Erosion monitoring during core overtopping using a laboratory model with digital image correlation and X-ray microcomputed tomography
Bibliographic record
Abstract
Core overtopping in embankment dams is an important phenomenon that may lead to contact erosion along the core–filter interface. This paper presents new experimental results regarding erosion mechanisms at the core–filter interface during core overtopping. The experimental results were obtained using a reduced-scale model with a variable upstream water level. Digital image correlation (DIC), microcomputed tomography (μCT), and sediment collection at the outlet were used to quantify erosion. Four experimental runs were conducted with a till core and different filters. Only one of the four filters satisfied the filter criteria that were applied. No contact erosion occurred during this test. For filters that did not respect the filter criteria, piping occurred within the core along the downstream slope when the water level reached the top of the core. As a result of the self-healing process within the core material, the erosion rate decayed with time as the hydraulic gradient increased. Results for DIC mainly reflected settlements within the filter due to erosion and a soil arching effect. The magnitude of the displacement vector obtained with DIC is directly proportional to the volume of till eroded. μCT showed that contact erosion occurred continuously.
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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 | 0.000 |
| 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 | 0.001 | 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".