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Record W4242516622 · doi:10.32920/ryerson.14638902.v1

Reliability analysis of truck escape ramp design

2021· preprint· en· W4242516622 on OpenAlexaffabout
Kaitlyn Ann Greto

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsTruckReliability (semiconductor)Moment (physics)Reliability engineeringVariance (accounting)Random variableVariable (mathematics)Computer scienceEngineeringStatisticsAutomotive engineeringMathematicsPower (physics)

Abstract

fetched live from OpenAlex

The truck escape ramp design presented by the Transportation Association of Canada is based on deterministic values of the design variables which include the required stopping distance, design speed, rolling resistance, and grade. Currently, a reliability analysis of the design of truck escape ramps does not exist. This report presents two methods used to analyze the reliability of truck escape ramp design; the first order second moment reliability method and the advanced first order second moment reliability method. These methods do not rely on deterministic values rather the mean and variance (moments) of each random variable’s probability distribution. Each reliability method was used to analyze truck escape ramps with one grade and two grades, for a total of four cases. The results of each case are provided and discussed along with an application to two existing truck escape ramps. The results show that the advanced first order second moment reliability method ensures more accurate results as well as a larger safety margin in comparison to the first order second moment method due to the nature of the methodology itself which considers design points.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.476
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.269
Teacher spread0.237 · 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.

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

Citations2
Published2021
Admission routes2
Has abstractyes

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