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劲性水泥土搅拌桩地下连续墙贝叶斯网络分析
引用本文:王红卫,周健,贾敏才.劲性水泥土搅拌桩地下连续墙贝叶斯网络分析[J].同济大学学报(自然科学版),2012,40(1):0027-0032.
作者姓名:王红卫  周健  贾敏才
作者单位:同济大学地下建筑与工程系,上海200092;同济大学岩土及地下工程教育部重点实验室,上海200092
基金项目:武汉轨道交通钟家村站及相邻区间关键技术研究(WHRT-KY-201013)
摘    要:分别分析了事件树、故障树和贝叶斯网络的优点和缺点,总结了三者联合应用的必要性,进而改善了贝叶斯网络的构造方法.方法是先将事件树的输出事件和序列事件分别作为故障树的顶事件和中间事件建造系统的故障树,再将故障树转化为系统的贝叶斯网络,然后利用贝叶斯网络的计算优势进行分析;建议贝叶斯网络中的任一非根节点事件出现失效时应按后验概率从大到小的次序对相应根节点事件进行控制.采用文中方法分析了SMW(soil-cement mixingwall)深基坑工程的基坑事故发生概率和造成事故的主要基本事件,证明了该方法的实用性.

关 键 词:贝叶斯网络  事件树  故障树  失效概率  基坑工程
收稿时间:2010/11/10 0:00:00
修稿时间:2011/11/23 0:00:00

Bayesian Networks Analysis on Soil cement Mixing Wall
WANG Hongwei,ZHOU Jian and JIA Mincai.Bayesian Networks Analysis on Soil cement Mixing Wall[J].Journal of Tongji University(Natural Science),2012,40(1):0027-0032.
Authors:WANG Hongwei  ZHOU Jian and JIA Mincai
Institution:Department of Geotechnical Engineering Tongji University,Department of Geotechnical Engineering Tongji University,Department of Geotechnical Engineering Tongji University
Abstract:This paper analyses the advantages and the disadvantages of Event Trees (ET), Fault Trees (FT) and Bayesian networks (BN) respectively. It is generalized that the combination of the three methods is necessary, and the way to construct a BN is improved. This way consists of three steps. Firstly, the FT whose top event and middle events are the output event and the event sequence of the ET respectively is built. Secondly, the constructed FT is transformed into a BN directly. Thirdly, with the advantages of calculation and analysis, the BN is used to make predictions as well as diagnostics. This article proposes that when any non-root node event appears failure in BN, the corresponding root node events should be controlled by the descending order of their posterior probability values. By application of the method proposed in the paper to the SMW retaining structures in a deep excavation, the failure probability of the top event and the main cause are figured out easily and the practicability of this method is proved.
Keywords:Bayesian networks  event trees  fault trees  failure probability  excavation engineering
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