Effect of backfilling surface settlement trough on waste cover leakage
Bibliographic record
Abstract
The effect of backfilling of a surface differential settlement trough to reduce leakage is explored both experimentally and numerically. The field experiment examined two lined sections each with an 11 mm-diameter hole in the liner on a nominally 4 horizontal:1 vertical slope. A 2 m by 3 m, 0.3 m deep depression was filled with a 50-50 sand-snow mixture in winter to give a continuous 4H:1V slope prior to covering with the liner and 0.3 m of cover soil. Spring thaw induced a differential settlement trough up to 0.14 m deep. A second section with a similar trough was backfilled with cover soil to reinstate the 4H:1V surface while the settlement depression in the liner remained. Over the 15 months of monitoring, the backfilling reduced leakage by 57% from a annual total of 565 L to 244 L (i.e., a 60% reduction in colder seasons, from 351.3 L to 137.8 L together with a 45% reduction in warmer seasons, from 141.8 L to 77.6 L). A 3D numerical model showed encouraging agreement with the experimental results. The model indicated an inverse relationship between leakage and slope gradient, and a direct relationship between leakage and depression depth and upgradient distance to the depression. The effect of cover hydraulic conductivity was complex. • First field experiment to demonstrate the effectiveness of filling surface trough as a repair method in reducing leakage through a geomembrane hole on a slope experiencing differential settlement. • Enhances the leakage prediction as a result of differential settlement using a validated 3D numerical model. • Provides guidance for waste cover design from the perspective of leakage control.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".