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Record W7132658169

Rail failure root cause analysis on North American Railway

2021· article· en· W7132658169 on OpenAlexvenueno aff
Daniel Szablewski, Robert Caldwell

Bibliographic record

VenueNPARC · 2021
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTrack (disk drive)DerailmentThird railRoot causeBallastTrainGrindingTrack geometry
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.198
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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