| Title |
Quantitative damage detection and evolution in composite structures using digital image correlation, machine learning, and peridynamics |
| Authors |
Vaitkūnas, Tomas ; Jasiūnienė, Elena ; Griškevičius, Justas ; Samaitis, Vykintas ; Griškevičius, Paulius |
| DOI |
10.3390/ma19101917 |
| Full Text |
|
| Is Part of |
Materials.. Basel : MDPI. 2026, vol. 19, iss. 10, art. no., p. 1-28.. ISSN 1996-1944 |
| Keywords [eng] |
composite materials ; damage detection ; damage evolution ; digital image correlation ; fatigue ; inverse identification ; machine learning ; peridynamics ; structural health monitoring |
| Abstract [eng] |
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. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
