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Record W3001524208 · doi:10.1080/23249935.2020.1720859

System reliability as a surrogate measure of safety for horizontal curves: methodology and case studies

2020· article· en· W3001524208 on OpenAlexaff
Rushdi Alsaleh, Tarek Sayed, Karim Ismail, Fahad AlRukaibi

Bibliographic record

VenueTransportmetrica A Transport Science · 2020
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsCarleton UniversityUniversity of British Columbia
Fundersnot available
KeywordsRollover (web design)Reliability (semiconductor)TruckMonte Carlo methodMode (computer interface)ModalStability (learning theory)Geometric designEngineeringReliability engineeringStatisticsComputer scienceTransport engineeringMathematicsAutomotive engineering

Abstract

fetched live from OpenAlex

Reliability analysis has been advocated to account for the uncertainty in geometric design and to evaluate the risk associated with various design options. Most of the previous studies using reliability-analysis in highway design evaluated only one-mode of noncompliance. This study assesses the performance of horizontal curves using a system of multi-modal noncompliance (insufficient sight distance, vehicle skidding, and vehicle rollover). Five case studies of a highway in British Columbia are considered. Two approaches were used: (1) second-order reliability-bounds with FORM analysis (First-Order Reliability Method), (2) Monte-Carlo Simulation (MCS). A calibrated design chart that accommodates heavy-trucks on horizontal-curves with sharp-radii is provided. The results show that the differences in the system probability of noncompliance between one-mode and system of multi-modes of noncompliance are more pronounced for heavy-trucks. Results also show that the probability of noncompliance associated with vehicle rollover is significantly affected by the stability-ratio compared to height-ratio and roll-rate.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.067
GPT teacher head0.293
Teacher spread0.226 · 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 designObservational
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

Citations27
Published2020
Admission routes1
Has abstractyes

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