From Scaled-Down to Full-Scale Rockfill Dams with Dry-Stone Pitching: A Numerical Study
Bibliographic record
Abstract
Rockfill dams with dry-stone pitching are about one hundred years old structures that are present in the French heritage.They are composed of a backfill made of decametric blocks and a protective pitching made of hand-placed stones without mortar on both dam's downstream and upstream faces.Electricity of France, a French stakeholder, operates approximately ten dams of this kind.However, the mechanical behavior of such a structure which is discrete in nature and that can bear large deformations is not very well understood, even if few studies have been conducted over the last decades.This study is a step forward for a better understanding of the role of the pitching in both static and seismic behaviors of such dams.Firstly, a mixed DEM-FEM numerical approach for the modeling of such dams is developed and validated based on experiments involving scaled-down rockfill dams.Secondly, simulations on full-scale dams are carried out and the role of the pitching considering different building techniques or properties is quantified.They clearly show large-scale effects at stake in the structure and the key role of the pitching weight and pitching-backfill interface.Finally, the perturbation in the dam resistance induced by a berm which is typically built on the downstream face is investigated.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".