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Record W1981525097 · doi:10.1139/l08-122

Seismic design and performance evaluation of steel-frame buildings designed using the 2005 <i>National building code of Canada</i>

2009· article· en· W1981525097 on OpenAlexafffundvenueabout
Md Yousuf, Ashutosh Bagchi

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsConcordia University
FundersConcordia University
KeywordsInfillBuilding codeSeismic analysisCode (set theory)Seismic hazardStructural engineeringGround motionFrame (networking)Work (physics)EngineeringCivil engineeringComputer science

Abstract

fetched live from OpenAlex

The seismic design provisions in the current edition of the National building code of Canada significantly differ from those in the earlier version of the code . Although the code has moved from the earlier prescriptive provisions towards a more performance-based design, such design principles have not yet been fully implemented. In the present work, four ductile steel moment-resisting frame buildings with heights of 5, 10, 15, and 20 storeys, were designed for Vancouver using the newer code, and their performances were studied to determine the level of seismic protection implied in the code. Synthesized and scaled real ground motion records were used to evaluate the nonlinear dynamic response of these structures. Although the buildings achieved the collapse prevention performance objective of the code, variations in evaluated performance parameters for the different buildings were observed. Also, building performance was found to be affected by the presence of infill walls, as well as by the nature of selected ground motion records and the methods used to scale them to Vancouver uniform hazard spectra.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
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 head0.016
GPT teacher head0.212
Teacher spread0.195 · 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

Citations12
Published2009
Admission routes4
Has abstractyes

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