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Record W3117154528 · doi:10.1177/0954409720980881

The permissible wheel load, wheel radius, and speed on a railroad

2020· article· en· W3117154528 on OpenAlexaff
Nazmul Hasan

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit · 2020
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsTreadStructural engineeringShear stressFatigue limitRADIUSEngineeringStress (linguistics)Shear (geology)Materials scienceMechanicsComputer sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

The paper aims to determine the permissible wheel load, wheel radius, and speed on a railroad considering fatigue shear stress in the rail head. In the literature, there are permissible wheel load and wheel radius formulae which consider shear fatigue limit as permissible shear stress; and hence do not offer acceptable wheel load, radius and speed. The deficiency lies in the permissible shear fatigue stress value. The permissible shear fatigue stress is suggested to be 24.4% of the tensile strength of rail steel with an assumed reliability of 99%. Addressing the deficiency, formulae are suggested for permissible wheel load, wheel radius and speed under three approaches. The formulae are generalised too considering the coefficient of friction at the wheel tread/rail interface. The permissible speed is suggested to be the minimum of two speeds based on the permissible shear fatigue stress at the wheel tread/rail interface and bending fatigue stress at the rail foot. A bending fatigue stress corresponding to a reliability of 95% is suggested for heavy haul because it makes a close balance between the two aforementioned speeds.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.184
Teacher spread0.174 · 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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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Same venueProceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid TransitSame topicRailway Engineering and DynamicsFrench-language works237,207