Quantitative Damage Detection and Evolution in Composite Structures Using Digital Image Correlation, Machine Learning, and Peridynamics.
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| Title: | Quantitative Damage Detection and Evolution in Composite Structures Using Digital Image Correlation, Machine Learning, and Peridynamics. |
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| Authors: | Vaitkūnas, Tomas1 (AUTHOR), Jasiūnienė, Elena2,3 (AUTHOR), Griškevičius, Justas3,4 (AUTHOR), Samaitis, Vykintas3,4 (AUTHOR), Griškevičius, Paulius1 (AUTHOR) |
| Source: | Materials (1996-1944). May2026, Vol. 19 Issue 10, p1917. 28p. |
| Subjects: | Composite structures, Digital image correlation, Finite element method, Fatigue (Physiology), Machine learning, Structural health monitoring, Damage models |
| Abstract: | Structural health monitoring (SHM) of composite structures using surface strain fields measured by digital image correlation (DIC) has been widely demonstrated; however, accurate damage quantification remains challenging. This study proposes a hybrid framework integrating finite element (FE) modeling, machine learning (ML), and peridynamics (PD). A CFRP specimen with a notch was subjected to cyclic loading, and damage evolution was monitored using DIC and validated by ultrasound measurements. A validated FE model generated synthetic strain-field datasets for ML training, enabling defect detection and quantitative characterization directly from surface strains. The trained models achieved high accuracy, including perfect notch detection and low prediction errors. A calibrated PD model captured internal damage evolution and fatigue behavior. The combined DIC–ML–PD approach enables accurate, non-contact damage identification and prognosis, supporting physics-informed digital twins for composite structures. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Structural health monitoring (SHM) of composite structures using surface strain fields measured by digital image correlation (DIC) has been widely demonstrated; however, accurate damage quantification remains challenging. This study proposes a hybrid framework integrating finite element (FE) modeling, machine learning (ML), and peridynamics (PD). A CFRP specimen with a notch was subjected to cyclic loading, and damage evolution was monitored using DIC and validated by ultrasound measurements. A validated FE model generated synthetic strain-field datasets for ML training, enabling defect detection and quantitative characterization directly from surface strains. The trained models achieved high accuracy, including perfect notch detection and low prediction errors. A calibrated PD model captured internal damage evolution and fatigue behavior. The combined DIC–ML–PD approach enables accurate, non-contact damage identification and prognosis, supporting physics-informed digital twins for composite structures. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961944 |
| DOI: | 10.3390/ma19101917 |
