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Record W2411603198 · doi:10.1115/jrc2016-5821

Tank Car Top Fittings Protection: Evaluation of Derailment Failure Risks — A Conceptual Study

2016· article· en· W2411603198 on OpenAlexaff
Barbara Di Bacco, Christopher Kirney, Anand Prabhakaran, Graydon F. Booth, Florentina M. Gantoi, Rou Wen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Integrity and Reliability Analysis
Canadian institutionsTransport Canada
Fundersnot available
KeywordsDerailmentEngineeringFinite element methodRigidity (electromagnetism)Work (physics)Reliability engineeringStructural engineeringMechanical engineeringTrack (disk drive)

Abstract

fetched live from OpenAlex

Top fittings devices on tank cars are subject to damage and failure under derailment conditions, potentially leading to the release of hazardous lading. This paper describes the conceptual development of an objective methodology for evaluating the risk of fittings protection failure and the potential reduction in that risk when mitigating strategies such as improved fittings protective structure are deployed. The methodology captures several key elements that affect fittings survival, including: the speed of derailment initiation, the impact velocity/force spectrum experienced by the fittings protective structure during the event, the strength/structural capacity of the protective structure, and the rigidity of the ground surface. Detailed finite element modeling efforts were employed to capture derailment dynamics and corresponding impact velocity spectra, as well as the strength of multiple protective designs. Future work, including validation, is planned to extend the concept into a detailed methodology.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.293
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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