Seismic fragility of bridges: An approach coupling multiple‐stripe analysis and Gaussian mixture for multicomponent structures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An approach is developed to build multivariate probabilistic seismic demand models (PSDMs) of multicomponent structures based on the coupling of multiple‐stripe analysis and Gaussian mixture models. The proposed methodology is eminently flexible in terms of adopted assumptions, and a classic highway bridge in Eastern Canada is used to present an application of the new approach and to investigate its impact on seismic fragility analysis. Traditional PSDM methods employ lognormal distribution and linear correlation between pairs of components to fit the seismic response data, which may lead to poor statistical modeling. Using ground motion records rigorously selected for the investigated site, data are generated via response history analysis, and appropriate statistical tests are then performed to show that these hypotheses are not always valid on the response data of the case‐study bridge. The clustering feature of the proposed methodology allows the construction of a multivariate PSDM with refined fitting to the correlated response data, introducing low bias into the fragility functions and mean annual frequency of violating damage states, which are crucial features for decision making in the context of performance‐based seismic engineering.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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