Large-Scale Laboratory Testing of the Lateral Resistance of a Timber Tie
Bibliographic record
Abstract
We present a large-scale laboratory test for quantifying the lateral resistance of a timber tie in ballast for the expected range of in-service train loads in North American railway track. The test uses three configurations to evaluate the contribution of the tie-ballast base friction, crib (side) friction, and shoulder (end) resistance, respectively, to overall tie-lateral resistance. The tests use a 1.52-m (60 in.) long, 1.27-m (50 in.) wide, 0.51-m (20 in.) high, and 0.005-m (0.19 in.) thick reinforced steel box, or ballast box, filled with 0.45 m (18 in.) of base ballast. Crib and shoulder ballast is placed as required. For each test, a single timber tie is placed on the ballast and laterally pushed up to 40 mm (1.5 in.) at a loading rate of 0.05 mm/s (0.002 in./s) while vertical and horizontal loads and displacements are recorded. The test is then repeated at several vertical loads, ranging from 5 kN (i.e., the estimated weight of track superstructure) to 160 kN (i.e., the maximum potential in-service ballast load transferred to a single timber tie). The test results are used to determine the peak-lateral resistance per tie for each test and the relationship between lateral load and normal load for each test configuration. This paper details the estimation of the maximum potential in-service ballast load transferred to a single tie, the ballast box design, the test configurations, and the equipment characteristics. This paper also outlines the testing methodology and provides an example application on a ballast material, McAbee ballast, used by the Canadian National Railway Company in Western Canada.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".