The Impact of Sandy Fouled Ballast Properties on Its Mechanical Behavior
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".