MétaCan
Menu
Back to cohort
Record W1996999909 · doi:10.1243/14644193jmbd179

Finite element analysis of a Timoshenko beam traversed by a moving vehicle

2009· article· en· W1996999909 on OpenAlexaff
Mahsa Moghaddas, Ramin Sedaghati, Ebrahim Esmailzadeh, Peyman Khosravi

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part K Journal of Multi-body Dynamics · 2009
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsDalhousie UniversityOntario Tech UniversityConcordia University
Fundersnot available
KeywordsTimoshenko beam theoryDeflection (physics)Finite element methodBeam (structure)Moving loadEquations of motionStructural engineeringMathematicsPhysicsMechanicsClassical mechanicsMathematical analysisEngineering

Abstract

fetched live from OpenAlex

In this study the finite element formulation for the dynamics of a bridge traversed by moving vehicles is presented. The vehicle including the driver and the passenger is modelled as a half-car planner model with six degree of freedom, travelling on the bridge with constant velocity. The bridge is modelled as a uniform beam with simply supported end conditions that obeys the Timoshenko beam theory. The governing equations of motion are derived using the extended Hamilton principle and then transformed into the finite element format by using the weak-form formulation. The Newmark-β method is utilized to solve the governing equations and the results are compared with those reported in the literature. Furthermore, the maximum values of deflection for the Timoshenko and Euler—Bernoulli beams have been compared. The results illustrated that as the velocity of the vehicle increases, the difference between the maximum beam deflections in the two beam models becomes more significant.

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)0.000
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Mechanical Engineers Part K Journal of Multi-body DynamicsSame topicRailway Engineering and DynamicsFrench-language works237,207