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Record W2885409191 · doi:10.1002/eqe.3029

Method for evaluation of concrete containment structure subjected to earthquake excitation and internal pressure increase

2018· article· en· W2885409191 on OpenAlexafffund
Xu Huang, Oh‐Sung Kwon, Evan C. Bentz, Julia Tcherner

Bibliographic record

VenueEarthquake Engineering & Structural Dynamics · 2018
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsSNC-Lavalin (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCANDU Owners Group
KeywordsContainment (computer programming)Structural engineeringFinite element methodCreepNonlinear systemEngineeringSeismic analysisNuclear power plantStress (linguistics)Geotechnical engineeringGeologyMaterials scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

Summary This study evaluates capabilities of the VecTor4 computer program to assess the seismic performance of a concrete containment structure subjected to both design‐basis and beyond design‐basis earthquakes. Based on a detailed nonlinear finite element model of the nuclear power plant containment structure, the seismic performance in terms of stress and strain distributions, cracks, yielding of reinforcement bars and tendons, as well as overall failure mechanism, is thoroughly reviewed. In addition, the post‐seismic performance of the containment structure subjected to a subsequent pressure increase is also investigated. Consideration is also given to the time‐dependent parameters of creep, shrinkage, and relaxation of prestressing tendons. It is found that the time‐dependent parameters and earthquake have a non‐negligible impact on pressure‐induced structural performance but that this impact can be predicted for a given earthquake. The proposed method is useful for assessing the progressive failure behaviour of containment structure under chain events.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0080.001

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 head0.006
GPT teacher head0.247
Teacher spread0.241 · 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
GenreMethods

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

Citations14
Published2018
Admission routes2
Has abstractyes

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