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Record W1715661110 · doi:10.1139/cjce-2012-0091

Effects of column stiffness irregularity on the seismic response of bridges in the longitudinal direction

2013· article· en· W1715661110 on OpenAlexafffundvenueabout
Payam Tehrani, Denis Mitchell

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStructural engineeringStiffnessSubstructureAbutmentDuctility (Earth science)Seismic analysisColumn (typography)SuperstructureGeotechnical engineeringGeologyMaterials scienceEngineeringComposite materialConnection (principal bundle)

Abstract

fetched live from OpenAlex

The longitudinal seismic responses of 4-span continuous bridges designed based on the 2006 Canadian Highway Bridge Design Code were studied using elastic response spectrum and inelastic time-history analyses. Several boundary conditions including unrestrained horizontal movements at the abutments and different abutment stiffnesses were considered in the nonlinear analyses. The seismic response of more than 2600 bridges were studied to determine the effects of different design and modelling parameters including the effects of different column heights, column diameters, and superstructure mass as well as different abutment stiffnesses. The bridges were designed using two different force modification factors of 3 and 5. The effects of column stiffness ratios on the elastic and inelastic analysis results, maximum ductility demands, concentration of ductility demands, and demand to capacity ratios were investigated. The results indicate that the seismic response and maximum ductility demands in the longitudinal direction are influenced by important parameters such as the total stiffness of the substructure, the column stiffness ratio, and the aspect ratio of the columns.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.397

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 head0.006
GPT teacher head0.176
Teacher spread0.170 · 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

Citations22
Published2013
Admission routes4
Has abstractyes

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