Accounting for Earthquake-Induced Dam-Reservoir Interaction Using Modified Accelerograms
Bibliographic record
Abstract
This paper proposes a new practical and efficient procedure to investigate the seismic response of gravity dams that (1) avoids the finite or boundary element discretization of the impounded reservoir; (2) can be applied using standard finite-element software not necessarily including fluid-structure interaction capabilities; and (3) accounts for dam and foundation flexibility, water compressibility, and reservoir bottom wave absorption. The proposed technique consists of modifying the original input ground acceleration to obtain a new accelerogram that directly accounts for the complex effects of fluid-structure interaction. This new accelerogram can then be applied to a dam or dam-foundation system without the impounded reservoir. The exact and simplified formulations of the proposed method are developed, and its efficiency is validated through examples of dam-reservoir systems with different geometries. Very satisfactory agreement is obtained when comparing the results to more advanced finite-element solutions including fluid-structure interaction capabilities. The new procedure enhances the efficiency of seismic assessment of gravity dams by reducing the modeling and computational burden associated with reservoir discretization while keeping the main advantages of conventional solid finite-element software in conducting dynamic analyses and providing the resulting stresses, strains, force distributions, and other dam response indicators.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".