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Record W4402477201 · doi:10.11159/icceia24.122

Experimental Modelling of Washboard Phenomenon in UnpavedRoads

2024· article· en· W4402477201 on OpenAlex
Marı́a José Torres, Bernardo Caicedo, Laura Ibagon Fabricio Yépez

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2024
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsPhenomenonComputer scienceGeologyPhysics

Abstract

fetched live from OpenAlex

The washboard phenomenon is identified by the presence of ripples on unpaved roads, emerging as vehicles traverse surfaces composed of sand, gravel, or mud.These undulating patterns not only cause discomfort but also pose potential hazards to drivers by disrupting tire-road contact.Despite its significance, limited research has been conducted on ripple phenomena, and the primary cause of their formation remains uncertain.This study aims to unravel some mechanisms of these patterns by analysing key factors such as vehicle speed, weight, and granular material properties.The research draws inspiration from the foundational work of [1][2][3][4][5][6].To investigate unpaved road ripples, experimental devices with rotating wheels at constant speeds have been employed.Previous observations suggest that ripples initiate as small waves, gradually growing to heights of up to 20 cm, with wavelengths ranging from 1 to 30 cm.Wave amplitude is influenced by factors such as vehicle speed, mass, shock absorbers, and tire inflation pressure [1][2][3][4][5][6].Field studies indicate that ripples predominantly occur in turning zones and on inclined roads, where additional stresses from vehicles impact the road material [7,8].Experiments conducted by [1][2][3][4][5][6] propose that unpaved road ripples result from repetitive vehicle passage at a critical speed, creating two distinguishable states: an apparently flat road and a road with ripples.Research findings suggest categorizing ripple phenomena into four modes based on vehicle speeds [4].At low speeds, small deformations dissipate with vehicle transit.At speeds higher than the critical speed, ripples emerge while the wheel remains in contact with the road.Higher speeds lead to continuous ripple growth until the wheel jumps, and at extremely high speeds, vehicle instability causes the wheel to leap from crest to crest.This research evaluates physical variables that influences ripple formation using an experimental multi-pass system.The system comprises an instrumented rotating wheel over a sandy path, revealing the evolution of soil ripples as the wheel passes over the track, which is the same device [9] used.Experimental simulations for various scenarios, including different wheel velocities, masses, and soil densities, were conducted.This device enables the assessment of soil plastic deformations, wheel trajectories, and dynamic forces.Furthermore, the research provides insights into potential mitigation strategies for washboard roads.It suggests that controlling vehicle speed and improving road material properties could effectively mitigate the formation and severity of these patterns, aligning with theories proposed by [3,10,11].The experiments confirm that speed defines ripple properties, such as amplitude and wavelength.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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