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Record W4211193891 · doi:10.3390/buildings12020202

Contemporary and Novel Hold-Down Solutions for Mass Timber Shear Walls

2022· article· en· W4211193891 on OpenAlexaff
Thomas Tannert, Cristiano Loss

Bibliographic record

VenueBuildings · 2022
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of British ColumbiaUniversity of Northern British Columbia
Fundersnot available
KeywordsStructural engineeringCross laminated timberShear wallStiffnessShear (geology)Compatibility (geochemistry)Deflection (physics)EngineeringMaterials scienceGeotechnical engineeringComposite material

Abstract

fetched live from OpenAlex

‘Mass timber’ engineered wood products in general, and cross-laminated timber in particular, are gaining popularity in residential, non-residential, as well as mid- and high-rise structural applications. These applications include lateral force-resisting systems, such as shear walls. The prospect of building larger and taller timber buildings creates structural design challenges; one of them being that lateral forces from wind and earthquakes are larger and create higher demands on the ‘hold-downs’ in shear wall buildings. These demands are multiple: strength to resist loads, lateral stiffness to minimize deflections and damage, as well as deformation compatibility to accommodate the desired system rocking behaviour during an earthquake. In this paper, contemporary and novel hold-down solutions for mass timber shear walls are presented and discussed, including recent research on internal-perforated steel plates fastened with self-drilling dowels, hyperelastic rubber pads with steel rods, and high-strength hold-downs with self-tapping screws.

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)0.000
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.038
GPT teacher head0.212
Teacher spread0.174 · 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

Citations33
Published2022
Admission routes1
Has abstractyes

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