Determining seismic safety margins by nonlinear soil-structure analysis
Bibliographic record
Abstract
The nuclear power plants (NPP) in Switzerland have to review their plant safety against the updated seismic hazard (uniform hazard Spectra, from 10 -3 /a to 10 -7 /a-denoted as ENSI-2015)-by performing both deterministic and probabilistic safety analyses.For this purpose, a method for direct estimation of seismic safety margins based on nonlinear soil-structure analysis was developed and applied for systems and components located inside the reactor building of NPP goesgen.The method is based on the evaluation of safety factors derived from scaling of response spectra for different hazard levels.For this purpose, two sets of deterministic in-structure floor response spectra (ISrS) were developed taking into account soil-structure interaction (SSI).The first set consists of the new linear elastic ISrS for the reference review level Earthquake (uhS, frequency of exceedance of 10 -4 /a, PgA=0.41g(mean value), called NESK3 in Swiss national regulations).The second one consists of the calculated nonlinear ISrS computed for the Safety margin review level Earthquake (uhS, frequency of exceedance of 10 -5 /a, PgA=0.71g(mean value), SmrlE).For obtaining the ISrS an equivalent linear-elastic 3D finite element model of the reactor building and the associated soil column (Soil structure analysis (SSI) in frequency domain, SASSI (ElFD), for NESK3) as well as a nonlinear model (SSI in time-domain using lSDyNA, for the SmrlE (NlTD)) were developed.Soil nonlinearity for the latter was incorporated through a hysteretic plasticity model whose shear response is dependent on soil effective pressure.To calibrate the plasticity model, gravel's shear-stiffness degradation-curve was modified to produce shear strength values consistent with the laboratory-measured friction angle.It is demonstrated that the direct estimation of safety margins by nonlinear soil-structure analysis yields more realistic results than extrapolations common to standard fragility analysis methods.
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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 | 0.000 | 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 | 0.001 |
| 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".