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Record W2796232731 · doi:10.11159/icgre18.155

Investigation into Some Design Aspects of Ballasted Railway TrackFoundations using Numerical Modelling

2018· article· en· W2796232731 on OpenAlexvenueno aff
Md Abu Sayeed, Mohamed A. Shahin

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTrack (disk drive)Computer scienceEngineeringCivil engineeringConstruction engineeringTransport engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Railways have become the most popular means of public transportation in many countries around the world. Therefore, an investigation into the impact of design parameters affecting the overall behaviour of railway track foundations under train dynamic loading is necessary for optimum and reliable design. Railway track foundations consist of a graded layer of granular media of ballast and subballast, which is laid on a naturally deposited subgrade soil. Most available methods for design of ballasted railway track foundations assume linear elastic behaviour for track geomaterials. However, the resilient behaviour of track geomaterials, especially the ballast layer, is mostly non-linear and may incur plasticity, depending on the level of applied stress. In this paper, a sophisticated three-dimensional finite element modelling is developed to investigate the track response subjected to train moving loads, by considering actual characteristics of track geomaterials including non-linearity and plasticity. The results are compared with the simpler elastic modelling response, and the practical implications are discussed.

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 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 score0.017
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.193
Teacher spread0.180 · 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 teacher head, 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

Citations0
Published2018
Admission routes1
Has abstractyes

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