Microstructure-informed hyper-viscoelastic model capturing soft tissue tensile behavior across large deformations

Abstract

Soft biological tissues exhibit highly nonlinear and time-dependent mechanical behavior arising from their complex collagen network microstructure. In this study, we present a unified, microstructure-informed hyper-viscoelastic constitutive model that captures the tensile response of soft tissues across small to large deformations under monotonic tension. The model couples a continuous fiber recruitment formulation — realized through a generalized Maxwell framework — with a physically motivated flow rule representing constrained segmental mobility. This time-dependent mechanism, inspired by reptation- and Brownian-like dynamics, captures viscoelastic relaxation governed by localized fibrillar rearrangement, interfibrillar sliding, and motion in loosely crosslinked regions. The formulation is thermodynamically consistent and includes explicit expressions for the tangent moduli to ensure computational stability in finite element simulations. The model was calibrated and validated using multi-step stress-relaxation experiments performed on human cervix specimens from both pregnant and nonpregnant individuals, revealing physiologically meaningful trends in fiber recruitment and viscoelastic properties. Notably, the model is capable of predicting faster relaxation responses using parameters calibrated from slower-relaxation data, demonstrating robustness across different strain rates. To demonstrate generalizability, the model was further applied to published datasets from rat subcutaneous tissue and bovine tendon, accurately capturing their viscoelastic responses. Compared to classical viscoelastic models, the proposed framework offers improved accuracy and mechanistic interpretability by explicitly linking macroscopic behavior to underlying collagen network structure and crosslinking density. This work provides a foundation for robust, microstructure-informed modeling of soft tissue mechanics and has broad applicability in tissue characterization and digital twin development.