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Record W4252948741 · doi:10.32920/ryerson.14655291

Study of Curvature Effects in Composite Steel I-Girder Bridges Using the V-Load Method

2021· preprint· en· W4252948741 on OpenAlexaff
Mohammadreza Davoodi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCurvatureStructural engineeringTorsion (gastropod)Finite element methodGirderBridge (graph theory)Box girderEngineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

Horizontally curved composite I-girder bridges are being increasingly used for highway interchanges and river crossings. The V-load method is widely used as a simplified method for the analysis of horizontally curved I-girder highway bridges as a straight I-girder considering the effect of torsion due to curvature. Recently, North American bridge design codes and specifications have specified certain limitations to treat horizontally curved bridges as straight ones in structural analysis and design. The purpose of this study is to investigate the applicability of those specified limitations by the V-Load method, to compare the results from the V-Load method with those obtained from the finite element analysis and to develop empirical expressions for curvature limitation. The results of this study shows that the North American codes and specifications underestimate the response with their specified curvature limitations. Based on this study, a modified equation for the curvature limitation is proposed.

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)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.014
GPT teacher head0.273
Teacher spread0.258 · 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 routes1
Has abstractyes

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