Experiments and Constitutive Model for Deep and Superficial Fascia. Digital Image Correlation and Finite Element Validation

Fascia is a highly organized collagenous tissue that is intimately connected with muscles. The mechanical properties of fascia strongly affect muscular actions and development of pathologies. The objective of this paper is to determine the mechanical properties of the deep and superficial fascia by uniaxial and pure shear tests and to propose and address the feasibility of a material hyperelastic constitutive model using combined Digital Image Correlation strain measurements and Finite Element (FE) computations. Experiments on deep and superficial fascia samples from the hindlimbs of six adult sheep along or perpendicular to collagen fibres and FE simulations of each experiment were compared. An anisotropic strain energy function was proposed to reproduce the mechanical behaviour of deep and superficial fascia and the material parameters were fitted using an optimization method and included in the FE simulations. The mechanical response of the deep and superficial fascia shows typical behaviour of soft connective tissues. It is shown that the samples in longitudinal direction are stiffer than the samples in transversal direction for both kinds of tissues. When considering the deep and superficial samples, it is evident a greater stiffness of deep samples for both longitudinal and transversal directions with respect to superficial samples. Overall good predictions were obtained with the model proposed which present relatively low ε values, ε = 0.1189 and ε = 0.0722 for deep and superficial fascia respectively. The experimental and FE results were compared. The simulated curves slightly underestimated or overestimated the load for uniaxial and pure shear tests, respectively, but very accurately captured the stiffness and the overall response. These results demonstrate that the proposed model and the fitted material properties applied have a good capability of reproducing the experimental conditions.