Laboratory investigation of the effect of rubber coating on stone ballast life
Bibliographic record
Abstract
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%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".