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Record W2744830092 · doi:10.1177/2041419617721530

Influence of elevated strain rate on the mechanical properties of hollow structural sections

2017· article· en· W2744830092 on OpenAlexaff
Cameron B. Ritchie, Matthew I Gow, Jeffrey A. Packer, Amin Heidarpour

Bibliographic record

VenueInternational Journal of Protective Structures · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUltimate tensile strengthStrain rateMaterials scienceStructural engineeringStrain (injury)Composite materialYield (engineering)Tensile testingCold-formed steelEngineeringBuckling

Abstract

fetched live from OpenAlex

As protective design engineering becomes more prevalent, cold-formed steel hollow structural sections are often desired design components. As such, it is necessary to understand the behavior of hollow structural sections subject to air-blast loading, including the material response under elevated strain rates. Dynamic tensile tests have hence been performed on subsize tensile coupons taken from the flats and corners of cold-formed rectangular hollow section members. Dynamic yield stresses were obtained at strain rates from 0.1 to 18 s −1 , which encompasses and exceeds the range recorded during far-field blast arena testing. The dynamic increase factor was calculated for each data point and synthesized with previous cold-formed rectangular hollow section tests at even higher strain rates (100–1000 s −1 ). The data set was used to determine Cowper–Symonds and Johnson–Cook parameters. The resulting material models can now be used to determine the strength increase of cold-formed rectangular hollow sections subject to a wide range of impulsive, elevated strain rate loads.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.018
GPT teacher head0.267
Teacher spread0.250 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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