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Record W4380449444 · doi:10.52202/069179-0268

EXPERIMENTAL TESTING OF MIXED ANGLE SCREWED HOLD-DOWN CONNECTIONS FOR CLT SHEAR WALLS

2023· article· en· W4380449444 on OpenAlexaff
Thomas Wright, Minghao Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsShear (geology)GeologyPhysicsMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Cross laminated timber (CLT) shear walls are an efficient lateral load resisting system for mass timber buildings.Ductility and energy dissipation of timber buildings is mostly provided by well detailed connections.Therefore, to achieve good seismic performance of CLT shear walls, hold-down connections must be not only strong and stiff but also ductile with sufficient displacement capacity to meet the drift demands.A novel hold-down connection is proposed using a mixture of screws installed at an inclined angle and 90° to the grain.The respective benefits of inclined and 90° screws can be combined to create a strong, stiff, and ductile connection.Two stages of experimental testing were undertaken with target connection capacities of 600 kN and 1200 kN respectively.The experimental results confirmed that mixed angle screws can provide a strong, stiff, and ductile hold-down solution for CLT shear walls.The optimal ratio of inclined screws to 90° screws was 1:2, and the optimal ratio of 12 mm inclined screws to 12 mm 90° screws was 1:1.5.The primary failure modes were screw withdrawal and wood embedment crushing.Only localised damage of timber around the screw holes was observed and this was repaired with epoxy.New screws were then reinstated with a small offset to the original screw locations and the repaired hold-down connections were found to have the same or even slightly better performance.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.036
GPT teacher head0.261
Teacher spread0.226 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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