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Record W4386838595 · doi:10.3390/engproc2023043024

Tests on Aluminum Sections with Simple and Complex Geometries

2023· article· en· W4386838595 on OpenAlexafffund
Prachi Verma, Sahar Dahboul, Liya Li, Pampa Dey, Nicolas Boissonnade

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologies
KeywordsMaterials scienceAluminiumBucklingStub (electronics)Structural engineeringBendingUltimate tensile strengthCompression (physics)CorrosionComposite materialAlloyEngineering

Abstract

fetched live from OpenAlex

Aluminum appears as a promising structural material as it shows many benefits such as a great strength to weight ratio, low maintenance costs, resistance to corrosion, recyclability, etc. Accordingly, characterizing the behavior and resistance of different aluminum sections under various loading conditions is essential. The current paper presents an experimental investigation on aluminum sections with simple and complex geometries to study the effect of cross-section shape on the buckling response of the aluminum sections. Firstly, eight tensile coupon tests were conducted on coupons of aluminum alloy 6061-T6 to accurately determine its material properties. Geometrical imperfections on the surface of each specimen were then measured using a 3D scanner. Further, eight stub column tests were also performed to study the behavior of I, H and complex cross-sections under pure compression. In addition to this, 12 cross-section tests under combined compression and bending are currently under way to study the buckling behavior of these specimens under eccentric compression.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.230
Teacher spread0.210 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes2
Has abstractyes

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