MétaCan
Menu
Back to cohort
Record W1987411720 · doi:10.3141/2313-06

Discrete Shear Connection for a Portable Composite Bridge

2012· article· en· W1987411720 on OpenAlexaff
Matthew Bowser, Scott Walbridge, Jeffrey West

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2012
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsUniversity of WaterlooVictorian Order of Nurses
Fundersnot available
KeywordsStructural engineeringGirderFlangeDeckComposite numberPrecast concreteComposite videoStiffnessShear (geology)BucklingFinite element methodParametric statisticsComposite constructionEngineeringMaterials scienceComposite materialMathematics

Abstract

fetched live from OpenAlex

To enable a portable composite bridge, a shear connection between steel plate girders and precast concrete deck panels, which would allow these two components to be fastened and unfastened, proposed. In the system, points of shear connection would be spaced at 3 m on center along the length of the girder. Finite element analysis was employed to compare the performance of the proposed composite system to a conventional composite girder with ductile shear studs. A nonlinear analysis was performed, and the proposed composite system was seen to demonstrate a response comparable to that of a composite girder with conventional shear studs. The model was verified for its capability to capture the possible effects of flange buckling, web buckling, and lateral torsional buckling of the steel plate girder. It was then confirmed that these failure modes did not influence the performance of the proposed portable composite bridge system for the investigated bridge configuration. A parametric study also was performed: the effects of shear connection stiffness and spacing on the behavior of the composite girder were investigated. In general, the investigated variations of these parameters had a significant influence on the girder stiffness but only a limited impact on the ultimate strength.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.078
GPT teacher head0.367
Teacher spread0.289 · 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 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

Citations3
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueTransportation Research Record Journal of the Transportation Research BoardSame topicStructural Load-Bearing AnalysisFrench-language works237,207