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Record W4223922170 · doi:10.1061/9780784484180.020

Novel Hold-Down Solutions for Cross-Laminated Timber Shear Walls

2022· article· en· W4223922170 on OpenAlexaffabout
Thomas Tannert

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsCross laminated timberStructural engineeringEngineered woodStiffnessCompatibility (geochemistry)Shear wallShear (geology)EngineeringMaterials scienceCivil engineeringComposite material

Abstract

fetched live from OpenAlex

Cross-laminated timber (CLT), an engineered wood product categorized as “mass timber,” is gaining popularity in residential and non-residential applications. The prospect of building larger timber structures creates structural challenges, amongst them being that lateral forces created by high winds and strong earthquakes are higher and create higher demands of “hold-downs.” These demands are multiple: high strength to resist loads, high stiffness to minimize deflections during wind events, as well as deformation compatibility to facilitate the desired rocking-motion of the shear walls during an earthquake. Herein, recent research on several innovative hold-down solutions will be provided: internal-perforated-steel-plates fastened with self-drilling dowels; hyperelastic rubber pads with steel rods; and solutions with self-tapping screws. All systems are capacity-protected in the non-dissipative components: strength, stiffness, and ductility is governed by the energy-dissipative shear wall components. The results from component-level and full-scale CLT shear wall tests are presented. The findings provide design guidance to practicing engineers and will inform future revisions of the Canadian Standard for Engineering Design in Wood.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.032
GPT teacher head0.256
Teacher spread0.224 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2022
Admission routes2
Has abstractyes

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