Effect of sub-freezing temperatures on ballast strength: A laboratory study
Bibliographic record
Abstract
Ice formation within the ballast layer of railroad track is common in regions that experience consistent temperatures below the freezing point of water. Freely draining ballast cannot retain as much moisture as saturated fouled ballast, which may develop ice-bonded particles depending on the nature of fouling material and moisture present. This paper quantifies and compares the effect of sub-freezing [-17 ± 5°C (0 ± 10°F)] temperatures on ballast strength to non-frozen [21 ± 5°C (70 ± 10°F)] conditions. Ballast specimens were tested in a large-scale direct shear apparatus at gravimetric moisture contents, ranging from 0 % to 12 % of the dry weight of fine material [smaller than 9.5 mm (3/8-in.)], and fouling index (FI) levels ranging from 0 to 40. In non-frozen conditions, addition of moisture and fouling typically reduces the shear strength of ballast, whereas the presence of fouling and absence of moisture typically increases the strength. In a frozen condition, however, the presence of moisture and fouling increased the strength of the ballast due to ice-bonding within the ballast matrix. An increased moisture content yielded higher strengths of moderately and heavily fouled specimens in a nonlinear fashion. Non-fouled samples reduced strength due to less ice-bonding. Interestingly, higher fouling levels nonuniformly changed the strength of the ballast depending upon whether mechanical friction and aggregate interlock or ice-bonding of fine material generated higher strength. Ballast resistance is a key parameter for quantifying the stress state present within the rail, thus requiring accurate assessment of ballast strength in a multitude of environments.
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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) | 0.001 | 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 | 0.001 |
| 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".