A Simplified Method for Performing Vector-Valued Probabilistic Seismic Hazard Analysis
Bibliographic record
Abstract
ABSTRACT For a seismic risk analysis, the seismic hazard results are usually combined with fragility functions to calculate the probability of seismic losses. Because vector-valued ground-motion intensity measures (IMs) are increasingly used by researchers and practicing engineers to evaluate fragility functions, seismic hazard calculations in terms of vector-valued IMs are required, which are determined by vector-valued probabilistic seismic hazard analysis (PSHA). However, the large computational cost associated with vector-valued PSHAs impedes the application of vector-valued IMs in performing a seismic risk analysis. The study presents a simplified method for conducting vector-valued PSHAs. This simplified method is developed using proposed equivalent earthquakes that are derived from conventional seismic hazard deaggregation. Formulas for performing vector-valued PSHAs using the simplified method are also proposed. Based on a numerical example, this study demonstrates the accuracy and efficiency of the proposed method.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".