Application of Dormant Reliability Analysis to Spillways
Bibliographic record
Abstract
Dams are essential infrastructures for water supply, flood control, energy production, and irrigation. A critical component for the safety of a dam is the spillway system which, by controlling releases, prevents overtopping of the dam. This in turn reduces impacts associated with excessive downstream flows and upstream water levels on infrastructures, the population, and the environment. This paper addresses reliability issues related to emergency spillways and specifically the estimation of their reliability level after prolonged periods of dormancy. During dormancy, spillway components are exposed to the environment and sustain cumulative damage that may trigger latent failures or failures on demand. Regular inspections and tests are used to detect and remediate latent failures and to assess the level of deterioration of components. The purpose of this study is to develop procedures to account for dormancy in the reliability analysis of spillways. It also demonstrates how these procedures can be used to evaluate the impact of the frequency of inspections and tests on the overall reliability of the spillways. This paper introduces measure of performance, dormant availability analysis, and dormant availability analysis via integrity assessment as methods to illustrate the unavailability or probability of failure on demand of a spillway system as a function of its dormancy period. This information can be used to determine the optimum frequency of inspection and tests taking into account the safety of the structure as well as the costs associated with inspection and testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".