Thirty-Sixth Canadian Geotechnical Colloquium: Advances in visualization of geotechnical processes through digital image correlation
Bibliographic record
Abstract
Digital image correlation (DIC) is an image-processing technique that calculates fields of incremental displacement by comparing two digital images and locating numerous small regions in both images to high subpixel accuracy. This technique is particularly well suited to the visualization of geotechnical failure processes such as the plastic collapse of a shallow foundation or the evolution of failure within a physical model landslide as it can yield full-field displacements at high spatial and temporal resolution. The adoption of digital image correlation methods by the geotechnical engineering community over the past 15 years has therefore provided researchers with a transformative tool for the visualization of failure mechanisms and the quantification of soil and soil–structure interaction behaviour within physical model experiments. The objective of this Canadian Geotechnical Colloquium is to provide an updated review of the factors that affect accuracy and precision of the technique and to highlight selected recent advances and emerging uses of DIC in geotechnical engineering applications with particular emphasis on geotechnical physical modelling and field monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".