Quantifying uncertainties and correlations of engineering demand parameters of building structures for regional seismic loss assessment
Why this work is in the frame
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Bibliographic record
Abstract
Abstract For an accurate regional seismic loss assessment, it is essential to quantify the uncertainties and correlations of the engineering demand parameters (EDP) of the building structures. Previous studies predicted the mean EDP of each structure by a regression function of the selected intensity measure (IM), while its variability is described by the “EDP residual.” The authors recently proposed a new formulation and Incremental Dynamic Analysis (IDA)‐based methods to evaluate the correlation between EDP residuals. This paper proposes an IM‐invariant method for estimating the variances and correlations of the EDP residuals of building structures. Based on the EDP residuals of various buildings estimated using the proposed method, primary structural characteristics affecting EDP residuals are identified. In addition, this study develops EDP estimation regression equations using predictive variables defined based on the identified structural characteristics to facilitate consideration of the EDP residual correlation in regional seismic loss assessment. Numerical examples verify the regression models and demonstrate that the proposed method can improve the accuracy of a regional loss assessment by considering the building types in the inventory.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it