Combining microstructural mechanical models with experimental data enhances our understanding of the mechanics of soft tissue, such as tendons. In previous work, a Bayesian framework was used to infer constitutive parameters from uniaxial stress-strain experiments on horse tendons, specifically the superficial digital flexor tendon (SDFT) and common digital extensor tendon (CDET), on a per-experiment basis. Here, we extend this analysis to investigate the natural variation of these parameters across a population of horses. Using a Bayesian mixed effects model, we infer population distributions of these parameters. Given that the chosen hyperelastic model does not account for tendon damage, careful data selection is necessary. Avoiding ad hoc methods, we introduce a hierarchical Bayesian data selection method. This two-stage approach selects data per experiment, and integrates data weightings into the Bayesian mixed effects model. Our results indicate that the CDET is stiffer than the SDFT, likely due to a higher collagen volume fraction. The modes of the parameter distributions yield estimates of the product of the collagen volume fraction and Young's modulus as 811.5 MPa for the SDFT and 1430.2 MPa for the CDET. This suggests that positional tendons have stiffer collagen fibrils and/or higher collagen volume density than energy-storing tendons.

翻译：将微观结构力学模型与实验数据相结合，可增强我们对肌腱等软组织力学行为的理解。先前研究采用贝叶斯框架，基于单轴应力-应变实验数据对马匹肌腱（特别是趾浅屈肌腱（SDFT）与趾总伸肌腱（CDET））进行了逐样本本构参数反演。本研究扩展该分析方法，旨在探究这些参数在马群中的自然变异规律。通过构建贝叶斯混合效应模型，我们推断了参数在群体层面的分布特征。鉴于所选超弹性模型未考虑肌腱损伤机制，需进行审慎的数据筛选。为避免临时性方法，我们提出了一种分层贝叶斯数据选择方法。该两阶段策略在逐实验数据筛选的基础上，将数据权重整合至贝叶斯混合效应模型中。研究结果表明：CDET较SDFT具有更高刚度，这可能源于其更高的胶原体积分数。参数分布的众数显示，胶原体积分数与杨氏模量的乘积估计值在SDFT中为811.5 MPa，在CDET中为1430.2 MPa。这表明位置性肌腱相较于储能性肌腱具有更刚硬的胶原纤维和/或更高的胶原体积密度。