A variational constitutive model for soft biological tissues.

Area of Science:

• Computational mechanics
• Biomaterials science
• Soft tissue modeling

Background:

• Accurate constitutive models are crucial for understanding soft biological tissue mechanics.
• Existing models may not fully capture complex behaviors like plasticity and rate-dependency.

Purpose of the Study:

• To formulate a fully variational constitutive model for soft biological tissues in the finite strain regime.
• To incorporate multiple physical phenomena including hyperelasticity, viscosity, and plasticity.
• To validate the model against experimental data from brain tissue.

Main Methods:

• Finite strain formulation
• Ogden-type hyperelasticity
• Finite viscosity and plasticity (deviatoric and volumetric)
• Rate and microinertia effects
• Time discretization and pre-minimization for variational updates
• Genetic algorithms for parameter identification

Main Results:

• The model successfully integrates hyperelasticity, viscosity, and plasticity.
• Variational updates were efficiently obtained using time discretization and pre-minimization.
• Genetic algorithms proved effective for complex parameter identification.
• Model predictions showed good agreement with experimental tests on swine and human brain tissue.
• The model demonstrated the ability to predict hysteresis, cyclic softening, rate effects, and plastic deformation.

Conclusions:

• The developed variational constitutive model provides a robust framework for simulating soft biological tissues.
• The model's ability to capture diverse mechanical behaviors highlights its potential for biomechanical applications.
• The use of genetic algorithms facilitates the identification of complex material parameters.