A physics-guided machine learning model for two-dimensional structures based on ordinary state-based peridynamics

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Abstract

This study presents a novel physics-guided machine learning model for two-dimensional structures based on ordinary state-based peridynamics. The linear relationships between the displacements of a material point and the displacements of its neighbours and the applied forces are obtained for the machine learning model by using linear regression. The numerical procedure for coupling the ordinary state-based peridynamic model and the machine learning model is also provided. The accuracy of the coupled model is verified by predicting deformations of a two-dimensional plate with circular cut-out subjected to tension and a twodimensional representation of three points bending test. To further demonstrate the capabilities of the coupled model, damage predictions for a two-dimensional representation of a three-point bending test, a notched plate with a hole subjected to tension, a square plate with a pre-existing crack subjected to tension, and a plate with a pre-existing crack subjected to sudden loading are presented.
Original languageEnglish
JournalTheoretical and Applied Fracture Mechanics
Publication statusAccepted/In press - 7 Dec 2020

Keywords

  • machine learning
  • peridynamics
  • fracture
  • nonlocal
  • linear regression

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