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Record W4399642387 · doi:10.1061/9780784485521.055

Equivalence and Comprehensive Guidance for Vertical Curves on Railroads

2024· article· en· W4399642387 on OpenAlexaff
Nazmul Hasan, Maki Soda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsEquivalence (formal languages)Computer scienceMathematicsPure mathematics

Abstract

fetched live from OpenAlex

This paper aims to resolve confusion among track professionals regarding the shape of vertical curves on railroads, specifically whether they should be considered parabolic or circular. Through mathematical, geometric demonstrations and CAD drawing, the paper establishes the equivalence between parabolic and circular vertical curves on railroads. It derives a general formula for vertical curves that serves as the foundational source for existing formulas in reputable literature, such as the Transit Cooperative Research Program (TCRP) report and the American Railway Engineering and Maintenance-of-Way Association (AREMA) manual. The paper reviews these vertical curve formulas and recommends a maximum acceptable value for vertical acceleration. It also provides installation criterion of a vertical curve. The minimum radius required to ensure stability of a vertical curve under thermal load under unloaded condition is analyzed; this important safety aspect is ignored in literature. The paper then suggests the absolute minimum values for the radius of a vertical curve on both ballasted and direct fixation track. By addressing these aspects, the paper aims to offer a comprehensive guidance for professionals involved in designing and implementing vertical curves on railroads, ensuring safety and efficient operation of the rail network.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.016
GPT teacher head0.251
Teacher spread0.235 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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