Seismic fragility and risk analysis of electric power substations
Bibliographic record
Abstract
Electric power substations are very vulnerable to earthquakes. This study has analyzed the seismic risk index of electric power substations of Hydro-Quebec. The critical parameters responsible for vulnerability of substations are identified by statistical analysis of field data. Correlation of different parameters with vulnerability, sensitivity of the weighting factors of critical parameters and sensitivity of seismic exposure levels to seismic risk index are also studied by statistical analysis. Study shows that year of manufacture of equipment, anchoring of heavy equipment, load-bearing system of the building, and control systems are the four most critical parameters for vulnerability. Vulnerability of substations largely depends on the performance of circuit breakers and control buildings during earthquakes. An analytical approach is used to determine the seismic fragility of circuit breakers. Risk based design concepts are used to determine the probability of failure of circuit breakers for a range of peak ground accelerations. The fragility curves are obtained by plotting the probability of failure as a function of peak ground acceleration. The fragility curves are used to determine the appropriateness of circuit breakers on various seismic zones.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".