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Record W3000083498 · doi:10.1061/9780784482124.011

Nonlinear Dynamic Analysis of Track Embankments for High-Speed Trains

2019· article· en· W3000083498 on OpenAlexaff
Negin Yousefpour, Eden Almog

Bibliographic record

VenueGeo-Congress 2019 · 2019
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsArup Group (Canada)
Fundersnot available
KeywordsTrainTrack (disk drive)Nonlinear systemComputer scienceGeotechnical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

With the first high speed trains (HST) emerging in the United States, it is critical to develop better understanding of the design and performance requirements for HST track embankments. One of the main challenges in this process is to determine proper track embankment design criteria, specifically stiffness criteria to meet the acceptable deformation limits considering the maximum design speed. This study investigates the long-term deformations of HST track embankments on soft clay subgrades. The required thickness and stiffness (deformation modulus) for track embankment layers to meet the maximum deformation criteria were evaluated for a typical at-grade embankment section. A nonlinear, three-dimensional finite element (FE) model was developed to simulate the track embankment dynamic behavior under HST loading. Results of the FE analyses showed that the maximum settlement resulted from the train cyclic loading was around the typical criteria adopted for the existing HSTs. Variations of the settlements with train loading cycles showed that the plastic deformations accumulated within the first 10 cycles and after that became stationary.

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 score0.011
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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
Published2019
Admission routes1
Has abstractyes

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