Seismic Resilience Assessment in Optimally Integrated Retrofitting of Existing School Buildings in Italy
Bibliographic record
Abstract
Modern society requires that structures exhibit greater levels of resilience, especially under earthquakes. The seismic resilience of buildings is thus gaining increased attention as a particular, beyond-code approach. Seismically retrofitted buildings behave satisfactorily under expected earthquake scenarios; however, this does not guarantee operativity after a seismic event. This study critically reviews several methods currently available in the literature that quantify the seismic resilience level of buildings from different perspectives. An existing reinforced concrete school building, retrofitted according to four distinct strategies, is first evaluated in terms of seismic resilience levels. The overview and critical analysis of available resilience assessment frameworks determine the most suitable parameters to measure the seismic resilience for buildings. Subsequently, this metric is incorporated as an additional decision variable into an integrated seismic and energy retrofitting set of strategies. A multicriteria decision-making analysis is performed to select the optimally combined seismic and energy retrofitting alternative under social, technical, environmental evaluation, and seismic resilience aspects. We show how resilience impacts the preference for integrated seismic and energy retrofitting strategies, especially when this metric is considered as an annualized expected value.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".