Degradation of crumb rubber modified railway ballast under impact loading considering aggregate gradation and rubber size
Bibliographic record
Abstract
Impact loads generated from the dynamic effect of passing trains can exacerbate the degradation level of ballast aggregate of railway track. To diminish the induced impact loads, the use of crumb rubber (CR) in the ballast course is characterized as a well-established procedure related to the modification of utilized material. Nonetheless, more in-depth assessments of size and percentage of CR particles combined with ballast aggregate are still required. The present study evaluates the influence of size and content of CR particles used for degradation reduction of ballast aggregate subjected to impact loading. For this purpose, a large-scale impact loading test is carried out on prepared specimens of aggregate by considering the initial gradation, subgrade condition, as well as the size and content of CR particles. The results indicate less ballast degradation for a higher percentage of CR particles. Meanwhile, the enhancement of rubber modified ballast against deterioration is further highlighted in the case of rigid subgrade. In addition, incorporation of larger-sized CR particles (12.5–25 mm) in a ballast specimen comprising more uniform gradation of aggregate can more effectively diminish the amount of degradation. Nevertheless, using smaller-sized CR particles (4.75–9.5 mm) for a ballast sample consisting of a broader range of sizes can better improve resistance against degradation.
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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 | 0.000 | 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) | 0.002 | 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".