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地基渐进破坏及极限承载力的Cosserat连续体有限元分析 被引量:9
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作者 唐洪祥 李锡夔 《岩土力学》 EI CAS CSCD 北大核心 2007年第11期2259-2264,共6页
利用Cosserat连续体理论和所发展的有限元数值方法,模拟了地基由应变软化引起的以应变局部化为特征的渐进破坏过程,并从等价塑性应变的发展变化,阐述了渐进破坏过程对所能发挥的极限承载能力的影响。结果表明,Cosserat连续体模型能有效... 利用Cosserat连续体理论和所发展的有限元数值方法,模拟了地基由应变软化引起的以应变局部化为特征的渐进破坏过程,并从等价塑性应变的发展变化,阐述了渐进破坏过程对所能发挥的极限承载能力的影响。结果表明,Cosserat连续体模型能有效地模拟由应变软化引起以应变局部化为特征的渐进破坏现象,对地基等土工结构物有必要进行渐进破坏分析。同时指出,在求解软化型土体地基的极限承载力时,如果仍按传统的极限平衡或极限分析理论进行分析,可能得出偏于危险的结果。 展开更多
关键词 Cosserat连续体 有限元法 应变软化 应变局部化 渐进破坏分析 极限承载力
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Methodology for estimating probability of dynamical system's failure for concrete gravity dam 被引量:2
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作者 王超 张社荣 +1 位作者 孙博 王高辉 《Journal of Central South University》 SCIE EI CAS 2014年第2期775-789,共15页
Methodology for the reliability analysis of hydraulic gravity dam is the key technology in current hydropower construction.Reliability analysis for the dynamical dam safety should be divided into two phases:failure mo... Methodology for the reliability analysis of hydraulic gravity dam is the key technology in current hydropower construction.Reliability analysis for the dynamical dam safety should be divided into two phases:failure mode identification and the calculation of the failure probability.Both of them are studied based on the mathematical statistics and structure reliability theory considering two kinds of uncertainty characters(earthquake variability and material randomness).Firstly,failure mode identification method is established based on the dynamical limit state system and verified through example of Koyna Dam so that the statistical law of progressive failure process in dam body are revealed; Secondly,for the calculation of the failure probability,mathematical model and formula are established according to the characteristics of gravity dam,which include three levels,that is element failure,path failure and system failure.A case study is presented to show the practical application of theoretical method and results of these methods. 展开更多
关键词 concrete gravity dam dynamical system failure mode identification calculation of system failure probability stochastic model
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