An investigation of the effects of axle spacing on the rail bending stress behavior
Bibliographic record
Abstract
Under heavy and frequent train loads, large stresses can develop in the rail, of which the bending stress is an important portion. Bending stress may cause fatigue defects to grow and also result in rail breaks, which is the dominant failure mode according to the records of derailments caused by rail issues reported by the Transportation Safety Board of Canada. In this study, the rail bending stresses under different track and loading conditions when the axle spacing between adjacent railcars varies were investigated. Finite element models of different complexities were established using ABAQUS. The Winkler model was also used in the investigation for comparison and reference. Three levels of track modulus, which are 13.79 MPa, 27.58 MPa, and 41.37 MPa were studied, representing soft, medium and stiff track conditions respectively. Two rail sections, the 115 RE rail and the 136 RE rail, were used, which are common rail sections in North American freight railways. Location effects of wheel loads on the rail bending stress behavior when the axle spacing varies were also examined. It is demonstrated that when wheel loads were applied at the middle of the rail head surface, under each track modulus and for each rail section, the maximum bending stress at the rail head generally follows a pattern of first increasing and then decreasing when the axle spacing increases, while the maximum bending stress at the rail base fluctuates in a small variation range and does not show a clear pattern. This thesis provides useful guidance in the aspect of studying the effects of axle spacing on the rail bending stress behavior. At the end of the thesis, limitations of current work, recommendations, and future work were also addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".