Sensitivity analysis of non‐local damage in soft biological tissues

Abstract

Computational modeling can provide insight into understanding the damage mechanisms of soft biological tissues. Our gradient‐enhanced damage model presented in a previous publication has shown advantages in considering the internal length scales and in satisfying mesh independence for simulating damage, growth and remodeling processes. Performing sensitivity analyses for this model is an essential step towards applications involving uncertain patient‐specific data. In this paper, a numerical analysis approach is developed. For that we integrate two existing methods, that is, the gradient‐enhanced damage model and the surrogate model‐based probability analysis. To increase the computational efficiency of the Monte Carlo method in uncertainty propagation for the nonlinear hyperelastic damage analysis, the surrogate model based on Legendre polynomial series is employed to replace the direct FEM solutions, and the sparse grid collocation method (SGCM) is adopted for setting the collocation points to further reduce the computational cost in training the surrogate model. The effectiveness of the proposed approach is illustrated by two numerical examples, including an application of artery dilatation mimicking to the clinical problem of balloon angioplasty.

Identification of Constitutive Parameters for the Non-Local Damage Model of Soft Biological Tissues

“…Secondly, the tension experiments are carried out on soft tissue of bovine heart artery containing damage process to collect the measurement. Thirdly, an inverse problem is solved by solving an optimization problem, and a surrogate model is built to replace nonlinear FEM computation to save the computational cost of inverse problems [2]. The computational results show the proposed model has good accuracy in the identification of nonlocal damage parameters.…”