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Record W4234692379 · doi:10.32920/ryerson.14651502.v1

Investigation of Load Distribution Factors for Simply-Supported Composite Multiple Box Girder Bridges

2021· preprint· en· W4234692379 on OpenAlexaff
Siham Kadhim Jawad

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStructural engineeringGirderBox girderFinite element methodBending momentDeflection (physics)EngineeringStructural loadParametric statisticsInfluence lineShear forceShear (geology)MathematicsMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Composite box-girder bridges are recently used in modern highway urban system because of their profitable and structural aptitude advantages. North Americans Codes of Practice specify empirical equations for girder moment and shear forces in such bridges in the form of live load distribution factors. These factors were proven to be conservative in some cases and underestimate the response in other cases. Therefore, an extensive parametric study, using the finite-element modeling, was conducted to examine the key parameters that influence the load distribution factors of such bridges. A total of 276 prototype bridges were analyzed to evaluate girder bending moment, shear force and deflection distribution factors for simply-supported composite multiple box-girder bridges when subjected to CHBDC truck loading. Design parameters considered in this study were bridges span length, numbers of design lanes, number of box girders and girder spacing. Based on the data generated from parametric study, sets of simple empirical expressions were developed for bending moment; shear force and deflection distribution factors for such bridges. A correlation between the finite-element results with CHBDC and AASHTO-LRFD empirical expressions showed the former are more reliable in structural design of composite box-girder bridges.

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 score0.415
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
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.217
Teacher spread0.201 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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