From Pixels to Predictions: Integrating Machine Learning and Digital Image Correlation for Damage Identification in Engineering Materials

Mostafa Sadeghian, Arvydas Palevicius, Jokubas Sablinskas, Paulius Griskevicius

Damage assessment in engineering materials is essential for structural reliability and safety. While traditional imaging techniques and Digital Image Correlation (DIC) provide valuable insights into deformation and crack evolution, they often require significant manual effort and suffer from accuracy limitations under complex loading conditions. Recent advances in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), have enabled the development of automated, high-resolution, and near real-time damage assessment techniques. This paper reviews methods that integrate ML with DIC to assess damage in composites, metals, and other engineering materials. We compare conventional ML models with modern DL architectures, discuss key challenges, and propose future research directions. The findings demonstrate that coupling DIC with ML significantly improves the accuracy, speed, and reliability of damage identification in engineering materials.