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Record W4240321110 · doi:10.24124/2020/59085

Numerical study on the seismic performance of hybrid steel moment frames with CLT balloon shear walls

2020· dissertation· en· W4240321110 on OpenAlexafffund
Mehdi Khajehpour

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of Northern British Columbia
FundersDivision of Graduate EducationUniversity of Northern British Columbia
KeywordsOpenSeesStructural engineeringShear wallMoment (physics)StiffnessShear (geology)Geotechnical engineeringGeologyNonlinear systemDuctility (Earth science)EngineeringMaterials scienceReinforced concreteComposite materialPhysics

Abstract

fetched live from OpenAlex

A proposed hybrid lateral load resisting system combining a moderately ductile steel moment resisting frame (SMRF) with Cross-laminated Timber (CLT) balloon-framed shear walls is investigated on 8, 12 and 16-storey case-study buildings using equivalent static, linear dynamic (modal), nonlinear static (push-over) and nonlinear dynamic (time history) analyses. First, a SMRF is designed using ETABS, then the hybrid structures are analysed in OpenSees. By adding the CLT shear wall to steel moment frame, the period of structure decreased and its stiffness increased. The time history analyses result revealed that by adding the CLT shear wall the maximum drift decreased, while the maximum base shear in hybrid structure slightly increased. The hold down uplift forces under earthquake records are reported and compared to each other. Using push-over capacity-curves, a ductility reduction factor of 3.6, an over strength factor of 1.57 and a seismic response modification factor of 5.67 are derived.

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)0.000
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
Metaresearch0.0000.000
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 integrity0.0010.000
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 head0.009
GPT teacher head0.214
Teacher spread0.206 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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Same topicSeismic Performance and AnalysisFrench-language works237,207