Systematic investigation on surrogate and active learning-based multivariate seismic fragility analysis under multiple sources of uncertainties
Bibliographic record
Abstract
This study proposes an advanced methodological framework for systematic investigations toward generalized and efficient multivariate seismic fragility analysis that integrates surrogate modeling and active learning. Aimed at reducing the computational demands of high-fidelity nonlinear time-history response analyses, the framework enables reliable fragility estimation under multiple sources of uncertainties. It combines Gaussian process regression with a convergence-guided sampling strategy for active learning, supported by norm-based error metrics to systematically control model accuracy. Global sensitivity analysis is then employed to identify key input variables, whereas the corresponding multivariate fragility surfaces have the ability to capture interaction effects between correlated intensity measures of ground motions, underscoring the limitations of traditional univariate approaches. Detailed, in-depth discussions are presented regarding the overall framework, strategies for surrogate modeling, techniques for fragility dimensionality reduction, as well as a thorough design process for active learning. The framework is validated and systematically examined through a representative case study, demonstrating its capability of achieving robust fragility estimates with significantly fewer simulations. These results highlight its potential for supporting scalable seismic risk assessment and broader applications in performance-based multi-hazards engineering.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".