Centrifuge modeling of municipal solid waste landfill failures induced by rising water levels
Bibliographic record
Abstract
New types of synthetic municipal solid wastes (MSWs) were developed, exhibiting engineering characteristics similar to those of real MSWs at different states of degradation in terms of total unit weight, void ratio, water content, compressibility, permeability, stress–strain relationship, and shear strength. Using these synthetic MSWs, several centrifuge model tests on the stability of geosynthetics-lined landfills with rising water levels were performed. The test results revealed the developing processes of MSW landfill failures induced by rising water levels. In most tests, a continuous slide occurred once a critical water level was attained. The ratio of critical water level and landfill height was 0.75–0.92 for present model tests using fresh or partly degraded synthetic MSWs with landfill slope ratio of 1:1–1:3, increasing with the increases of MSW degradation states and decreases of the slope ratio. An engineered berm increased the critical water level by 1.3%–9.5%. The landfills without an engineered berm tended to slide along the bottom liner; however, for normal landfills with an engineered berm and a slope ratio of 1:3, penetrating cracks developed when the failures occurred. These results provide a good reference for water level and stability control of the landfill, and the construction of an engineered berm is suggested to improve the stability of these landfills.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".