Combined Seepage and Slope Stability Analysis of Rapid Drawdown Scenarios for Levee Design
Bibliographic record
Abstract
The strength of levees can be affected during fluctuations in the water table. It is also possible for the climate to have an influence on the position of the water table in an earth levee. Traditional methods have resulted in approximate methods for dealing with the transient fluctuations of the water table in a levee. These approximations are generally accepted in engineering practice but the question can be rightfully raised as to how these approximations compare to a rigorous transient combined seepage and slope stability analysis. Software technology has significantly changed in recent years and is now at the point where it is much easier to perform transient seepage analyses. There are new questions that can be asked. Does an effective stress analysis diverge significantly from the 3-stage Duncan (1990) analysis? If so, under what conditions? This paper compares the Duncan (1990) three-stage methodology for analyzing rapid drawdown scenarios to a combined transient seepage and slope stability analysis. Traditional limit equilibrium methods will be utilized in the slope stability analysis and the accommodation of saturated and unsaturated pore-water pressures will be considered. Analyses of a number of typical cross-sections will be considered in order to determine the potential influence of geometry. The intent of the paper is to illustrate scenarios under which the Duncan (loc. cit.) methodology produces similar results to the results of a more rigorous analysis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".