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Record W2203305600 · doi:10.1139/cjce-2014-0492

Seismic fragility of steel moment-resisting frames in Vancouver and Montreal designed in the 1960s, 1980s, and 2010

2015· article· en· W2203305600 on OpenAlexafffundvenueabout
Lucía Valentina Díaz Gómez, Oh‐Sung Kwon, Mohammad Reza Dabirvaziri

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoGovernment of OntarioCompute Canada
KeywordsOpenSeesFragilityStructural engineeringStiffnessMoment (physics)Incremental Dynamic AnalysisColumn (typography)Nonlinear systemEngineeringSeismic analysisConnection (principal bundle)Reinforced concrete

Abstract

fetched live from OpenAlex

Typical steel moment-resisting frames (MRF) of six-storey buildings in Vancouver and Montreal were designed for three different provisions of the National Building Code of Canada (1960s, 1980s, and 2010). Numerical models were developed in OpenSees to understand the seismic performance of the structures. These models accounted for strength and stiffness degradation through appropriate representations of the beam–column connection behaviours, which were calibrated against experimental results available in the literature. The behaviour of the buildings was evaluated through pushover and nonlinear time history analyses. The pushover analysis results showed that the 1960s and 2010 steel MRFs of both cities exhibited strong-column-weak-beam failure mode. The 1980s steel MRFs of both cities showed soft-storey mechanism. Fragility curves were developed for the steel MRFs based on the seismic demands evaluated using nonlinear time history analyses, which can be used for regional seismic impact assessment studies in the future.

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)0.001
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 score0.228
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.009
GPT teacher head0.179
Teacher spread0.171 · 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

Citations9
Published2015
Admission routes4
Has abstractyes

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