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Record W4389986599 · doi:10.1139/cjce-2023-0284

Ductility-related seismic modification factor for CLT shear-wall and glulam moment-resisting frame dual system

2023· article· en· W4389986599 on OpenAlexaffvenueabout
Biniam Tekle Teweldebrhan, Solomon Tesfamariam

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of WaterlooOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsStructural engineeringDuctility (Earth science)Shear wallMoment (physics)Shear (geology)Frame (networking)Seismic analysisGeotechnical engineeringSeismic loadingGeologyStructural systemMaterials scienceEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

The cross-laminated timber (CLT) shear-wall and glulam moment-resisting frame (CLTW–GMRF) dual system is a recently completed research prepared for the British Columbia (BC) Forestry Innovation Investment Ltd. With the introduction of new structural systems, the need to update existing building code becomes evident. Accordingly, this study evaluates the ductility-related force modification factor ( R d ) of the CLTW–GMRF system for the National Building Code of Canada, utilizing the FEMA P-695 procedure. In two performance groups, 16 archetype buildings are designed considering different building storey heights, CLT shear-wall locations, and wall-frame moment proportions. Numerical model of the systems is developed in OpenSees and incremental dynamic analyses are conducted using 30 bi-directional ground motion records that represent the seismicity of Vancouver, BC, Canada. Collapse margin ratios are calculated to assess the adequacy of the trial R d factors. The research determined that with an over-strength factor of 1.5, an R d of 3 is found to be acceptable for the system.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.027
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.013
GPT teacher head0.200
Teacher spread0.186 · 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 teacher head, 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

Citations7
Published2023
Admission routes3
Has abstractyes

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