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Record W2954878952 · doi:10.29173/mocs96

Lateral Performance of Cross-laminated Timber Shear Walls: Analytical and Numerical Investigations

2019· article· en· W2954878952 on OpenAlexaffvenue
Md Shahnewaz, Thomas Tannert, Marjan Popovski

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2019
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsFPInnovationsUniversity of Northern British Columbia
Fundersnot available
KeywordsCross laminated timberShear wallStructural engineeringParametric statisticsShear (geology)Finite element methodKinematicsEngineeringGeotechnical engineeringGeologyMathematicsPhysics

Abstract

fetched live from OpenAlex

Cross-laminated timber (CLT) is becoming a viable option for mid-rise buildings in North America. CLT walls are very effective in resisting lateral forces resulting from wind and seismic loads, yet no standard provisions are available to estimate the resistance of CLT shear walls under lateral loading. The present research investigated CLT shear wall’s performance by evaluating the preferred kinematic rocking behaviour. An analytical procedure was proposed to estimate the resistance of CLT shear walls in a platform type construction. Finite element models of CLT shear with various brackets and hold-downs connections were developed. The models were validated against experimental results. Furthermore, a parametric study on CLT shear walls with the variation of type and number of connectors was conducted. The resistance estimated from parametric study and against analytical were compared. The proposed formulas can be useful tool for the design of CLT platform-type buildings, however, require further experimental validation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.201
Teacher spread0.192 · 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 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
Published2019
Admission routes2
Has abstractyes

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