We review nine invariant and dispersion-type anisotropic hyperelastic constitutive models for soft biological tissues based on their fitting performance to experimental data from three different human tissues. For this, we used a hybrid multi-objective optimization procedure. A genetic algorithm is devised to generate the initial guesses followed by a gradient-based search algorithm. The constitutive models are then fit to a set of uniaxial and biaxial tension experiments conducted on tissues with differing fiber orientations. For the in silico investigation, experiments conducted on aneurysmatic abdominal aorta, linea alba, and rectus sheath tissues are utilized. Accordingly, the models are ranked with respect to an objec tive normalized quality of fit metric. Finally, a detailed discussion is carried out on the fitting performance of each model. This work provides a valuable quantitative comparison of various anisotropic hyperelastic models, the findings of which can aid those modeling the behavior of soft tissues in selecting the best constitutive model for their particular application.
翻译:我们根据软生物组织与三个不同人体组织的实验数据相匹配的性能,对软生物组织9个异变和分散型动脉多动脉组成模型进行了审查。为此,我们采用了混合的多目标优化程序。设计了一个遗传算法,以产生最初的猜想,然后是梯度搜索算法。然后,组成模型适合对纤维取向不同的组织进行的一系列非亚轴和双轴张力实验。在硅质调查中,利用了对动脉细胞腹膜、直肠腹膜和直肠切片组织进行的实验。因此,这些模型的排位与适应度的正态正常质量相匹配。最后,对每种模型的恰当性能进行了详细讨论。这项工作为各种反动脉动超弹性模型提供了宝贵的数量比较,其结果可以帮助那些模拟软组织行为的人选择最佳的组成模型,用于其特定应用。