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Record W2157068833 · doi:10.1115/pvp2002-1391

Failure Analysis of Thinned Wall Elbows Under Excessive Seismic Loading

2002· article· en· W2157068833 on OpenAlexfundno aff
Masaki SHIRATORI, Yoji Ochi, Izumi Nakamura, Akihito Otani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologies
KeywordsStructural engineeringResidualFinite element methodSensitivity (control systems)BucklingBent molecular geometryFailure mode and effects analysisMaterials scienceThinningGeologyGeotechnical engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

A series of finite element analyses has been carried out in order to investigate the failure behaviors of degraded bent pipes with local thinning against seismic loading. The sensitivity of such parameters as the residual thickness, locations and width of the local thinning to the failure modes such as ovaling and local buckling and to the low cycle fatigue damage has been studied. It has been found that this approach is useful to make a reasonable experimental plan, which has to be carried out under the condition of limited cost and limited period.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.198
Teacher spread0.188 · 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 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

Citations3
Published2002
Admission routes1
Has abstractyes

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