Use of a three-dimensional scanner for shrinkage curve tests
Bibliographic record
Abstract
A procedure is proposed for conducting shrinkage limit tests using a three-dimensional (3D) scanner. Shrinkage limit tests were conducted on 27 different soils of varying plasticity. In this study only eight of the shrinkage curves were determined using 3D scanning techniques, while the remaining 19 were taken from literature. An individual specimen was scanned between 30 and 50 times to produce a high-resolution shrinkage curve. Shrinkage curves for each material were obtained by curve fitting a shrinkage model to the measured dataset. The primary intent of the research was to relate the shrinkage curve equation to the plasticity of a given soil. Using linear regression analysis, an empirical correlation was developed to reasonably relate parameter csh from the shrinkage model to the ratio of the plastic and liquid limits. The shrinkage curves produced based on the model have an average difference of ∼1.2% in terms of measured void ratio and predicted void ratio. The method was demonstrated to be robust for materials of low, medium, and high plasticity. The proposed methodology also presents a means of estimating a shrinkage curve in its entirety based solely on the volume of an air-dried sample, the specific gravity, and Atterberg limits of the specimen. This effectively reduces the amount of work needed to derive the shrinkage curve and could potentially reduce the time for a shrinkage limit test by half or more.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".