Reduced model of macro-scale stochastic plasticity identification by Bayesian inference: Application to quasi-brittle failure of concrete
Reduced model of macro-scale stochastic plasticity identification by Bayesian inference: Application to quasi-brittle failure of concrete
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贝叶斯推理宏观随机塑性识别的简化模型：在混凝土准脆性破坏中的应用
DOI：
10.1016/j.cma.2020.113428
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发表时间：
2020
影响因子：
7.2
通讯作者：
E. Karavelić
中科院分区：
文献类型：
--
作者：
A. Ibrahimbegovic;H. G Matthies;E. Karavelić
在本文中，我们处理了混凝土材料的基于概率的尺度桥接，当在中观尺度（骨料与水泥微观结构可见的尺度）上传递详细信息时，使用基于voronoi细胞的微观结构表示，在宏观尺度（混凝土是均质连续体的尺度）上选择简化模型，并使用局部破坏的随机塑性模型。这是通过使用贝叶斯推理来实现的，贝叶斯推理提供了以随机变量（RV）表示的宏观尺度模型参数的概率分布，以补偿中尺度模型的减少，其中参数表示为随机场（RF）。该方法的独创性之处在于由此产生的宏观尺度随机塑性模型，该模型可以根据其参数的相应概率分布来最好地量化数据丢失引起的不确定性。本文第一部分（见karavelici et al.(2019)）中提出的混凝土中尺度模型详细说明了所提出的程序，包括简单的弹性响应，以及在断裂过程区（FPZ）硬化的塑性响应，随后是局部破坏阶段的软化响应。这种局部失效的情况意味着经典的均质化过程不再适用，而应该被根据兴趣量（qi）定义的宏观尺度简化模型所取代，这对于每个特定的响应阶段不一定相同。将参数识别的完整结果集在宏观尺度上结合在具有嵌入不连续的实体有限元（ED-FEM）中，使其具有非常强大的预测性能。对于宏观尺度简化模型的特殊选择ED-FEM允许人们在单元水平上计算qi，或者作为弹性响应的应变能，或者作为每种破坏模式的塑性耗散。因此，通过匹配为宏观尺度和中尺度模型计算的qi结果来执行的代表宏观尺度模型参数的rv概率分布的贝叶斯推理计算，在概率设置中减少为均匀化类型的过程，可以成功处理任何不再适用尺度分离的情况。
In this paper we deal with a probability-based scale bridging for concrete material when passing the detailed information at the meso-scale (the scale where the aggregate vs. cement microstructure is visible) with a Voronoi-cell based microstructure representation towards the chosen reduced model at the macro-scale (the scale where the concrete is a homogenized continuum) with a stochastic plasticity model for localized failure. This is accomplished by using Bayesian inference providing the probability distributions of the macro-scale model parameters expressed as random variables (RV) in order to compensate for the model reduction from the meso-scale, where parameters are expressed as random fields (RF). The original aspect of this approach is in the resulting macro-scale stochastic plasticity model, which can best quantify the uncertainty due to data loss in terms of the corresponding probability distribution of its parameters. The proposed procedure is illustrated in detail for the concrete meso-scale model presented in Part I of this paper (see Karavelić et al. (2019)), both for the simple elastic response, as well as for the plastic response with hardening in the fracture process zone (FPZ), followed by a softening response in the localized failure phase. This context of localized failure implies that the classical homogenization procedure no longer applies, and should be replaced by a macro-scale reduced model defined with respect to a quantity of interest (QoI), which is not necessarily the same for each particular response phase. The complete set of results for the parameter identification is combined together at the macro-scale in terms of a solid finite element with embedded discontinuity (ED-FEM), granting it very powerful predictive properties. The particular choice of ED-FEM for the macro-scale reduced model allows one to compute the QoI at an element-level, either as strain energy for the elastic response, or as plastic dissipation for each failure mode. Thus, the Bayesian inference computation of the probability distributions of the RVs representing the macro-scale model parameters, performed by matching such QoI results computed for the macro-scale and the meso-scale models, reduces to a homogenization-type procedure within a probabilistic setting that can successfully handle any case where the separation of scales no longer applies.
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DOI：
10.1016/j.cageo.2012.05.017
发表时间：
2013-06
期刊：
Comput. Geosci.
影响因子：
--
作者：
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DOI：
10.1186/s40323-020-00185-y
发表时间：
2019-12
影响因子：
--
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DOI：
10.1080/12795119.2003.9692509
发表时间：
2003
期刊：
Revue Française de Génie Civil
影响因子：
--
作者：
S. Ghavamian;I. Carol;A. Delaplace
通讯作者：
A. Delaplace
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影响因子：
4.7
作者：
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DOI：
10.1016/j.advengsoft.2014.01.013
发表时间：
2013
期刊：
ArXiv
影响因子：
--
作者：
A. Kucerová;M. Leps
通讯作者：
M. Leps