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Stochastic constitutive modeling of elastic-plastic materials with uncertain properties
Computers and Geotechnics ( IF 5.3 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.compgeo.2020.103642
Maxime Lacour , Norman A. Abrahamson

Abstract A new deterministic constitutive model is developed that has a form that is efficient for use in a stochastic constitutive model in which the material properties and the stress and stiffness fields are considered uncertain and modeled as random variables. Simplifications are made to the deterministic model to allow for efficient propagation of uncertainty of model inputs using the Polynomial Chaos (PC) method to quantify the effect of uncertainty in the model parameters on the system response. With large uncertainties in the model parameters that are typical in soil models, the use of a simplified model is justified because the effect of the simplification will be masked by the uncertainties in the model parameters. Each component of the stress and stiffness tensor is expanded along the PC basis, and a small number of PC coefficients are updated along loading/unloading increments. The simplified deterministic model gives similar results to other traditional models, but the equations it involves are computationally much more efficient when extended to the stochastic model and solved with PC. The results are PC coefficients of each stress and stiffness component along the loading/unloading parts of the curve, from which probability distributions of the stress and stiffness can be reconstructed. Compared with traditional Monte-Carlo simulation, the PC method gives similar results while being several orders of magnitude faster computationally. The PC expansion method can also be extended to the non-linear stochastic finite element method.

中文翻译:

具有不确定特性的弹塑性材料的随机本构建模

摘要 开发了一种新的确定性本构模型,其形式可有效用于随机本构模型,在该模型中,材料特性和应力和刚度场被认为是不确定的,并被建模为随机变量。对确定性模型进行了简化,以允许使用多项式混沌 (PC) 方法有效传播模型输入的不确定性,以量化模型参数中的不确定性对系统响应的影响。由于土壤模型中典型的模型参数存在很大的不确定性,因此使用简化模型是合理的,因为模型参数的不确定性会掩盖简化的效果。应力和刚度张量的每个分量都沿 PC 基展开,并且少量 PC 系数随着加载/卸载增量而更新。简化的确定性模型给出了与其他传统模型相似的结果,但是当扩展到随机模型并用 PC 求解时,它所涉及的方程在计算上效率更高。结果是沿曲线加载/卸载部分的每个应力和刚度分量的 PC 系数,从中可以重建应力和刚度的概率分布。与传统的蒙特卡罗模拟相比,PC 方法给出了相似的结果,同时在计算上要快几个数量级。PC展开法也可以推广到非线性随机有限元法。简化的确定性模型给出了与其他传统模型相似的结果,但是当扩展到随机模型并用 PC 求解时,它所涉及的方程在计算上效率更高。结果是沿曲线加载/卸载部分的每个应力和刚度分量的 PC 系数,从中可以重建应力和刚度的概率分布。与传统的蒙特卡罗模拟相比,PC 方法给出了相似的结果,同时在计算上要快几个数量级。PC展开法也可以推广到非线性随机有限元法。简化的确定性模型给出了与其他传统模型相似的结果,但是当扩展到随机模型并用 PC 求解时,它所涉及的方程在计算上效率更高。结果是沿曲线加载/卸载部分的每个应力和刚度分量的 PC 系数,从中可以重建应力和刚度的概率分布。与传统的蒙特卡罗模拟相比,PC 方法给出了相似的结果,同时在计算上要快几个数量级。PC展开法也可以推广到非线性随机有限元法。结果是沿曲线加载/卸载部分的每个应力和刚度分量的 PC 系数,从中可以重建应力和刚度的概率分布。与传统的蒙特卡罗模拟相比,PC 方法给出了相似的结果,同时在计算上要快几个数量级。PC展开法也可以推广到非线性随机有限元法。结果是沿曲线加载/卸载部分的每个应力和刚度分量的 PC 系数,从中可以重建应力和刚度的概率分布。与传统的蒙特卡罗模拟相比,PC 方法给出了相似的结果,同时在计算上要快几个数量级。PC展开法也可以推广到非线性随机有限元法。
更新日期:2020-09-01
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