Influence of Permeability Anisotropy on Seepage and Slope Stability of an Earthen Dam During Rapid and Slow Drawdown
Bibliographic record
Abstract
Permeability one of the most significant parameters influencing reservoir performance is the accessibility that fluids flow through, this study investigates the influence of anisotropic permeability on seepage patterns and slope stability of an earthen dam during rapid and slow drawdown conditions using physical and numerical modeling.Physical modeling using a small-scale dam model and numerical simulations using SEEP/W and SLOPE/W were performed for rapid and slow drawdown scenarios.The present study focuses on the impact of hydraulic anisotropy and soil characteristics on the seepage rate and the stability evaluation of the upstream and downstream slopes for rapidly and slow drawdown scenario during transient flow regime and compared the findings with numerical results.The results show that anisotropic permeability increased seepage rates by over 75% and reduced slope stability by over 55% compared to the isotropic case.The effects were more significant for rapid drawdown conditions.Moreover, effects of hydraulic anisotropic on the physical model's progress to saturation and the period of time needed for reaching saturation (steady state) has been twice as long as it takes an isotropic model to finally reach saturation, this is because a drop in saturated hydraulic conductivity levels causes flow rates to decrease, which in turn causes a gradual development of seepage inside the earth dam.The study demonstrates the importance of incorporating anisotropic permeability for accurate prediction of seepage and slope stability during drawdown.
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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 | 0.000 | 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) | 0.000 | 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".