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Related Experiment Video

Updated: May 28, 2026

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

Quantitative Damage Detection and Evolution in Composite Structures Using Digital Image Correlation, Machine

Tomas Vaitkūnas1, Elena Jasiūnienė2,3, Justas Griškevičius4

  • 1Department of Mechanical Engineering, Kaunas University of Technology, Studentu Str. 56, LT-51424 Kaunas, Lithuania.

Materials (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid framework for structural health monitoring (SHM) in composite materials. The approach accurately detects and quantifies damage using digital image correlation, machine learning, and peridynamics for improved structural prognosis.

Keywords:
composite materialsdamage detectiondamage evolutiondigital image correlationfatigueinverse identificationmachine learningperidynamicsstructural health monitoring

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Published on: September 14, 2017

Area of Science:

  • Materials Science and Engineering
  • Mechanical Engineering
  • Computational Mechanics

Background:

  • Surface strain fields from digital image correlation (DIC) are used for structural health monitoring (SHM) of composite structures.
  • Accurate damage quantification using these methods remains a significant challenge.

Purpose of the Study:

  • To develop and validate a hybrid framework integrating finite element (FE) modeling, machine learning (ML), and peridynamics (PD) for enhanced damage quantification in composite structures.
  • To enable accurate, non-contact damage identification and prognosis for physics-informed digital twins.

Main Methods:

  • A carbon fiber reinforced polymer (CFRP) specimen with a notch was subjected to cyclic loading.
  • Damage evolution was monitored using DIC and validated with ultrasound measurements.
  • A validated FE model generated synthetic strain-field datasets for ML training, and a calibrated PD model captured internal damage.

Main Results:

  • The hybrid framework achieved high accuracy in defect detection and quantitative characterization directly from surface strains.
  • Trained ML models demonstrated perfect notch detection and low prediction errors.
  • The peridynamics model successfully captured internal damage evolution and fatigue behavior.

Conclusions:

  • The combined DIC-ML-PD approach offers an accurate, non-contact method for damage identification and prognosis in composite structures.
  • This framework supports the development of physics-informed digital twins for advanced structural health monitoring.