Investigation of grouting diffusion in a single rock fracture considering the influences of obstructions
Bibliographic record
Abstract
Grouting reinforcement was used to improve rock strength and avoid seepage in rock engineering. A self-developed visualised test platform was developed and the influences of different fracture openness on grouting diffusion modes were revealed; the Bingham rheological model was imported to simulate the grouting diffusion process in a single plate fracture, the spatiotemporal distribution of the velocity field under different obstructions was determined using the finite element method. The results indicate that (i) the grout diffuses faster with the increase of fracture openness, while a stagnation effect of the grouting diffusion velocity behind the obstruction occurs. (ii) Due to obstructions, the grouting diffusion process can be divided into four stages: circular diffusion, flat diffusion, vortex diffusion, and butterfly diffusion. (iii) The grouting diffusion area is divided into a fully reinforced zone and a semi-reinforced zone, and the area of the latter increases with the fracture openness, while being little affected by the size of any obstruction. (iv) Furthermore, some new grouting diffusion laws were revealed considering the asymmetrical arrangement of obstructions. The results presented in this work will be helpful for describing and predicting the grouting process in fracture networks.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".