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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.
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]
Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 194116753
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PubTypeId: academicJournal
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  Label: Title
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  Data: Quantitative Damage Detection and Evolution in Composite Structures Using Digital Image Correlation, Machine Learning, and Peridynamics.
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  Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. May2026, Vol. 19 Issue 10, p1917. 28p.
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  Data: <searchLink fieldCode="DE" term="%22Composite+structures%22">Composite structures</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+image+correlation%22">Digital image correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Finite+element+method%22">Finite element method</searchLink><br /><searchLink fieldCode="DE" term="%22Fatigue+%28Physiology%29%22">Fatigue (Physiology)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Structural+health+monitoring%22">Structural health monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Damage+models%22">Damage models</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3390/ma19101917
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 28
        StartPage: 1917
    Subjects:
      – SubjectFull: Composite structures
        Type: general
      – SubjectFull: Digital image correlation
        Type: general
      – SubjectFull: Finite element method
        Type: general
      – SubjectFull: Fatigue (Physiology)
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Structural health monitoring
        Type: general
      – SubjectFull: Damage models
        Type: general
    Titles:
      – TitleFull: Quantitative Damage Detection and Evolution in Composite Structures Using Digital Image Correlation, Machine Learning, and Peridynamics.
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          Name:
            NameFull: Vaitkūnas, Tomas
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            NameFull: Jasiūnienė, Elena
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            NameFull: Griškevičius, Justas
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            NameFull: Samaitis, Vykintas
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            NameFull: Griškevičius, Paulius
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            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 19961944
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              Value: 19
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              Value: 10
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            – TitleFull: Materials (1996-1944)
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