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Record W4386749902 · doi:10.3390/engproc2023043019

Experimental Characterization of the Resistance of Tubular Aluminum Sections

2023· article· en· W4386749902 on OpenAlexafffundabout
Sahar Dahboul, Liya Li, Prachi Verma, Pampa Dey, Nicolas Boissonnade

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversité de SherbrookeUniversité Laval
FundersFonds de recherche du Québec – Nature et technologies
KeywordsStub (electronics)Materials scienceStructural engineeringDurabilityUltimate tensile strengthAluminiumComposite materialCorrosionBucklingBridging (networking)EngineeringComputer science

Abstract

fetched live from OpenAlex

Aluminum has a bright future as a structural material due to its excellent corrosion resistance, durability, lightweight, and complete recyclability. However, it is necessary to fully understand its mechanical behaviour under various loading conditions to make it competitive with other materials, such as concrete or steel, for civil engineering applications, especially as primary load-bearing structural members. To develop an in-depth knowledge on the behaviour of extruded aluminum sections of various shapes, an extensive experimental study was undertaken with specific emphasis on analyzing the buckling response of rectangular and square hollow sections (RHS and SHS) with 6061-T6 aluminum alloy under compressive loads. In this regard, six stub column tests were performed under axial compression, while eleven short beam-column tests under eccentric compression are currently in progress. Additionally, six tensile coupon tests were performed to obtain the full material stress–strain curve, and initial geometrical imperfections were measured mechanically and using a 3D scanner. Finally, the results of the stub column tests were compared to the resistances calculated using the Canadian standard CSA S157, which were generally conservative compared to the experimental observations.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.129

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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 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

Citations1
Published2023
Admission routes3
Has abstractyes

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