Capillary effect on flow in the drainage layer of highway pavement
Bibliographic record
Abstract
This paper aims to examine capillarity effect on flows in the drainage layer of highway pavement. A two-dimensional (2-D) model based on the Richards equation was used to simulate saturated and unsaturated flows in the drainage layer. For comparison, flows were also simulated using a 1-D Boussinesq equation based model and a 2-D model based on the Laplace equation, both assuming saturated flow only. The drainage layer was modeled with sand and gravel, which possess similar hydraulic properties to those of commonly used filling materials in practice. The results showed that the two saturated flow models agreed well with each other, indicating the dominance of horizontal flow in the drainage layer. However, their predictions differed significantly from those of the variably saturated flow models. The latter model predicted significant flow activities in a relatively large unsaturated zone, especially for a sandy drainage layer. Such unsaturated flow contributes to and enhances the capacity of the drainage layer. With the unsaturated flow neglected, the saturated flow models over-predicted the extent of the saturated zone and hence the groundwater table elevation. As the current engineering design of the drainage layer is typically based on the groundwater table elevation predicted by the saturated flow models, the finding of this study suggests that the design criterion is likely to lead to over-design of the drainage system. Further work is also required to prove the practical significance of the capillary effect and account for other factors.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".