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Record W3217518720 · doi:10.22055/jacm.2021.38826.3290

Modeling of the dynamic rail deflection using elastic wave propagation

2021· article· en· W3217518720 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsTransport Canada
Fundersnot available
KeywordsBallastTrack (disk drive)Deflection (physics)Elasticity (physics)Computer scienceStructural engineeringEngineeringPhysicsClassical mechanics

Abstract

fetched live from OpenAlex

There is a class of tasks that requires considering the dynamics not only for rolling stock but also for the response of the railway track. One of the directions of railway transport development, which encourages the transition to fundamentally new dynamic models of the railway track, is undoubtedly an increase in traffic speed. To solve such problems, the authors applied a model of the stressed-strained state of a railway track based on the dynamic problem of elasticity theory. The feature of this model is the calculation of dynamic stresses and deformations induced by the spread of elastic waves through the objects of the railway track. Based on the mathematical modeling of stress propagation in the under-rail basis, authors have shown the influence of various objects of a railway track on the formation of the outline of the front of the elastic wave and determined the main time intervals. Furthermore, the authors propose the following analytical method, which, in addition to the soil's physical and mechanical properties, considers the properties of the ballast as a layer that transmits pressure to the roadbed and takes an active part in the formation of the interaction space.

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.

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 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 score0.474
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.160
GPT teacher head0.450
Teacher spread0.290 · 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