Adapting a rapid seismic screening method for the evaluation of school buildings
Bibliographic record
Abstract
The poor seismic performance of schools has made their assessment and retrofit a priority in moderate and high seismic zones. Given the large building inventory to evaluate, rapid seismic screening methods are often implemented to prioritize detailed interventions. This paper describes schools’ specific characteristics to be considered when applying these procedures, and shows how the FEMA154 approach can be modified to consider them. The adapted method is a score assignment procedure based on the following six essential characteristics: seismicity, lateral load resisting system, construction year, potential structural weaknesses (or irregularities), potential for pounding of adjacent buildings, and local soil conditions. The method is illustrated with case studies, applied to 101 school buildings in Quebec. Results show that most of the parameters considered influence the final scores. In particular, the treatment of structural weaknesses and potential for pounding proved effective in differentiating the likely seismic performance of the buildings.
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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 | 0.003 | 0.001 |
| 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".