Visualization of Chemical Grout Permeation in Transparent Soil
Bibliographic record
Abstract
Abstract This paper presents an experiment that visualizes the permeation of chemical grout in transparent soil. The low initial viscosity (5 mPa · s) urea-formaldehyde resin (UFR) was chosen as grouting material in this experiment. The transparent soil used in this study is made of fused silica and a calcium bromide solution with the same refractive index. Triaxial and permeability tests are carried out to demonstrate that the geotechnical properties and hydraulic permeability of this transparent soil are typical of granular soils and suitable for modeling natural sand in permeation problem. A combined grouting and optical measurement system is developed for this study, which consists of an air pressure driven grout injection station to inject grout into a transparent soil model, laser to illuminate the cross-section of interest inside the model, and a charge-coupled device (CCD) camera to capture a series of images during the whole grout injection and permeation process. Image processing techniques including digital image correlation (DIC) are applied to sequence of images to detect the edges of the grout bulb and displacement distribution. The relationship of the grouting radius and grouting time corresponds to Maag's formula on permeation grouting. As a result, the validity of this innovative experiment has been verified.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".