A pre-earthquake regional seismic risk estimation methodology and a prioritization approach for regional risk assessment
Bibliographic record
Abstract
The 2023 Kahramanmaras earthquakes highlighted the urgency of assessing and intervening in old, seismically deficient buildings. However, not all old buildings collapse during earthquakes despite noncompliance with current codes. Prioritizing buildings based on seismic risk offers a reasonable approach. This study analyzed 17 242 1–10 story pre-2000 RC buildings in Kahramanmaras, considering factors such as soil conditions, number of stories, construction year, fault proximity, and seismic demand, and their effects on structural damage distribution. Risk class distributions of these buildings are estimated by a pre-earthquake regional seismic risk estimation methodology developed based on the outcomes of performance-based rapid seismic risk assessment of pre-2000 buildings in Istanbul using PERA2019 methodology, and compared with actual earthquake damage. Results showed a strong correlation between “very high risk” buildings and those heavily damaged or collapsed. Finally, based on the results, a regional-scale prioritization strategy was proposed to efficiently identify high-risk buildings prone to excessive damage.
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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 | 0.001 | 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".