Numerical Simulation of Chemical Grouting in Heterogeneous Porous Media
Bibliographic record
Abstract
A mathematical model to simulate chemical grout injection, grout curtain formation in aiding rational design of chemical grout systems in saturated porous media is proposed. Three-dimensional modular groundwater flow simulation model (MODFLOW) and three-dimensional multi-species reactive transport (RT3D) model are combined and modules for the gelling process are incorporated to simulate the grouting process. The paper investigates the influence of varying degrees of soil heterogeneity and layering on grout barrier formation through numerically generated hydraulic conductivity fields. Layer persistence and range of conductivity can be specified during generation of conductivity fields. Grout barrier performance is assessed by simulation of grout injection in a three-dimensional domain followed by determination of post-grouted conductivity field and calculation of overall grout curtain hydraulic conductivity using a flow model. The simulation results show that spatial variability control the effectiveness of grout curtain performance. About 80–90% of the 25 hydraulic conductivity fields with high variability failed to reach the desired effective hydraulic conductivity.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".