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Record W2019177501 · doi:10.1139/l06-061

Damage assessment of steel angle members subjected to very-low-cycle loading

2006· article· en· W2019177501 on OpenAlexvenueno aff
Yeon-Soo Park, Sun-Joon Park, Sung-Hoo Kang, Byung‐Chul Suh

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2006
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBucklingStructural engineeringMaterials scienceCrackingFinite element methodLow-cycle fatigueDeformation (meteorology)Composite materialEngineering

Abstract

fetched live from OpenAlex

This study deals with damage processes to steel structural members up to an ultimate cracking state caused by local buckling that occurs under large deformations due to very-low-cycle loading. In this study, a very-low-cycle loading means a repetitive loading, with 5 to 20 loading cycles, within the large plastic range. Experiments were conducted on steel angle members that were subjected to very-low-cycle loading that caused global and (or) local buckling and plastic elongation. The objective of the experiments was to quantify important physical factor relationships between cracks and ruptures to large repetitive deformations. A nonlinear finite element method analysis was performed to trace the experimental behavior of steel structural members. A new approach to seismic damage assessment for steel members is proposed based on local stress-strain histories and cumulative states of deformation at critical parts.Key words: damage index, very-low-cycle loading, local strain, buckling, crack, steel member.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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 designBench or experimental
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
Published2006
Admission routes1
Has abstractyes

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