Speckle Pattern Improvements for Digital Image Correlation to Capture Small Strains in Tensile Tests on Rock
Bibliographic record
Abstract
Digital image correlation (DIC) is a popular optical strain measurement technology for deformation analysis in rock mechanics laboratory testing. Although reliable and repeatable, traditional strain gauge and extensometer measurement techniques have drawbacks including high recurring cost and limited spatial distribution of data. DIC utilizes continuous photography and computer software to track paint speckle pixel displacements of a material deforming under load, producing a full-field strain map for each recorded increment. Although DIC presents an opportunity to overcome the limitations of traditional methods, significant precision and care is required to track typical total displacements of 0.005 mm (0.01% strain) in Brazilian tensile stress (BTS) tests of igneous and sedimentary specimens measuring 47.6 mm diameter. For computer algorithms to precisely track individual pixels between frames, it is often necessary to apply a unique speckle pattern conforming to several criteria to the rock specimen. A variety of speckle application techniques have been reported in rock mechanics literature; however, the importance of a high-quality speckle pattern and resulting influence on the accuracy of DIC results is frequently overlooked. The laboratory testing program in this research utilized 2-Dimensional DIC on BTS tests to investigate the cost, efficiency, ease of application, and effectiveness for several speckle application methods. In addition to testing traditional spraypaint, airbrush, and stamp application methods, an innovative laser engravement speckle application technique was developed in this research that provides more reliable DIC results. This research provides practical speckle pattern application recommendations for small strain 2D DIC measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".