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Record W4382516664 · doi:10.4203/ccc.1.3.8

Laboratory investigation of the effect of rubber coating on stone ballast life

2023· article· en· W4382516664 on OpenAlexaboutno aff
Morteza Esmaeili, Parvaneh Namaei

Bibliographic record

VenueCivil-comp conferences · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsnot available
Fundersnot available
KeywordsBallastCoatingNatural rubberMaterials scienceForensic engineeringEngineeringComposite materialElectrical engineering

Abstract

fetched live from OpenAlex

So far, many studies have been conducted to improve the degradation behavior of ballasted tracks by using waste tires mixed with ballast.However, coating stone ballast materials with rubber particles, which has been proposed in recent years, has received less attention in the literature.In this paper, a laboratory investigation on the effect of rubber coating on ballast life was carried out.For this purpose, the optimal coating method with a focus on selecting the appropriate percentage of rubber particles mixed with adhesive was presented in the first stage.Then, by applying the selected coating, its effect on the abrasive behavior of ballast taken from a quarry in Tehran city was investigated through Los Angeles and Micro-Deval tests.In the next step, breakage and settlement of the ballast with and without rubber coating were evaluated by performing ballast box test by applying 100,000 loading cycles with an amplitude of 15 kN and frequency of 3 Hz.The results confirm that the application of rubber coating has led to a reduction in the Los Angeles and Micro-Deval coefficients by 66.15% and 93.18%, respectively, and increasing settlement and decreasing breakage under cyclic loading by 24.17% and 88.32%, respectively.In general, according to the Canadian Pacific Rail Code, the use of this technology can enhance the average ballast life by almost 91%.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.738

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 studies0.0000.001
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 head0.025
GPT teacher head0.224
Teacher spread0.199 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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