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Microstructure-informed constitutive modeling of granular media under multidirectional loading: From particle-scale to continuum.

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Thermodynamically consistent modeling of granular soils using physics-informed neural networks.

Nazanin Irani1, Mohammad Salimi2, Torsten Wichtmann2

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Summary

This study introduces a novel constitutive model for granular soils using geotechnically- and physics-informed neural networks (GINN). The GINN model ensures thermodynamic consistency and accurately predicts soil behavior, outperforming existing models.

Keywords:
Constitutive modellingEnergy conservationGINNPhysics-informed neural networksThermodynamics laws

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Area of Science:

  • Computational Geomechanics
  • Materials Science
  • Artificial Intelligence in Engineering

Background:

  • Data-driven models excel at pattern recognition but often lack physical grounding and generalizability.
  • Physics-informed neural networks (PINNs) integrate governing equations into machine learning for enhanced physical consistency.
  • Existing constitutive models for granular soils may not fully capture complex behaviors or adhere to thermodynamic principles.

Purpose of the Study:

  • To develop a novel, thermodynamically consistent constitutive model for granular soils.
  • To leverage geotechnically- and physics-informed neural networks (GINN) for integrating physical laws with data-driven learning.
  • To ensure model predictions adhere to fundamental thermodynamic principles, including non-negative dissipation.

Main Methods:

  • Developed a GINN model incorporating a composite loss function that includes thermodynamic admissibility constraints.
  • Ensured non-negative material dissipation rate by calculating it from total work input and a free energy potential.
  • Validated the model against monotonic drained triaxial test data across various initial void ratios and stress states.

Main Results:

  • The GINN model accurately simulates the shear strength and dilative response of granular soil samples.
  • Predictions demonstrated consistency with thermodynamic laws, including strictly non-negative material dissipation.
  • The model's predictive accuracy was comparable to, and in some aspects superior to, widely adopted literature models.

Conclusions:

  • The proposed GINN framework provides a robust and thermodynamically consistent approach for modeling granular soils.
  • Integrating physical principles directly into neural networks enhances the reliability and generalizability of constitutive models.
  • This approach offers a promising direction for developing advanced, physics-based machine learning models in geomechanics.