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基于显示连通贝叶斯网络的机场塔台 地震易损性分析
Earthquake fragility evaluation of air traffic control tower based on explicit connectivity Bayesian Network
投稿时间:2018-11-13  修订日期:2018-12-08
DOI:
中文关键词:  机场塔台  多维易损性  显示连通贝叶斯网络  层次分析法  易损性曲线
英文关键词:Air traffic control tower  multidimensional fragility  explicit connectivity Bayesian network  Analytic Hierarchy Process  fragility curve
基金项目:国家自然科学基金项目(面上项目,重点项目,重大项目)
作者单位E-mail
贾大卫 西北工业大学 dwjnwpu@163.com 
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中文摘要:
      由于目前缺少针对塔台结构地震易损性的分析方法,本文将基于性能的多维易损性分析方法,结合显示连通贝叶斯网络应用于机场塔台的多维易损性分析。建立塔台的有限元模型,考虑地震激励的不确定性,通过非线性时程分析获得响应数据;将塔台结构系统分为三个层次,每个层次按包含的层数分为相应的子层次。根据功能特性确定子层次中构件的评价指标和极限状态,建立服从多元对数正太分布的概率地震需求模型;考虑各种极限状态之间的相关性,建立极限状态方程,确定失效域,通过蒙特卡洛法求得构件的超越概率;建立塔台结构的显示连通贝叶斯网络模型,利用层次分析法获得中间节点的条件概率表,利用MATLAB进行贝叶斯网络的推理计算,得到整体结构的超越概率,实现从单一层次的易损性到整体易损性的推理。
英文摘要:
      This paper applies performance-based multidimensional fragility analysis method with the combination of explicit connectivity Bayesian network to the fragility evaluation of ATC towers. The finite element model of the ATC tower is established, and the uncertainty of the earthquakes is considered. The structural response data is obtained by the nonlinear time history analysis method. The whole tower structure is divided into three levels, and each level is further divided into corresponding sub-levels according to the number of layers it includes. The damage index and the limit state of the components in the sub-level is determined and the probabilistic seismic demand model which obeys a multi-lognormal distribution is established. Considering the correlation between different limit states, the corresponding multi-dimensional limit state function is established, the corresponding failure domain is analyzed, and the probability of the failure of the component is calculated by Monte Carlo method. The explicit connectivity Bayesian network model of the ATC tower is established and the conditional probability tables of intermediate nodes are established by using Analytic Hierarchy Process. MATLAB is used to perform the inference calculation of the Bayesian network and obtain the failure probability of the whole structure, which achieving the inference from the component fragility to the whole structural fragility.
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