Duration-dependent Fragility Functions for Seismic Damage and Loss Assessment of High-rise Buildings
Bibliographic record
Abstract
The availability of large repositories of recorded and synthetic ground motions coupled with structural response simulation data in recent years has led to the increased popularity of data-driven models for seismic damage and loss assessment of buildings. This paper explores the benefits of using bivariate fragility functions to estimate earthquake-induced damage and economic loss in high-rise buildings. The dataset used in this study contains 15,000 simulations of modern high-rise reinforced concrete shear wall buildings which are subjected to ground motion records at five different intensity levels ranging from 100-yr to 4975-yr return periods. The proposed functions are conditioned on average spectral accelerations and ground motion significant duration. The results indicate that bivariate fragility functions improve damage state prediction success by 15% relative to conventional univariate functions (standard of practice). To develop bivariate functions, nominal and ordinal probit regression models are fit to the dataset. Although both models improve the predictive performance considerably, ordinal functions can lead to a 10% reduction in misclassified collapse instances, i.e., the minority class. Univariate functions tend to overestimate seismic losses at lower ground motion intensity levels while underestimating them at higher intensities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".