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Record W2469729989 · doi:10.12943/cnr.2015.00060

DEVELOPMENT OF FINITE ELEMENT MODELS FOR THE STUDY OF AGEING EFFECTS IN CANDU 6 CONCRETE CONTAINMENT BUILDINGS

2016· article· en· W2469729989 on OpenAlexaffvenue
Yuqing Ding, Shahzma Jaffer

Bibliographic record

VenueCNL Nuclear Review · 2016
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsContainment (computer programming)Finite element methodStructural integrityStructural engineeringAgeingNuclear powerNuclear engineeringComputer scienceEngineeringForensic engineeringEnvironmental sciencePhysics

Abstract

fetched live from OpenAlex

In nuclear power plants (NPPs), concrete containment buildings (CCBs) provide the final physical barrier against the release of radioactive materials into the environment and protect the nuclear structures housed within the containment building. CCBs have to be maintained to ensure leak tightness and sound structural integrity for the safe operation throughout the life of NPPs. However, the integrity of CCBs may be affected by the ageing of its concrete, post-tensioning cables and reinforcing bars (rebars). Finite element models (FEMs) of CANDU 6 CCBs have been developed using 2 independent finite element programs for the study of the effect of ageing of CCBs. These FEMs have been validated using multiple-source data and have been used for preliminary analyses of the effect of thermal load and ageing degradation on the concrete structure. The modelling assumptions and simplifications, approach, and validation are discussed in this paper. The preliminary analyses for temperature effects and potential applications to the study of ageing degradation in CCBs using the FEMs are briefly introduced.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.251
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2016
Admission routes2
Has abstractyes

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Same venueCNL Nuclear ReviewSame topicConcrete Corrosion and DurabilityFrench-language works237,207