A Progressive Approach to Account for Large-Scale Roughness of Concrete–Rock Interface in Practical Stability Analyses for Dam Safety Evaluation
Bibliographic record
Abstract
This paper proposes a progressive approach to assess the properties of large-scale roughness at dam–rock interfaces and the implementation of their effects into practical dam stability analyses. The analysis steps, ranked per increasing degree of complexity, consist of studying a gravity dam monolith using, first, the gravity method (GM), second, the finite element (FE) method (FEM) with a simplified horizontal planar dam–rock interface, and, third, the FEM with a detailed irregular geometry of the dam–rock interface. In the first two steps, the simplification of the rock foundation geometry is paired with the implementation of apparent cohesion and friction angle into the models. These apparent parameters are evaluated based on nonlinear shear strength criteria combined with an interface roughness coefficient (IRC) introduced to characterize the roughness of a dam–rock joint extending along the whole dam footprint. This coefficient is approximated herein numerically based on FE models. The inputs and steps of the progressive approach are illustrated through several examples of typical dam–rock systems and rock profiles based on bathymetric and LiDAR surveys. The results mainly show that the effects of rock foundation roughness on dam sliding stability can be efficiently represented with apparent cohesion and friction angles. The effectiveness of the simplified models coupled with the conservatism of the results they provide are likely to favor their adoption by practicing engineers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".