Using the Conditional Spectrum Method for Improved Fragility Assessment of Concrete Gravity Dams in Eastern Canada
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Bibliographic record
Abstract
The accurate estimation of fragility functions requires the proper selection of ground motion records at different intensity levels. However, most of the available fragility assessments of concrete dams use the same records at all intensity levels and often selects them with an inadequate target spectrum. In order to improve the fragility assessment of such structures, this paper proposes the use of records selected with the Conditional Spectrum (CS) method within a multiple stripes analysis. The approach is applied to a dam in Eastern Canada, and a comparison with the methodology used by other studies is done. It is shown that the approach proposed herein allows for the reduction of the seismic response and fragility of the dam. Moreover, the uncertainty related to material properties becomes less significant when using the CS method, and the fragility curves could be reasonably estimated by considering the ground motions as the only source of uncertainty.
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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