Low‐cost digital image correlation and strain measurement for geotechnical applications
Bibliographic record
Abstract
Abstract Particle image velocimetry (PIV), or digital image correlation (DIC), is a widely used technique to measure soil displacements and strains in small‐scale geotechnical models. Arrays of single‐board computers (SBCs) produced by Raspberry Pi, and their associated 8‐MP cameras, are being used at the University of Cambridge to capture the images required for DIC analysis. This alternative to more expensive camera set‐ups has numerous advantages. A single expensive and large camera can be replaced—at low cost—by multiple cameras, adding flexibility and affordability to any experimental set‐up. Traditionally, the alignment of multiple cameras to each other and the referencing to a known coordinate system required painted or machined markers to be located on the observation windows through which the experiments are viewed. This can obstruct localised soil grain displacement measurements in those areas of the model where such markers are placed. To complement the Raspberry Pi camera system, a markerless calibration method was used during image acquisition. This paper outlines the set‐up of four of these small computers and associated cameras, provides an overview of the use of the markerless referencing system and reviews two different experimental apparatus used to measure soil displacement and strain. When the cost of additional cabling, connectors and mounting hardware is considered for this system, the total cost to implement was approximately $125 USD per camera plus one‐time costs of $175 USD for system peripherals, which represents outstanding value and enables practically all geotechnical laboratories to develop similar capabilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".