Simulation of Colloidal Silica Grout Injection Using Shear Effects
Bibliographic record
Abstract
The colloidal silica grout systems are being investigated for environmental containment barriers and ground improvement. Colloidal silica (CS) grout system behaves as a fluid but reacts after a predetermined time to form a solid, semi-solid or gel. These solid or semi-solid gels offer several orders of magnitude reduction in hydraulic conductivity in the porous media. A numerical model is developed to simulate chemical grouting into porous media by combining a groundwater flow simulation model (MODFLOW) and a 3D multi-species reactive transport model (RT3D). The methods to estimate the grout gel viscosity as a function of gel cure time (gel age), shear rate, and grout concentration are incorporated. The non-uniform gel viscosity is indirectly incorporated into MODFLOW by changing the effective hydraulic conductivity in each cell. The present model is used to analyse the experimental observations on colloidal silica grout injection into a sand column. The model was able to reproduce the observed injection pressures to a large extent. It has been shown that the shear effect needs to be incorporated in the grout gelation model. The model will help in better understanding the physics of grouting, the processes taking place and identifying the parameters.
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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 | 0.001 | 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".