An Investigation of Factors that Influence the Water Diversion Capacity of Inclined Covers with Capillary Barrier Effects
Bibliographic record
Abstract
Covers with capillary barrier effects (CCBEs) can be used as an alternative to more traditional covers that rely on materials with a low saturated hydraulic conductivity. A typical CCBE includes a fine-grained soil layer placed on a coarser material. Other layers can also be added to help the cover play its role(s) efficiently. In a cover built on a flat area, the capillary barrier effect at the interface between the fine and coarse materials allows the finer soil layer to store incoming water, which can later be released by evaporation. Such Store-and-Release covers can be quite convenient in arid and semi-arid conditions. In sloping areas, a CCBE also acts as a lateral water diversion system. Part of this diversion occurs along the sloping interface between the two superimposed soils. It can contribute significantly to the reduction of water percolation deeper into the underlying wastes. However, inclined CCBEs are more complex, as they are influenced by many factors that are not yet fully understood. In such layered covers, moisture is not evenly distributed along the length of the slope. Under some conditions, the moisture-retaining layer can reach a critical degree of saturation at a certain down dip location, which increases infiltration of water into the coarse material and reduces the cover efficiency. This paper presents some of the main results of an ongoing investigation of inclined CCBEs that includes testing on a physical model, field work on large scale covers, and numerical analyses of various cover scenarios. Emphasis is placed here on numerical simulations to assess some of the influence factors that affect the diversion capacity and moisture distribution. The numerical results show how the diversion capacity is affected by the saturated hydraulic conductivity of the fine-grained soil, by the precipitation rate and duration, and by the layer thickness. The findings presented here can be useful for the design of inclined CCBEs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".