NOVEL DEVELOPMENTS AND FINDINGS FOR THE SAFETY ASSESSMENT OF EARTHQUAKE-EXCITED DAM-RESERVOIR SYSTEMS
Bibliographic record
Abstract
Many dams worldwide have been in service over 50 years and are located in high seismicity areas. The initial seismic design of these critical structures was generally conducted using simplified methods that do not fully take into account of the dynamic nature of earthquake excitation and the complex fluid-structure interaction. Although significant work has been done to evaluate the seismic response of dams, there is still a need to improve commonly used simplified methods and to accurately assess the efficiency of more sophisticated ones. The first part of this paper proposes new practical formulas to evaluate earthquake-induced hydrodynamic loading on concrete dams. This original technique generalizes the classical added-mass formulation by including the effects of dam flexibility and reservoir bottom absorption. Frequency response functions of hydrodynamic pressures within the reservoir are compared to analytical solutions. It is shown that the method accurately predicts hydrodynamic loads and that it can be easily implemented in a computer program or a spreadsheet. The second part of the paper investigates finite element modeling aspects to assess the seismic performance of concrete dams. Several finite element models of dam-reservoir systems with various dimensions are used to conduct frequency and time domain analyses. Potential-based fluid elements and viscous boundary conditions are validated against analytical solutions and they are shown to perform adequately for practical seismic analysis of dam-reservoir systems.
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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 | 0.003 |
| 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) | 0.003 | 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".