论文标题
关于软生物组织各向异性高弹性组成模型的基于计算机的综述
An in silico-based review on anisotropic hyperelastic constitutive models for soft biological tissues
论文作者
论文摘要
我们根据来自三种不同人体组织的实验数据的拟合性能，回顾了九种不变和色散型各向异性超弹性组成模型。为此，我们使用了混合多目标优化过程。设计了一种遗传算法来生成初始猜测，然后是基于梯度的搜索算法。然后将本构模型拟合到在具有不同纤维取向的组织上进行的一组单轴和双轴张力实验。对于计算机研究，利用了对动脉瘤性主动脉，Linea alba和直肌鞘组织进行的实验。因此，对于拟合度量的OBJEC归一化质量，这些模型对模型进行了排名。最后，对每个模型的拟合性能进行了详细的讨论。这项工作提供了各种各向异性超弹性模型的有价值的定量比较，其发现可以帮助建模软组织的行为，以选择特定应用的最佳组成型模型。
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.
