Seepage forces and confining pressure effects on piping erosion
Bibliographic record
Abstract
An experimental study of piping erosion is presented. Various artificial granular filter and base soil combinations are tested in a permeameter under variable confining pressures to determine the critical gradient where soil erodes through the filter. Previous research has concentrated on establishing a filter to soil grain size ratio criteria, typically D15f /D85s < 4, which separates safe from potentially unsafe filters. These works often ignored self-filtration zone formation phenomena and rarely documented the influence of variables such as confining pressure, filter thickness, and hydraulic gradient. To adequately control all variables that may influence piping erosion, a new permeameter was designed and careful attention was paid to sample preparation. Artificial glass beads were water pluviated to permit consistent repeatable uniform samples. By monitoring head, settlement, confining pressure, amount of eroded soil, and water outflow rate, the onset of piping can be determined. It is shown that the grain-size ratio is the most important parameter affecting piping erosion. A soil-filter system with D15f /D85s < 8 will not fail, whereas a D15f /D85s > 12 will not be able to retain base soil. For 8 < D15f /D85s < 12, piping will only occur if the hydraulic gradient exceeds a critical threshold. The critical gradient is lower if the head is rapidly increased, as a filtration zone is inhibited from forming. A very thin filter has a similar effect. Stability is somewhat inversely related to the confining pressure level for small grain-size ratios.Key words: filters, seepage forces, confining stress, piping erosion.
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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 | 0.000 | 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 | 0.001 |
| 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".