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Evaluation of Residual and Actual Stress Levels of Steel Columns in Existing Building Structures due to Working Loads

2006· article· en· W1977076469 on OpenAlex
Hyo Seon Park, W.H. Lee, Ki-Ho Yun, Hong C. Rhim

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKey engineering materials · 2006
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsAlpha Technologies (Canada)
Fundersnot available
KeywordsResidual stressStructural engineeringResidualFinite element methodColumn (typography)Stress (linguistics)Measure (data warehouse)Materials scienceEngineeringComputer scienceComposite materialAlgorithm

Abstract

fetched live from OpenAlex

A wide variety of residual stress measurement methods can be used to measure the axial stress in a column. One of the most widely used techniques for measuring residual stress is slotting method since it is relatively simple and cost-effective to implement at vertical columns in field. However, the slotting method is considered to be semi-destructive or destructive method depending on the amount of material to be removed. Therefore, in this paper, optimal depth of slots for measuring actual stress is presented to minimize the amount of material to be removed. Finite element method is used to estimate the minimum depth of the slot in H-shaped steel column. By performing actual saw cutting, optimality of the simulated depth of the slot is investigated.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.401
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.030
GPT teacher head0.256
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