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Record W2765890375

Seismic Fragility Analysis of Steel Moment-Resisting Frames (MRF) Designed in Canada in the 1960s, 1980s, and 2010

2014· dissertation· en· W2765890375 on OpenAlexaboutno aff
Valentina Lucia Diaz Gomez

Bibliographic record

VenueTSpace (University of Toronto) · 2014
Typedissertation
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOpenSeesStructural engineeringFragilityStiffnessColumn (typography)Moment (physics)Connection (principal bundle)Progressive collapseBeam (structure)Steel frameNonlinear systemEngineeringIncremental Dynamic AnalysisFrame (networking)Seismic analysisFinite element methodReinforced concreteMechanical engineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

A typical steel moment-resisting frame (MRF) of six stories was designed for three different provisions of the National Building Code of Canada (1960s, 1980s, and 2010) and for two different cities (Vancouver and Montreal). Numerical models were developed in OpenSees to understand the seismic performance of the structures. These models accounted for strength and stiffness degradation through the appropriate representation of the beam-column connection behaviour. The beam-column connection models were calibrated against experimental results available in the literature. The behaviour of the buildings was evaluated through pushover and nonlinear time history analyses. The 1960s and 2010 steel MRFs of both cities presented strong-column-weak-beam behaviour and the failure in the connections provoked the collapse of the structures. The 1980s steel MRFs of both cities showed column sway mechanism. Fragility curves were developed for the steel MRFs using nonlinear time history analyses.

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 score0.134
Threshold uncertainty score0.269

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.198
Teacher spread0.191 · 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
Published2014
Admission routes1
Has abstractyes

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Same venueTSpace (University of Toronto)Same topicSeismic Performance and AnalysisFrench-language works237,207