Post-erosion mechanical response of internally unstable soil of varying size and flow regime
Bibliographic record
Abstract
One of the leading causes of dam failure is internal erosion. The impact of erosion of non-plastic fine particles, known as suffusion, on the soil structure and strength has been studied experimentally. However, influences including sample size have not been thoroughly investigated. Internally unstable gap-graded cohesionless soil samples with various sizes were investigated using an erosion-triaxial apparatus. Samples were subjected to downward inflows of different seepage velocities. The results indicated that the potential for clogging increased with an increase in specimen length, leading to less fine particle erosion. Internal erosion changed the mechanical soil behaviour even after the loss of fines equal to 5% of the overall sample volume. Eroded specimens with similar intergranular void ratios showed similar undrained post-erosion behaviour. However, the magnitude of the post-erosion initial undrained peak shear strength is a function of coarse particle interlocking, residual fine content, and equivalent intergranular contact index. It was also found that the steady state line remained unchanged after erosion of fine particles and the mobilized friction angle at the steady state line is independent of the residual fine content.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".