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A proposal to use the inverse problem for determining parameters in a constitutive model for concrete
Soft Computing ( IF 4.1 ) Pub Date : 2021-05-03 , DOI: 10.1007/s00500-021-05745-x
W. M. Pereira Junior , R. A. Borges , D. L. Araújo , J. J. C. Pituba

Concrete is a material of utmost importance in construction, and therefore various numerical models have been developed to satisfactorily represent its observed behavior. Within this perspective, damage models stand out, especially isotropic damage ones that can satisfactorily represent the nonlinearity characteristics resulting from stiffness deterioration having the added advantages of a few parameters to be identified. However, these models are dependent on experimental observations to determine the constitutive variables which greatly influence the accuracy of the obtained response. Therefore, using more robust identification techniques can improve the process of determining these internal variables. In this sense, this paper intends to contribute to the literature by using swarm intelligence bioinspired optimization techniques combined with a concrete damage model to determine its constitutive variables. Observable sources are obtained from complete stress–strain curve proposals obtained from the literature, and the optimization firefly and bee colony algorithms are used. The efficiency of these optimization algorithms is verified, as well as that of the damage model in uniaxial tension and compression situations. Willmott's correlation indexes reveal values close to 0.97, which indicates an excellent correlation between the generated numerical data and reference responses.



中文翻译:

关于使用反问题来确定混凝土本构模型中参数的建议

混凝土是建筑中最重要的材料,因此已经开发出各种数值模型来令人满意地表示其观察到的行为。在这种情况下,损伤模型尤其突出,尤其是各向同性的损伤模型,可以令人满意地表示由刚度下降引起的非线性特征,并具有一些尚待确定的参数的附加优点。但是,这些模型依赖于实验观察来确定本构变量,而本构变量会极大地影响所获得响应的准确性。因此,使用更可靠的识别技术可以改善确定这些内部变量的过程。在这个意义上,本文旨在通过使用群体智能生物启发优化技术结合具体的损伤模型来确定其本构变量,从而为文献做出贡献。从文献中获得的完整应力-应变曲线建议中获得了可观察到的资料,并使用了优化的萤火虫和蜂群算法。验证了这些优化算法的效率,以及单轴拉伸和压缩情况下损伤模型的效率。威尔莫特的相关指数显示出接近0.97的值,这表明所生成的数值数据与参考响应之间具有极好的相关性。并使用萤火虫和蜂群优化算法。验证了这些优化算法的效率,以及单轴拉伸和压缩情况下损伤模型的效率。威尔莫特的相关指数显示出接近0.97的值,这表明所生成的数值数据与参考响应之间具有极好的相关性。并使用萤火虫和蜂群优化算法。验证了这些优化算法的效率,以及单轴拉伸和压缩情况下损伤模型的效率。威尔莫特的相关指数显示出接近0.97的值,这表明所生成的数值数据与参考响应之间具有极好的相关性。

更新日期:2021-05-03
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