Performance assessment of peat rail subgrade before and after mass stabilization
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".