MétaCan
Menu
← Back to cohort
Record W4250303603 · doi:10.32920/ryerson.14656971.v1

Unified approach for bridge deck design

2021· preprint· en· W4250303603 on OpenAlexaffabout
Karim Meleka

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDeckStructural engineeringOrthotropic materialGirderBending momentBridge deckFlexural rigidityBridge (graph theory)Deflection (physics)Structural rigidityRigidity (electromagnetism)EngineeringFinite element methodPhysics

Abstract

fetched live from OpenAlex

Current Canadian Highway Bridge Design Code includes design provisions to establish live load demands in (i) reinforced concrete decks over longitudinal girders, (ii) orthotropic deck over longitudinal girders, and (iii) orthotropic deck over transverse beams. However, it only provides an equation for factored applied moment on concrete deck under single point load. Similar equations for orthotropic decks are as yet unavailable. As such, parametric study was conducted to lead to new empirical expressions for moment in bridge decks subjected to truck wheel loading considering each of the three cases of orthotropy: (i) relatively torsionally stiff, flexurally soft decks; (ii) relatively uniformly thick decks; and (iii) relatively torsionally soft, flexurally stiff decks. Using the proposed formulations, bridge deck design can be treated in a unified way across different deck types, accounting for longitudinal-transverse flexural rigidity of decks. Application of these methods can significantly simplify the analysis of decks and allow bridge engineers to make comparisons across different deck design alternatives.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.061
GPT teacher head0.260
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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicStructural Behavior of Reinforced Concrete→French-language works237,207→