Operational Safety of Dams and Hydropower Installations
Bibliographic record
Abstract
This paper discusses simulation approaches to understanding the systems operations of dams and hydropower plants, and provides practical examples from current practice in Canada and Sweden. Dams and hydropower installations are complex structures or systems of interest comprising geotechnical, structural, mechanical, electric, and other subsystems, such as gates, control equipment, and operators. A dam system—in contrast to just the dam—comprises the embankment of the dam along with the various waterways past the dam, and usually with accompanying mechanical and electrical equipment for on-site operational control. Current engineering approaches to dam safety are mostly based on probabilistic risk analysis (PRA) of the geotechnical aspects of the dam (e.g., slope stability under seismic loads, internal piping, overtopping erosion, abutment stability, etc.). PRA primarily address the capability of a dam to withstand loads, such as the demand caused by the design flood and the spillway’s capacity to pass that flood, or the demand caused by the design earthquake and the dam’s capacity to withstand resulting ground shaking. In contrast, experience has shown that many dam failures and perhaps the majority of dam incidents do not result from extreme geophysical loads, but rather from operational events. These incidents and failures occur because an unusual combination of reasonably common events occurs, and that unusual combination of events has an undesirable outcome.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".