An analytical framework to estimate downtime and model the recovery of buildings after an earthquake
Bibliographic record
Abstract
While modern seismic design codes intend to ensure life-safety in extreme earthquakes, policy-makers are moving towards performance objectives stated in terms of acceptable recovery times. This thesis describes a framework to estimate downtime and model the post-earthquake recovery of buildings. Downtime estimates include the time for mobilizing resources after an earthquake and conduct necessary repairs. The proposed framework advances the well-established FEMA P-58 and REDi methodologies by modeling temporal building recovery trajectories to target recovery states such as stability, shelter-in-place, reoccupancy, and functional recovery, as well as by providing probabilistic seismic performance measures that are useful for decision-making. The proposed framework is implemented to evaluate a range of modern 8- to 24-story residential reinforced concrete shear wall buildings located in Seattle, WA. The assessment results indicate that under a functional-level earthquake (roughly equivalent to ground shaking with a return period of 475-years), the average probability across all building heights of not achieving a target shelter-in-place recovery state immediately after the earthquake is 16%, and the probability of downtime to functional recovery exceeding four months is 91.5%. These probabilities exceed the 10% threshold suggested for similar performance measures in the 2015 NEHRP guidelines and FEMA P-2090, respectively. Furthermore, the framework is used to quantify the impact of design strategies on the building’s downtime performance. The results illustrate that certain structural design interventions are effective in ensuring a small probability (<10%) of not achieving the shelter-in-place recovery state immediately after the earthquake but are ineffective in achieving an aggressive four-month target downtime to functional recovery, for which, non-structural enhancements and mitigation measures to impeding factor delays would need to be considered.
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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 | 0.005 |
| 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 | 0.001 |
| 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".