The Role of Pre-Conditioning Frequency in the Experimental Characterization of Hyper-Elastic Materials as Models for Soft Tissue Applications

Abstract

Rubber-like materials as many soft tissues can be described as incompressible and hyper-elastic materials. Their comparable elastic behavior, up to a certain extent, has been exploited to develop and test experimental methodologies to be then applied to soft biological tissues such as aortic wall. Hence, theoretical and experimental simulation of aortic tissue, and more generally blood vessel tissue, has been often conducted using rubbers. Despite all the efforts in characterizing such materials, a clear and comprehensive testing procedure is still missing. In particular, the influence of pre-conditioning in the mechanical response of hyper-elastic materials has been often neglected. In this paper, the importance of pre-conditioning is demonstrated by: (i) exploring the effect of stretching frequency applied before the uniaxial tensile test; (ii) recognizing the role of specimen geometry and strain amplitude; (iii) verifying the impact of experimental data acquisition on finite element predictions. It was found that stress–strain relationship shows a statistical difference between some frequencies of pre-conditioning and its absence. Only certain pre-conditioning frequencies were able to generate repeatable experimental data for strip or dumb-bell shapes. This feature corresponds to a consistent reduction in the scatter of critical pressures obtained by numerical simulations.

In vivo soft tissue compressive properties of the human hand

“…Most soft tissues are known to display viscoelastic, anisotropic and non-linear responses [30][31][32][33][34][35]. Soft tissue properties are sensitive to several experimental variables including: strain rate [32,36,37], deformation [38], preconditioning [39][40][41], post mortem degradation [42][43][44], age [25,45,46], and sex [47,48].…”

The effect of thickness measurement on numerical arterial models

“…Furthermore, a convenient strain-energy function (SEF) needs to be used to facilitate predictions of stress distribution across the vessel wall thickness under physiological or pathological loads. Unique mechanical behaviours emerge from each SEF applied to the same original data set (de Gelidi et al, 2016).…”