzy-BP neural network is established by training and testing the neural network. (4) taking the left hole of the ZY tunnel as an example, the tunnel is divided into different geological structures or main structures. Objective construction conditions are divided into several "unit segments". Using the established AHP-Fuzzy-BP evaluation model, the corresponding risk index values of each unit segment are used as the BP neural network test input vector. The risk evaluation results of each "unit section" can be obtained. The correctness and applicability of the model in tunnel construction risk evaluation are proved by comparing with the tunnel risk analysis report. This paper lists the countermeasures and control methods of the main risk factors in the whole course of the construction of the road tunnel, and provides some guidance and reference for the construction risk management of the tunnel in the future.
【学位授予单位】：青岛理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U455.48

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