FRAMEWORK TO INTRODUCE SITE RESPONSE IN SEISMIC RISK ASSESSMENT AND APPLICATION TO METIS CASE STUDY
Bibliographic record
Abstract
The goal of this work is to develop a comprehensive strategy for defining ground motion and soil columns for SSI and floor response. A first step consists in the evaluation whether 1D or multidimensional (2D, 3D) site response analysis are required for the site under study. This step is not further discussed in the present work but we rely on existing strategies. The HPC strategy developed in FE solver code_aster1 allows for a fully parallel implementation for 3D and 2D site effects estimation. Then the seismic motion to be applied at the boundary of the finite element model has to be defined based on PSHA output or complementary wave propagation analysis. One major bottleneck in the numerical site response analysis is the consideration of uncertainty. Indeed, the 1D computations run very fast, however, whenever applicable (that is absence of pronounced 3D effects), such analyses require the consideration of uncertainty due to the simplifying assumptions on lateral variability and incoming wavefield. For this purpose, we develop an approach to define consistent (linear) equivalent 1D columns reflecting the observed variability and attenuation. We perform complementary 2D analysis to analyze the impact of 2D wave propagation and lateral variability. We quantify and propagate uncertainty for the case study to define a reduced set of 1D columns (and the corresponding probability distributions) that would well represent the mean and standard deviation. Both the variability of input ground motion on rock as well as the uncertainty related to site data are considered. The developed methodology is applied to the METIS case study site located in central Italy with moderate seismicity such as often encountered in regions with nuclear installations in European countries. 1Code aster, general public licensed structural mechanics finite element software, internet site: http://www.code-aster.org.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".