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Related Experiment Video

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Data-Driven Computational Simulation in Bone Mechanics.

J A Sanz-Herrera1, J Mora-Macías2, J Ayensa-Jiménez3

  • 1School of Engineering, University of Seville, Camino de los descubrimientos s/n, 41092, Seville, Spain. jsanz@us.es.

Annals of Biomedical Engineering
|July 19, 2020
PubMed
Summary
This summary is machine-generated.

Data-driven methods offer a model-free approach for simulating tissue biomechanics, overcoming challenges in constitutive modeling for biological tissues. This technique captures natural spatial variations in bone tissue, enhancing biomechanical simulations.

Keywords:
Computational biomechanicsData-driven approachExperimental bone tissue mechanicsNumerical simulation

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Area of Science:

  • Computational mechanics
  • Tissue biomechanics
  • Biomedical engineering

Background:

  • Data-driven methods are emerging as disruptive technologies across physics and engineering.
  • Accurate constitutive models for biological tissues are challenging due to microstructural variability.
  • Tissue biomechanics presents a suitable context for data-driven approaches.

Purpose of the Study:

  • To apply data-driven methodology to characterize and mechanically simulate cortical bone tissue.
  • To demonstrate the efficacy of data-driven approaches in overcoming limitations of traditional constitutive models.
  • To investigate the recovery of natural spatial variations in bone tissue properties.

Main Methods:

  • Mechanical testing of cortical horse bone tissue using a biaxial machine.
  • Digital image correlation to obtain displacement fields.
  • Data-driven methodology flowchart utilizing displacement, strain, and approximated stress data.

Main Results:

  • The data-driven methodology successfully simulated biomechanical problems without imposing constitutive laws.
  • The approach recovered the natural spatial variation inherent in bone tissue's complex structure.
  • Comparison with classical model-based solutions validated the data-driven method's effectiveness.

Conclusions:

  • Data-driven methodology is a valuable tool for direct simulation in biomechanics.
  • This approach effectively handles the heterogeneity and stochasticity of biological tissues.
  • Model-free simulation using data-driven techniques enhances the accuracy of biomechanical models.