Rail failure root cause analysis on North American Railway
Bibliographic record
Abstract
NRC analyzed a broken rail that occurred on a North American Railway in the springtime. The break took place in 115RE standard rail placed in the high rail position of a 5 degree lubricated curve. Rail inspection focused on verifying mechanical, microstructural and chemistry measurements against current AREMA guidelines for these material properties. In addition, fractography was carried out on the fracture surfaces that led to the critical rail failure. The rail defect took place in heavily curved track territory. To pinpoint the root cause(s) of this failure NRC performed a site inspection on a 30 mile length of track inspecting 29 curves, observing running surface conditions, and recording rail profiles and eddy current measurements to build an understanding of track conditions that might have contributed to the observed critical rail failure. The paper describes the methodology undertaken in this investigation and details the outcomes at each investigative step, along with conclusions shedding light on the impact of metrics on the critical rail defect that led to the train derailment. Emphasis is placed on overall running track conditions in the investigated subdivision and on factors affecting the derailment. The paper concludes with a list of recommendations on metrics that need to be monitored with greater scrutiny to prevent future derailments. Improved rail material selection and/or more stringent grinding maintenance practices are also suggested to help prevent rail defect occurrences that might lead to critical track failures in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".