Predicting the diversion length of capillary barriers using steady state and transient state numerical modeling: case study of the Saint-Tite-des-Caps landfill final cover
Bibliographic record
Abstract
Covers with capillary barrier effect (CCBE) have already been proposed to meet regulatory requirements for landfill final covers. Modeling of CCBE can be a relatively complex and time-consuming task. Simpler, albeit conservative, design tools — such as steady state numerical analyses — can, in certain cases, be justified and have a positive impact in practice. In this study, numerical simulations were performed of the experimental CCBE constructed on the Saint-Tite-des-Caps landfill (Quebec). The CCBE consists of a capillary barrier, composed of sand and gravel, on top of which a layer of deinking by-products (DBP) was installed as a protective layer (also to control seepage). The addition of a protective layer over the infiltration control layer (such as a capillary barrier) is required in most jurisdictions. In many European countries, such as Germany and the Netherlands, a thick “recultivation” layer is required. The results of numerical simulations were compared with the in situ behaviour of the Saint-Tite CCBE as well as with analytical solutions. The effectiveness of the capillary barrier was assessed by quantifying the diversion length (DL), which reflects the lateral drainage capacity of the CCBE, i.e., the capacity to drain water laterally. Collection of the water that has drained laterally prevents seepage into the waste mass. This study shows that when the seepage rate reaching the top layer of the capillary barrier is controlled, it is possible to predict the worst-case scenario in terms of seepage (and therefore predict the shortest DL) using steady state numerical simulations. These simpler-to-perform numerical simulations could be adopted in practice, at least in a pre-feasibility study for cases with a similar profile as the one at the Saint-Tite-des-Caps experimental CCBE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".