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Record W1797263115 · doi:10.1139/cjce-2013-0021

Analytical study on seismic force modification factors for cross-laminated timber buildings

2013· article· en· W1797263115 on OpenAlexaffvenueabout
Shiling Pei, Marjan Popovski, John W. van de Lindt

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsFPInnovations
Fundersnot available
KeywordsCross laminated timberStructural engineeringSeismic analysisBuilding codeApartmentEngineeringRange (aeronautics)Seismic loadingShear wallCivil engineering

Abstract

fetched live from OpenAlex

With two producers in operation and over 20 buildings already constructed or in planning process, use of cross-laminated timber (CLT) is gaining popularity in Canada. Since CLT as a structural system is currently not included in the National Building Code of Canada (NBCC), one of the most important issues are the values for the force modification factors for seismic design of CLT structures when NBCC equivalent static force procedure is used. In this study, a test-calibrated numerical model for CLT shear walls was applied to develop the design resistances for typical CLT wall configurations. An estimation of a possible range of Rd-factors was obtained by developing design variations for three multi-storey CLT apartment buildings. By specifying the desired seismic performance in terms of inter-storey drift, it is concluded that an Rd-factor of 2.0 will likely provide desirable building performance during the design earthquake level event in Vancouver, B.C.

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)0.002
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.021
GPT teacher head0.226
Teacher spread0.205 · 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

Citations56
Published2013
Admission routes3
Has abstractyes

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