Improving recovery time consequence models in regional seismic risk assessment by leveraging high-fidelity building-specific recovery simulations
Bibliographic record
Abstract
Current regional seismic risk modeling approaches predominantly emphasize direct financial loss as the primary measure of earthquake impact. Integrating recovery time into these models can better support decisions toward community resilience and a shift toward more people-centered and equitable disaster risk modeling. Although advanced probabilistic models for estimating recovery times of individual buildings are becoming more common, regional-scale quantification for community-level simulations remains overly simplified, often relying on low-fidelity approaches without the explicit consideration of key underlying drivers of recovery. This study proposes a methodological approach to improve recovery time consequence models in regional simulations by leveraging high-fidelity, building-specific recovery simulations. We employ downtime fragility functions and consequence models to characterize key elements of the recovery process, such as impeding factor delays and repair times. This approach provides a probabilistic estimation of post-earthquake recovery time to achieve two distinct recovery states, that is, shelter-in-place and functional recovery. The proposed methodology enables a more efficient quantification of recovery time at the building portfolio scale by leveraging simulation-based recovery time consequence models that could be seamlessly integrated into regional risk assessments. The proposed consequence models are trained based on 50,000 TREADS (Tool for Recovery Estimation And Downtime Simulation) recovery simulation results of modern high-rise (8–24 stories) reinforced concrete shear wall buildings at various ground-shaking intensity levels and applied, for illustrative purposes, to a portfolio of 218 buildings across Metro Vancouver, BC, under a magnitude-9 Cascadia subduction zone earthquake.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".