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Record W4249678008 · doi:10.24124/2021/59191

High-capacity hyperelastic hold-down for cross-laminated timber shear walls

2021· dissertation· en· W4249678008 on OpenAlexaffabout
Abubakar Oyawoye

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsCross laminated timberStiffnessNatural rubberStructural engineeringHyperelastic materialMaterials scienceShear (geology)Dissipative systemComposite materialFailure mode and effects analysisEngineeringFinite element methodPhysics

Abstract

fetched live from OpenAlex

Cross-laminated timber (CLT) continues to establish a stronger footing in the Canadian construction industry, also as an option for lateral load resisting systems, such as shear walls. Recent modifications to the Canadian Standard for Engineering Design in Wood (CSA O86- 19) allow only rocking kinematics as energy dissipative mechanics for CLT shear walls, whereby hold-down must remain elastic. These provisions necessitate the development of novel hold-down solutions. In this report, the performance of a hyper-elastic high-capacity hold-down was investigated at the component level through tests on: (1) hold-down steel rod, (2) CLT housing, and (3) hold-down assemblies with different sizes of rubber pads. The tests demonstrated that: i) the rubber hold-down can remain elastic under a rocking kinematics provided that the elastic limit of the steel rod is not exceeded; ii) failure of the rod is the subsequent desired ductile mode; iii) the CLT width influences the failure mode; iv) the shape factor influences the achievable deformation of the rubber pad; v) increasing the rubber pad thickness reduces the hold-down stiffness; and vi) increasing the rubber pad width increases the hold-down stiffness. Numerical modelling and optimization suggested that using an intermediate steel laminate between layers of rubber pads could improve its performance. Based on the results of the investigations presented herein, a capacity-design procedure for the hyper-elastic hold-downs was proposed.

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)0.001
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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Same topicWood Treatment and PropertiesFrench-language works237,207