A NON-PARAMETRIC APPROACH FOR MULTIVARIATE SEISMIC FRAGILITY AND RISK ANALYSIS
Bibliographic record
Abstract
In most proposed methodologies for performance-based seismic design and evaluation, probabilistic seismic demand modelling and fragility analysis are key elements in handling uncertainty. This work puts forward a non-parametric method for constructing analytical seismic fragility functions and the corresponding risk estimates in the context of performance-based earthquake engineering. The approach leverages the thoroughness of multiple-stripe analysis and the versatility of bootstrap sampling to calculate mean risk estimates. Moreover, confidence intervals can also be quantified in each stage of the performance assessment. The proposed technique is free of constraining hypotheses such as lognormality, homoscedasticity, and linear dependence of seismic response. These are commonly employed in constructing multivariate probabilistic seismic demand models and may introduce significant bias into fragility estimates. Although of simple application, the method is shown to be systematic in approximating the seismic risk measures. The case study of a three-span concrete girder bridge in eastern Canada illustrates its application to a multicomponent structure. The interaction of reinforced concrete piers, abutment, and elastomeric bearings and their contribution to the whole bridge performance is comprised in this example. Ground motion record sets are rigorously selected using the generalised conditional intensity measure approach based on the available ground motion models for the region. Component- and system-level fragility curves and associated mean annual frequencies of damage state exceedance are used to characterise the bridge’s seismic performance. Finally, the results of the case study highlight the challenge of assessing the vulnerability of structures in regions of moderate seismicity.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".