An analytical framework to assess earthquake‐induced downtime and model recovery of buildings
Bibliographic record
Abstract
While modern seismic design codes intend to ensure life‐safety in extreme earthquakes, policy‐makers are moving toward performance objectives stated in terms of acceptable recovery times. This article describes a framework to probabilistically model the post‐earthquake recovery of buildings and provide quantitative seismic performance measures, expressed in terms of downtime, that are useful for decision‐making. Downtime estimates include the time for mobilizing resources after an earthquake and conduct necessary repairs. The proposed framework advances the well‐established Federal Emergency Management Agency (FEMA) P‐58 and Resilience‐based Earthquake Design initiative (REDi) methodologies by modeling temporal building recovery trajectories to target recovery states, such as stability, shelter‐in‐place, reoccupancy, and functional recovery. The shelter‐in‐place recovery state accounts for relaxed post‐earthquake habitability standards, in contrast with the reoccupancy recovery state that relates to pre‐event habitability criteria. Analogous to safety‐based codes, which specify a threshold for the probability of collapse under a given ground motion shaking intensity, this framework permits evaluating the probability of a building not achieving a target recovery state, for example, shelter‐in‐place, immediately after an earthquake, or, alternatively, the probability of achieving a target recovery state, for example, functional recovery, within a specified time frame. The proposed framework is implemented to evaluate a modern 12‐story residential reinforced concrete shear wall building in Seattle, WA. The assessment results indicate that under a functional‐level earthquake (roughly equivalent to ground motion shaking with a return period of 475 years), the probability of not achieving shelter‐in‐place immediately after the earthquake is 22%, and the probability of downtime to functional recovery exceeding 4 months is 88%, which far exceeds acceptable thresholds suggested in the 2015 National Earthquake Hazards Reductions Program (NEHRP) guidelines and FEMA P‐2090.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".