ReviewMaterials (Basel, Switzerland)2025
From Pixels to Predictions: Integrating Machine Learning and Digital Image Correlation for Damage Identification in Engineering Materials.
Review in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Quantitative Damage Detection and Evolution in Composite Structures Using Digital Image Correlation, Machine Learning, and Peridynamics.Materials (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.