Practical Formulas for Frequency Domain Analysis of Earthquake-Induced Dam-Reservoir Interaction
Bibliographic record
Abstract
Dam-reservoir dynamic interactions are complex phenomena requiring advanced mathematical and numerical modeling. Although available sophisticated techniques can handle many aspects of these phenomena, simplified procedures are useful and still needed to globally evaluate the dynamic response of dam-reservoir systems. This paper presents and validates an original practical procedure to investigate earthquake induced dam-reservoir interaction in the frequency domain, including the effects of dam flexibility, water compressibility, and reservoir bottom wave absorption. The procedure relates hydrodynamic pressure due to any deflected modal response of a two-dimensional gravity dam on a rigid foundation to hydrodynamic pressure caused by a horizontal rigid body motion. New analytical expressions that can be easily programmed in a spreadsheet package or implemented in a dam structural analysis program are also proposed to conduct simplified fundamental mode earthquake analysis of gravity dams. The techniques presented can be efficiently used to provide valuable insight into the effects and relative importance of the various parameters involved in the dynamic response of dam-reservoir systems. Although the mathematical derivations and closed-form expressions developed were applied to dam-reservoir systems herein, they can be easily adapted to other fluid-structure interaction problems.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".