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Experimental and Data Analysis Workflow for Soft Matter Nanoindentation
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Microstructure-Informed Hyper-Viscoelastic Model Capturing Soft Tissue Tensile Behavior Across Large Deformations.

Lei Shi1, Kristin Myers2

  • 1Department of Mechanical Engineering, Kennesaw State University, Marietta, GA, 30060, USA.

Journal of the Mechanics and Physics of Solids
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new hyper-viscoelastic model for soft tissues, linking mechanical behavior to collagen structure. The model accurately predicts tissue responses and offers better mechanistic insight than traditional methods.

Keywords:
Collagen fiber networkContinuous fiber modelFiber recruitmentFinite viscoelasticityMicrostructure-informed modeling

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Area of Science:

  • Biomechanics
  • Materials Science
  • Computational Biology

Background:

  • Soft biological tissues display complex nonlinear and time-dependent mechanical properties.
  • These behaviors stem from the intricate collagen network microstructure within the tissues.

Purpose of the Study:

  • To develop a unified, microstructure-informed hyper-viscoelastic constitutive model for soft tissues.
  • The model aims to capture tensile responses across various deformations and strain rates.

Main Methods:

  • A generalized Maxwell framework was used for continuous fiber recruitment.
  • A physically motivated flow rule, inspired by reptation and Brownian dynamics, was incorporated.
  • The model was calibrated and validated using stress-relaxation experiments on human cervix, rat subcutaneous tissue, and bovine tendon.

Main Results:

  • The model accurately captured the viscoelastic responses of different soft tissues.
  • Physiologically meaningful trends in fiber recruitment and viscoelastic properties were identified.
  • The model demonstrated robustness by predicting faster relaxation from slower-relaxation data.

Conclusions:

  • The proposed model offers improved accuracy and mechanistic interpretability compared to classical models.
  • It explicitly links macroscopic tissue behavior to collagen network structure and crosslinking.
  • This work provides a foundation for microstructure-informed soft tissue modeling and digital twin development.