MétaCan
Menu
Back to cohort
Record W1812521739 · doi:10.1139/cjce-2012-0404

Seismic assessment and upgrade of Type 2 construction steel moment-resisting frames built in Canada between the 1960s and 1980s using passive supplemental damping

2013· article· en· W1812521739 on OpenAlexafffundvenueabout
Nikolas Kyriakopoulos, Constantin Christopoulos

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaPolytechnique Montréal
KeywordsStructural engineeringDuctility (Earth science)Moment (physics)Seismic analysisEngineeringEarthquake resistant structuresFrame (networking)Seismic loadingMaterials scienceMechanical engineeringPhysicsCreep

Abstract

fetched live from OpenAlex

The seismic performance of a typical 1960s Type 2 construction steel moment-resisting frame hospital structure designed only for lateral wind loads was investigated. The structure was found to have a soft first storey and displayed large P–Δ sensitivities. An experimental program determined that the connections had considerable inherent ductility and were stable up to 2.0% interstorey drift, despite not having been designed for a ductile cyclic response. The structure was numerically modelled using advanced strength degradation considerations. A nonlinear time-history analysis was conducted using Montreal and Vancouver ground motions and the structure’s performance was found to be inadequate under the considered design hazard levels. Retrofits were proposed for the two orthogonal frames using a performance-based approach and supplemental damping, rather than local interventions to increase the ductility of these connections, and the performance of the final retrofit designs were investigated numerically to confirm that the desired performance levels were achieved.

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)0.000
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 score0.236
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.207
Teacher spread0.198 · 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

Citations4
Published2013
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicSeismic Performance and AnalysisFrench-language works237,207