Preliminary Performance Evaluation of a Mechanically Stabilized Earth Wall under Flooding and Rapid Drawdown Conditions
Bibliographic record
Abstract
Seepage and stability performance of a 4-m high and 6-m long MSE wall reinforced with metal strips was evaluated under flooding and rapid drawdown conditions by numerical analysis and full-scale physical modeling. A full-scale wall was constructed with a poorly graded sand backfill at an indoor geo-structure testing facility located at the Royal Military College of Canada. Flooding and rapid drawdown conditions were applied by filling and emptying a water reservoir in front of the MSE wall. Variation of water levels with time in the MSE wall backfill was measured by standpipe piezometers and moisture and suction instrumentation installed in the backfill material. Numerical seepage and stability models were calibrated using data from the physical model tests. A 1-m flooding event (measured from the base of the wall) saturated the MSE backfill in 27 h. Backfill desaturation due to rapid drawdown of the reservoir took about two times as long compared to saturation time. Parametric studies were carried out using the calibrated numerical models to investigate variables including wall height, flood height, and backfill hydraulic properties. Simulations showed that limit equilibrium factor of safety can increase by up to 105% after flooding and decrease by 25% after rapid drawdown depending on water pressure head in front of the wall relative to the wall height.
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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) | 0.001 | 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".