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Record W4380450714 · doi:10.52202/069179-0297

SEISMIC RESPONSE OF BALLOON TYPE CLT SHEAR WALLS

2023· article· en· W4380450714 on OpenAlexafffundabout
Zhiyong Chen, Marjan Popovski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsFPInnovations
FundersU.S. Forest ServiceFPInnovationsCanadian Forest ServiceGovernment of Canada
KeywordsShear wallBalloonShear (geology)Structural engineeringGeologyDeflection (physics)Earthquake resistanceStructural integrityGeotechnical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Balloon-type mass timber shear walls are one of the most efficient structural systems to resist lateral loads induced by earthquakes or high winds.This system, however, is not included in the 2020 National Building Code of Canada and has no design guidelines in the 2019 Canadian Standard for Engineering Design in Wood, so it is out of reach of most designers.A multi-year research project has been initiated at FPInnovations to quantify the system performance and develop the necessary technical information to codify balloon-type mass timber shear walls.This paper presents the initial results of a study on the seismic response of balloon-type CLT shear walls.A mechanics-based analytical model was updated to predict the CLT panel resistance, in addition to predicting the deflection and resistance of balloon type CLT shear walls.The influence of specific key parameters such as wall length and thickness, aspect ratio, vertical loads, and vertical joints, on the structural performance of this wall system under lateral loads was investigated using the updated model.The results of this study will give a valuable insight into the seismic performance of balloon type CLT shear walls.

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 score0.005

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 science0.0000.000
Research integrity0.0010.000
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 head0.021
GPT teacher head0.218
Teacher spread0.198 · 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

Citations0
Published2023
Admission routes3
Has abstractyes

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Same topicWood Treatment and PropertiesFrench-language works237,207