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Record W4395090399 · doi:10.32920/25413625

Unified Approach for Liveload Moments and Deflections in Orthotropic Bridge Deck Due to CHBDC Truckloading

2024· preprint· en· W4395090399 on OpenAlexaffabout
Ahmed Hamed

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDeckStructural engineeringOrthotropic materialGirderBending momentFlexural rigidityDeflection (physics)Bridge deckTransverse planeStructural rigidityBridge (graph theory)EngineeringMoment (physics)Flexural strengthFinite element methodPhysics

Abstract

fetched live from OpenAlex

The current Canadian Highway Bridge Design Code includes design provisions to establish live load moment demands for reinforced concrete decks over longitudinal girders, orthotropic deck over longitudinal girders, and orthotropic deck over transverse beams. However, it only provides an equation for the factored applied moment on the deck as a function of the girder spacing, neglecting the effect of flexural and torsional rigidities of the deck. As a result, a parametric study was carried out in order to produce new empirical expressions for moment in bridge decks subjectedto truck wheel loading, takinginto account each of the three cases oforthotropy, namely: relatively torsionally stiff, flexurally soft decks, relatively uniformly thickness decks, and relatively torsionally soft, flexurally stiff decks. Bridge deck design can be treated in a unified manner across different deck types using the proposed formulations, accounting for longitudinal and transverse flexural rigidity of decks. The use of these methods can significantly simplify deck analysis and allow bridge engineers to compare 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 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 categoriesMeta-epidemiology (narrow)
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 score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.044
GPT teacher head0.299
Teacher spread0.255 · 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.

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
Published2024
Admission routes2
Has abstractyes

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