Impact of parameter selection on seismic loss and recovery time estimates: A variance‐based sensitivity analysis
Bibliographic record
Abstract
Probabilistic models in performance‐based earthquake engineering propagate uncertainties from key input parameters to output performance measures. Although these models integrate important sources of uncertainty, several model parameters are deterministic and remain constant despite the difficulty in defining them with high confidence based on empirical or theoretical arguments. This study employs variance‐based sensitivity analysis to investigate how uncertainty in (1) demands, (2) fragility functions, (3) building replacement consequences and (4) impeding factor delays impact seismic loss and recovery time estimates. The results indicate that the size of modeling uncertainty added to the simulated demand distribution has the most significant impact on the variance in seismic losses at all, but the highest hazard level, that is, 2475‐year. At low hazard levels, that is, 100 and 475 years, the uncertainty in the capacity of structural components (e.g. slab–column connections) and nonstructural components (e.g. elevator) are the main contributors to variance in downtime to re‐occupancy and functional recovery, respectively. At the 2475‐year intensity level, the uncertainty in building replacement cost and replacement time becomes the primary contributor of the variance in the seismic loss and recovery time outputs due to the high probability of irreparable damage. The analyses presented in this article offer valuable insights into which parameters deserve more attention when conducting seismic loss and recovery time assessments.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".