Abstract:
"The Influence of the Specimen Shape and Loading Conditions on the Parameter Identification of a Viscoelastic Brain Model"
Accurate biomechanical characterization of human brain tissue under dynamic loading is critical for developing predictive computational injury models. This study evaluates how specimen geometry and test assumptions affect the identification of viscoelastic parameters by comparing classical analytical optimization—assuming perfect step shear loading and uniform strain—with a three‐dimensional finite element (FE) approach that captures the true strain ramp and heterogeneous deformation in cylindrical white‐matter samples. Seven human brain specimens (12 mm × 18 mm) underwent 50% engineering shear strain at 500 s⁻¹, and stress–relaxation data were used to identify Prony‐series parameters both analytically (with and without the loading ramp) and via FE‐based response‐surface optimization. Analytical models neglecting the ramp underpredicted peak shear forces (by up to ~20 %), while uniform‐strain assumptions failed to capture complex strain fields: only ~44 % of elements reached the target shear. FE‐based parameter sets yielded instantaneous and long‐term shear moduli within literature ranges and closely matched experimental force–time histories. Sensitivity studies showed that reducing specimen height improves analytical predictions, but changing to cubic shape does not. These biomechanics‐focused findings demonstrate that FE‐based optimization, which integrates real loading profiles and three‐dimensional deformation, is essential for reliable brain material parameter identification and model biofidelity.
