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Effects of Reference Displacement and Damage Accumulation in Wood Shear Walls

2004· article· en· W2027453070 on OpenAlexfundno aff
Jeffrey D. Langlois, Rakesh Kumar Gupta, Thomas H. Miller

Bibliographic record

VenueJournal of Structural Engineering · 2004
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsnot available
FundersFPInnovationsU.S. Department of Agriculture
KeywordsStiffnessDisplacement (psychology)Structural engineeringReduction (mathematics)Materials scienceMonotonic functionShear wallGeotechnical engineeringComposite materialGeologyMathematicsEngineeringGeometry

Abstract

fetched live from OpenAlex

The objectives of this study were: (1) to evaluate the effect of reference displacement on wall behavior under fully reversed cyclic loading using the Consortium of Universities for Research in Earthquake Engineering (CUREE) test protocol and (2) to assess damage accumulation for the imposed drift levels. Three walls were tested under monotonic loads to determine reference displacements. Four sets of tests, each consisting of two wall specimens, were conducted using the CUREE protocol with four different reference displacements. Results show that the reference displacement can influence wall strength by up to 15% while there was little or no effect on stiffness and area under the backbone curve. Three additional tests were conducted using a segmented version of the CUREE protocol to provide a way to correlate visible damage and stiffness degradation to imposed drifts. Results show that while visible damage was minimal at drifts as high as 1%, (8% of nails slightly damaged), a 52% reduction in secant stiffness had occurred.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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

Citations16
Published2004
Admission routes1
Has abstractyes

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