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Record W2148774596 · doi:10.1139/l05-016

Establishing design guidelines for compound horizontal curves on three-dimensional alignments

2005· article· en· W2148774596 on OpenAlexfundvenueno aff
Said M. Easa, Essam Dabbour

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCurvatureTerrainHorizontal and verticalRADIUSGeometryCrestArc lengthGeometric designRadius of curvatureAccelerationComputer scienceGeologyMathematicsArc (geometry)OpticsPhysicsMean curvature

Abstract

fetched live from OpenAlex

In current design guides, the minimum radii of compound horizontal curves are based on the design requirements of simple horizontal curves for each arc on flat terrain. Such a design ignores the effects of compound curvature and vertical alignment. This paper uses computer simulation software to establish the minimum radius requirements for compound curves, considering these effects. The actual lateral acceleration experienced by a vehicle negotiating a two-dimensional (2-D) simple curve is recorded as a base scenario to facilitate the analysis of a compound curve on a flat terrain or combined with vertical alignment (three-dimensional (3-D) compound curves). The vertical alignments examined include upgrades, downgrades, crest curves, and sag curves. Mathematical models for minimum radius requirements were developed for flat and 3-D compound curves. Three types of design vehicles were used. The results show that an increase in the minimum radius ranging from 5% to 26% is required to compensate for the effects of both compound curvature and vertical alignment.Key words: highway geometric design, compound horizontal alignments, side friction, vehicle simulation, 3-D alignments.

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: none
Teacher disagreement score0.829
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.038
GPT teacher head0.237
Teacher spread0.199 · 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

Citations11
Published2005
Admission routes2
Has abstractyes

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