Earthquake Magnitude and Shaking Intensity Dependent Fragility Functions for Rapid Risk Assessment of Buildings
Bibliographic record
Abstract
An integrated web application, referred to as ER2 for rapid risk evaluator, is under development for a user-friendly seismic risk assessment by the non-expert public safety community. The assessment of likely negative consequences is based on pre-populated databases of seismic, building inventory and vulnerability parameters. To further accelerate the computation for near real-time analyses, implicit building fragility curves were developed as functions of the magnitude and the intensity of the seismic shaking defined with a single intensity measure, input spectral acceleration at 1.0 s implicitly considering the epicentral distance and local soil conditions. Damage probabilities were compared with those obtained with the standard fragility functions explicitly considering epicentral distances and local site classes in addition to the earthquake magnitudes and respective intensity of the seismic shaking. Different seismic scenarios were considered first for 53 building classes common in Eastern Canada, and then a reduced number of 24 combined building classes was proposed. Comparison of results indicate that the damage predictions with implicit fragility functions for short (M ≤ 5.5) and medium strong motion duration (5.5 < M ≤ 7.5) show low variation with distance and soil class, with average error of less than 3.6%.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".