Preliminary Numerical Modeling of a Mechanically Stabilized Earth Wall under Flooding and Rapid Drawdown Conditions
Bibliographic record
Abstract
Design guidelines to ensure stability and performance of mechanically stabilized earth (MSE) walls under flooding and rapid drawdown conditions are a priority for the Wisconsin Department of Transportation (WisDOT). Performance of a 4-m high MSE wall reinforced with metal strips was evaluated. Seepage and slope stability analysis were conducted on the model wall. Porewater pressure and free water surface obtained from transient and steady state seepage analyses were used as initial conditions for slope stability analysis. Results indicated that for a poorly graded (SP) sand backfill with a hydraulic conductivity of 7.4×10-4 cm/s, a 2-m high flooding event in front of the MSE wall saturates the backfill and reaches the steady state condition in 6 hours. The limit-equilibrium factor of safety increased from 2.2 before flooding to 2.8 immediately after flooding and equilibrates back to 2.2 after 6 hours. On the other hand, rapid drawdown of a 2-m face flooding event drops the factor of safety from 2.2 to 1.7. Factor of safety increases back to 2.2 in about 6 hours after rapid drawdown. Flooding and rapid drawdown causes variation in the stability factor of safety of the MSE wall of around 25%. Construction of a 4-m high instrumented MSE wall to conduct complementary full-scale physical testing is ongoing. The wall will be subjected to flooding, precipitation, and rapid drawdown conditions. Porewater pressures, displacements, strains, and stresses within the backfill will be measured. The anticipated outcome of the simulation and testing program is a series of design graphs for use by WisDOT engineers.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".