Simplified seepage rate estimation of zoned embankment dams
Bibliographic record
Abstract
Seepage rate is a crucial parameter in embankment dam safety management, informing early warning systems and risk-based decision-making. However, under- or over-estimating seepage quantity during design-stage, often due to simplified 2D modeling, leads to discrepancies between predicted and measured values. This study proposes a modified method for estimating seepage rates in zoned embankment dams, improving upon Yang et al. A closed-form solution is also newly derived by addressing the limitation of previous study. The approach enables realistic prediction of partitioned seepage rates within the core layer and separately accounts for seepage contributions from the foundation and abutments. The accuracy of the method was verified through comparison with extensive measured seepage data from a well-monitored central cored rockfill dam. The close agreement between estimated and observed values supports the applicability of this modified method for establishing reliable seepage management criteria in embankment dams. The findings highlight that seepage through the foundation typically surpasses that through the core, attributable to differences in permeability characteristics. The ability to predict seepage quantities through different dam components individually lays the groundwork for improved seepage monitoring plans and enhanced dam safety management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".