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Record W2560795425 · doi:10.1139/cgj-2016-0256

Performance assessment of peat rail subgrade before and after mass stabilization

2016· article· en· W2560795425 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Geotechnical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSubgradeBallastStiffnessGeotechnical engineeringTrack (disk drive)ModulusTrainStructural engineeringPeatGeologyEngineeringEnvironmental scienceMaterials scienceComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Railway tracks over peat subgrades can experience large ground deformations, increased pore-water pressures, formation of pumping holes, and pumping of fines during the passage of trains, which can lead to accelerated track deterioration and risk of derailment. One approach to mitigate these issues is to improve the subgrade stiffness using mass stabilization, which involves mixing a binding agent, such as cement, into a soil to improve its physical properties. This paper describes the development and use of a method to calculate trackbed modulus to quantify the improvement due to mass stabilization at a site with peat subgrade. Track modulus was calculated using in-service freight trains by measuring track displacements using digital image correlation and wheel loads from a nearby wheel impact load detector. Because of the voids that existed between the rail, sleepers, and ballast it was found that using displacements of the ballast crib to calculate the trackbed modulus, instead of the overall track modulus using rail or sleeper displacements, provided a way to quantify the improvement of the subgrade that was not affected by the presence of voids. The results indicate the post-rehabilitation trackbed modulus was double the original baseline value for the track section, indicating that mass stabilization can be an effective rehabilitation strategy to improve the stiffness of problematic peat subgrades.

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.

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 score0.159
Threshold uncertainty score0.307

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 head0.004
GPT teacher head0.187
Teacher spread0.183 · 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