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Record W4252828905 · doi:10.24124/2020/59102

Hyperelastic hold-down for cross-laminated timber shear walls

2020· dissertation· en· W4252828905 on OpenAlexaffabout
Hosein Asgari

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsHyperelastic materialNatural rubberStructural engineeringCross laminated timberDissipative systemDeformation (meteorology)Materials scienceStiffnessElastomerShear (geology)Composite materialEngineeringFinite element methodPhysics

Abstract

fetched live from OpenAlex

Cross-laminated Timber (CLT) is increasingly being used in tall buildings. However, there are some challenges when designing high-rise CLT structures, amongst them the need for novel hold-downs (HD), for shear walls. While commonly used HDs behave as a dissipative connection, the current Canadian Standard for Engineering Design in Wood recommends designing HDs as a non-dissipative connection. As hyperelastic material, an elastomer (rubber) is capable to carry high loads without inelastic deformation. This thesis presents experimental studies at material- and component-levels using a hyperelastic rubber HD solution for CLT walls. A total of 53 quasi-static monotonic and cyclic tests were performed. The HDs exhibited high strength and deformation capacity without any residual deformation after unloading. The shape factor and loaded area of rubber layers were found as the main effective factors on the rubber HD’s response, and an empirical load-displacement relation was also developed based on these parameters.

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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.014
GPT teacher head0.240
Teacher spread0.225 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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Same topicWood Treatment and PropertiesFrench-language works237,207