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Record W2569327886 · doi:10.1177/0954409716683078

Dynamic behaviour of transition zones in soft soils during regular train traffic

2017· article· en· W2569327886 on OpenAlexaff
Bruno Zuada Coelho, Jeffrey A. Priest, P. Hölscher

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit · 2017
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsUniversity of Calgary
FundersEngineering and Physical Sciences Research CouncilTechnische Universiteit DelftProRailUniversity of Southampton
KeywordsTransition zoneCulvertTrack (disk drive)Transition (genetics)StiffnessComputer scienceGeologyGeotechnical engineeringEngineeringStructural engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Transition zones in railway tracks are of importance for infrastructure managers, due to the high maintenance required to maintain appropriate track geometry. To improve our understanding of the performance of transition zones, a research program was conducted in The Netherlands, in which a transition zone was extensively monitored during regular train traffic. This paper presents some of the results from the monitoring of this transition zone. The results highlight the poor performance of this transition zone compared to its expected design performance. The track was found to be hanging over the transition zone, exhibiting a rocking motion about a culvert. Track stiffness was found to reduce linearly with increasing train speed. The implications on the design of transition zones are discussed, with recommendations made.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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

Citations21
Published2017
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