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Record W4404966238 · doi:10.1680/jbren.24.00042

Curvature limits for composite steel box girder bridges

2024· article· en· W4404966238 on OpenAlexaffabout
Imad Eldin Khalafalla, Khaled Sennah

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Bridge Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsToronto Metropolitan UniversityGeorgian College
Fundersnot available
KeywordsCurvatureStructural engineeringDeflection (physics)Box girderGirderEngineeringDeckFinite element methodBending momentBridge (graph theory)GeometryMathematicsPhysics

Abstract

fetched live from OpenAlex

Composite concrete deck over steel box girder bridges are used in curved alignments as an economical solution because of their high flexural and torsional strength. Curvature effects on bending, shear and deflection may be ignored for bridges with light curvature. The Canadian highway bridge design code (CHBDC), the American Association for State Highway and Transportation Officials (Aashto) guide specifications for horizontally curved bridges and Aashto LRFD bridge design specifications specify limitations to treat a horizontally curved bridge as a straight one in structural design for only girder bending moment. Also, CHBDC curvature limitation does not differentiate between open and closed bridge cross-sections. The curvature limitation of the Aashto guide does not consider the effect of bridge width, leading to an inaccurate estimate of the structural response. To investigate the accuracy of these curvature limitations, a series of horizontally curved composite steel box girder bridges were analysed using finite-element modelling. The key parameters considered in this study were the degree of curvature and bridge span, width and continuity. Longitudinal bending stresses, support reactions, deflections and frequencies were determined and compared with those obtained for straight bridges of identical configurations. Results showed that code curvature limits were unsafe and needed to be updated. Empirical expressions were developed to determine curvature limits more accurately and reliably.

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)0.004
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Bridge EngineeringSame topicRailway Engineering and DynamicsFrench-language works237,207