Permeation Grouting in Silty Sand for Soil Stabilization and Water Control
Bibliographic record
Abstract
Polyurethane and acrylate resins are routinely injected into soils to gain strength and control water flow. However, most published data regarding these materials’ performance are derived from laboratory samples created under ideal conditions. In this study, a large container was filled with a silty SAND (SM) to simulate in situ soil conditions frequently found in Vancouver, BC, and British Columbia’s Lower Mainland. The injection pressures, volumes, lift heights, and other details have been compiled to evaluate this methodology for permeation grouting. In situ and laboratory testing will be used to verify the material performance and the strength of the soil/resin composite. Dynamic cone penetrometer (DCP), Consolidated Isotropically Undrained Triaxial (CIU), Unconfined Compressive Strength (UCS), and Direct Simple Shear (DSS) testing have been performed on the polyurethane and acrylate soil composites. The soil type, compaction methods, injection methods, and testing performed have been documented and are presented in the final paper. Initial testing results indicate considerable strength gain with polyurethane treated soil versus untreated soil and the applicability of acrylate treatment in support of soil excavation projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".