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Record W3129147422 · doi:10.2749/vancouver.2017.1344

Column Removal Analysis of Bare Steel Gravity Frames Using Connection Behaviour from Physical Tests

2017· article· en· W3129147422 on OpenAlexaff
Steven A. Oosterhof, Robert G. Driver

Bibliographic record

VenueReport · 2017
Typearticle
Languageen
FieldEngineering
TopicStructural Response to Dynamic Loads
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRobustness (evolution)Structural engineeringFraming (construction)Progressive collapseColumn (typography)Connection (principal bundle)Shear (geology)Computer scienceEngineeringMaterials scienceReinforced concreteComposite materialChemistry

Abstract

fetched live from OpenAlex

This paper investigates the dynamic response of bare steel framing systems with commonly-used shear connections under several column removal scenarios. The analysis follows the Imperial College London method for progressive collapse assessment, which provides a simplified approach that accounts for the dynamic effects associated with instantaneous column removal and a practical framework for assessing the collapse resistance of a structure. Load–deformation relationships and failure limits for beam-to-column connections under combined moment, shear, and tension used for this study are taken directly from physical test data, providing realistic connection behaviour for the prediction and assessment of dynamic response. The robustness of various shear connections is quantified and compared, and connection parameters that significantly affect performance under dynamic loading are discussed.

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 score0.003
Threshold uncertainty score0.007

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 science0.0010.000
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 head0.021
GPT teacher head0.304
Teacher spread0.283 · 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
Published2017
Admission routes1
Has abstractyes

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