Assessing Piping Risks by Finite Elements
Bibliographic record
Abstract
Backward erosion piping (BEP) is a leading cause of failure in dams and levees, but uncertainties in erosion progression characteristics and soil properties provide significant challenges to a deterministic analysis approach. For this reason, a risk-based approach to BEP failure is of great interest to engineers. The probability of failure is most commonly determined through the use of event trees and engineering judgment. When using this approach, geologic variability in the subsurface is incorporated into risk analysis through estimates of subjective probabilities regarding the existence of a continuous, erodible layer in the foundation. Recently, however, there have been developments in the random finite element method (RFEM) modeling of BEP progression that have demonstrated alternate means of assessing the probability of pipe progression in spatially variable soils. In this study, an RFEM model for BEP progression is presented. Results are presented to illustrate the influence of soil variability on the probability of BEP progression. Results indicate that the probability of BEP progression increases as the spatial correlation length increases.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".