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Record W3214041973 · doi:10.1115/1.4053052

Deterministic and Probabilistic Evaluations of Structures and Components Credited for Seismic Design Extension Conditions

2021· article· en· W3214041973 on OpenAlexaffabout
A. Saudy, Medhat Elgohary

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsKinectrics (Canada)
Fundersnot available
KeywordsFragilityProbabilistic logicReliability engineeringPercentileSeismic analysisComponent (thermodynamics)EngineeringConfidence intervalMargin (machine learning)Computer scienceStructural engineeringStatisticsMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract There is “high confidence” in the ability of the structures, systems, and components (SSCs) of nuclear power plants (NPPs) to perform as designed for design basis accidents (DBAs). For design extension conditions (DECs), the SSCs are required to perform as designed with “reasonably high confidence.” DECs represent scenarios or accidents that are more severe than DBAs. Typically, a spectrum of initiating events including random failure of systems or components, internal and external hazards is used in defining scenarios leading to the DECs. Seismic events that exceed the design basis earthquake (DBE) of a station could be considered seismic DECs. A deterministic design method is proposed to address higher demands of seismic DECs in the new and existing Canada Deuterium Uranium (CANDU) NPPs. The deterministic method builds on the current requirements of applicable codes and standards and recommends more relaxed acceptance criteria. Nevertheless, a means to probabilistically evaluate built-in margin exceeding the demand induced by a seismic DEC would provide a measure of the confidence in a DEC-assigned structure or component performing its function. Therefore, a probabilistic method that estimates the probability of survivability for a structure or component when subjected to the demand induced by a seismic DEC is proposed. The probabilistic method could be used to indicate whether there is a need for applying design modification to existing design features to address demands of seismic DEC. The mean, 5th-percentile, and 95th-percentile fragility functions of these SSCs are used. These fragility functions are typically developed to determine the high-confidence-low-probability-of-failure (HCLPF) value associated with the contribution of a structure or component to the overall plant seismic risk. Sample cases for design features that were implemented in existing CANDU NPPs to address seismic DECs are presented. Both the deterministic and probabilistic methods are applied to cases of civil structures, passive mechanical and electrical components, as well as active control and instrumentation components.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.715
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.102
GPT teacher head0.367
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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