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Record W4323312700 · doi:10.1139/cjce-2022-0222

Assessment of seismic acceleration demands on nonstructural components in moderately ductile RC frame buildings designed according to NBC 2015

2023· article· en· W4323312700 on OpenAlexafffundvenueabout
Reza Sheikhzadeh Shayan, Rola Assi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsÉcole de Technologie Supérieure
FundersMitacs
KeywordsAccelerationStructural engineeringAmplification factorSeismic hazardBenchmark (surveying)Building codeSeismic analysisMoment (physics)Frame (networking)Peak ground accelerationReinforced concreteDuctility (Earth science)Nonlinear systemEngineeringComputer scienceSeismologyGeologyGround motionPhysicsGeodesyTelecommunications

Abstract

fetched live from OpenAlex

This paper presents an extensive investigation of the seismic demands on acceleration-sensitive nonstructural components (NSCs) attached to reinforced concrete moment-resisting frame buildings with limited ductility and designed according to the National Building Code of Canada. A total of four benchmark structures with varying heights are considered in this study. Linear and nonlinear time-history analyses were performed using artificial ground motions compatible with Montreal site Class C uniform hazard spectra having 2% and 10% probabilities of being exceeded in 50 years. Seismic acceleration demands on NSCs were evaluated by assessing the horizontal height factor Ax, the component dynamic amplification factor Ar, and the component force factor Sp compared with various building code provisions. The overall acceleration demands of NSCs are discussed, and a corresponding optimized Sp profile is proposed to improve the estimation of the seismic force demands on NSCs at the rooftops of buildings located in moderate seismic zones.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.019
GPT teacher head0.239
Teacher spread0.220 · 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 designObservational
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

Citations1
Published2023
Admission routes4
Has abstractyes

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