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Seismic Design Strength of Cold-Formed Steel-Framed Shear Walls

2010· article· en· W2114368694 on OpenAlex
Reynaud Serrette

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Structural Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsnot available
FundersMcGill University
KeywordsShear wallStructural engineeringCold-formed steelShear strength (soil)Seismic analysisGeotechnical engineeringShear (geology)Bearing capacityFrame (networking)Displacement (psychology)EngineeringGeologyMaterials scienceComposite materialFinite element methodMechanical engineering

Abstract

fetched live from OpenAlex

A method for estimating the available strength level (load and resistance factor design) seismic resistance of light-frame cold-formed steel shear walls is discussed. The proposed method attempts to account for the early onset of inelastic behavior in light-frame shear walls by evaluating the strength level resistance in terms of an equivalent shear wall yield strength. Application of the proposed method is illustrated using shear wall data from an independent test program and wall performance is compared to current light-frame bearing wall design requirements and expected system performance. It is shown that the proposed method results in design values that are generally conservative compared to current design recommendations and the overall performance of the tested walls is characterized using five parameters that relate common design strength and displacement quantities.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.200
Teacher spread0.194 · 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