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Displacement-Based Design of Hybrid RC–Timber Structure: Seismic Risk Assessment

2019· article· en· W2970920834 on OpenAlexafffundabout
Solomon Tesfamariam, Jayanthan Madheswaran, Katsuichiro Goda

Bibliographic record

VenueJournal of Structural Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicMasonry and Concrete Structural Analysis
Canadian institutionsWestern UniversityUniversity of British ColumbiaOkanagan University CollegeUniversity of British Columbia, Okanagan Campus
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringStiffnessStructural systemDissipationInfillFrame (networking)Induced seismicityDisplacement (psychology)EngineeringNonlinear systemBeam (structure)Reinforced concreteDamperHybrid systemMoment (physics)Cross laminated timberComputer scienceCivil engineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

A cross-laminated timber (CLT) wall is a composite structural assembly with high strength and stiffness properties. In this study, the beneficial properties of this wall were used in designing a new CLT-reinforced concrete (RC) hybrid system building. The hybrid system consisted of an RC moment-resisting frame with CLT infill. The energy dissipation capacity of this system was further enhanced by using steel slit dampers as connectors between the CLT and RC beam. To facilitate the adoption of new technology in earthquake engineering applications, direct displacement-based design was used. The utility of the proposed system was illustrated on a six-story CLT-RC frame dual system. Performance of the proposed design method was shown by conducting nonlinear time history analyses with consideration of seismicity of Vancouver, Canada. The results indicated that the proposed CLT-RC hybrid system is capable of withstanding lateral forces due to intense seismic excitations.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.004
GPT teacher head0.193
Teacher spread0.190 · 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

Citations22
Published2019
Admission routes3
Has abstractyes

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