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Record W4402512014 · doi:10.11159/ijci.2024.015

The Impact of Sandy Fouled Ballast Properties on Its Mechanical Behavior

2024· article· en· W4402512014 on OpenAlexvenueno aff
Ahmed Nabil Ramadan, Jinxi Zhang, Tianchi Li, Biao Xu, Peng Jing

Bibliographic record

VenueInternational Journal of Civil Infrastructure · 2024
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBallastGeotechnical engineeringEnvironmental scienceGeologyOceanography

Abstract

fetched live from OpenAlex

The impact of the coefficient of uniformity and porosity on the shear strength of sandy fouled ballast was examined in this study.Data from existing studies were analyzed to understand how these factors, alongside the fouling index, influence the mechanical behavior of the clean and fouled ballast.A clear inverse relationship between the coefficient of uniformity and shear strength was found when the fouling index was lower than 50%; beyond this point, the relationship tended to become positive.Furthermore, an unexpected direct relationship was noted between porosity and shear strength, where lower porosity led to diminished shear strength.This atypical behavior was attributed to the combined presence of sand with ballast, creating a heterogeneous material that altered the typical impact of porosity seen in single-material compositions.These insights underscore the importance of considering multiple parameters, such as the coefficient of uniformity, porosity, and fouling index, for accurate predictions of shear strength.Understanding these relationships is essential for optimizing the mechanical behavior of sandy fouled ballast, thereby enhancing railway track design and maintenance strategies.This investigation provides a basic foundation to enhance the estimation and prediction of the shear strength of sandy fouled ballast based on experimental investigation of current studies.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.576

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)0.0010.000

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 head0.017
GPT teacher head0.281
Teacher spread0.264 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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