Microscale to macroscale bentonite grout flow in sand
Bibliographic record
Abstract
Permeation grouting is a soil improvement technique that involves injecting a grout into the pores of a soil volume to enhance its engineering properties. Bentonite grouts are commonly used for seepage control, and their effectiveness in improving the resistance of sand to static and cyclic loading has been demonstrated. However; modeling bentonite grout flow has been challenging due to its complex rheology and the intricate nature of soil pores. Since grouts exhibit non-Newtonian rheological behavior, using Darcy law with a constant hydraulic conductivity cannot accurately describe grout flow in soil. In this paper, we propose a model that represents soil pores as idealized capillary tubes. This model derives the macroscale grout behavior in soil from the microscale grout behavior inside individual pores. It incorporates the grout’s power law rheological model and considers a nonzero slip boundary condition between the grout and the walls of the capillary tubes. To evaluate the model, a series of experiments was performed under different conditions. The proposed model offers a simple and robust method to predict the relationship between injection pressure and injection rate for power law grouts. It also highlights the importance of considering a nonzero slip boundary condition for bentonite grout injection.
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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) | 0.000 | 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 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".