Development of a damage simulator for probabilistic seismic vulnerability assessment of electrical installations
Bibliographic record
Abstract
Recent earthquakes have revealed the vulnerability of electric power networks to seismic events. To assess their susceptibility to seismic shaking, a user-friendly damage simulator is developed. It consists of two major components: seismic hazard and damage calculation, whereas the inventory of the exposed transmission towers and substations and their vulnerability are provided by the user. The application uses open-source software without any financial costs to users. The computation starts with selection and calculation of either probabilistic or user-defined seismic hazard scenarios including the local site effects. Spectral accelerations at the fundamental vibration period of transmission towers and the peak ground accelerations for substations are considered as intensity measures (IMs) of the transitory seismic shaking. The probabilistic damage assessment incorporates uncertainties in the site parameters and vulnerability of electric installations. The epistemic uncertainty is considered through the logic tree approach introduced in the latest seismic hazard of the National Building Code of Canada, aleatory uncertainty is captured with the Monte Carlo analysis option, whereas the inherent uncertainty related to the structural dynamic response and damage assessment is accounted for with a set of fragility curves describing different damage states. An example of the seismic site characterization, hazard assessment and vulnerability analysis of Hydro-Quebec electrical installations in the Saguenay region, Canada, is presented to illustrate the capacity of the developed software to predict potential damage. Results indicate the resistance of transmission towers and the relatively high vulnerability of substations to seismic shaking.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".