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Record W2193305263 · doi:10.3141/2369-06

Wide-Base Single-Tire and Dual-Tire Assemblies

2013· article· en· W2193305263 on OpenAlexafffund
Damien Grellet, Guy Doré, Jean-Pascal Bilodeau, Thomas Gauliard

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2013
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversité Laval
KeywordsRutCrackingAsphalt pavementFatigue crackingGeotechnical engineeringAsphaltMaterials scienceStructural engineeringEnvironmental scienceGeologyComposite materialEngineering

Abstract

fetched live from OpenAlex

Past studies suggest that wide-base single tires [WBSTs (455/55R22.5)] induce pavement strains that can be either more or less severe than those caused by dual tires of similar sizing, as strain depends on both the spatial direction of the strain and where the strain is located in the pavement. An experimental assessment of strain basins occurring at various positions within the hot-mix asphalt (HMA) layer, as well as within pavement unbound layers, was undertaken to further this understanding. The method and the results of this assessment, along with the pavement damage predicted by using available models are presented. Four failure mechanisms were considered: HMA rutting, both bottom-up and top-down fatigue cracking, and structural rutting. Testing was conducted at two sites on four roads over a range of loads, pressures, and temperatures by using WBSTs and different sizes of dual tires. Data analysis showed several critical strain zones near the tire edges and at the tire center. Optic-fiber sensors were used to analyze these phenomena. HMA rutting was calculated by considering vertical shear strain near the surface under the edge of the tires. Other failure mechanisms were calculated by using maximum strain. The results predicted that the WBSTs tested may induce less damage in the upper part of the HMA layer and more damage when fatigue cracking and rutting of soils and unbound materials are considered. Data collected were from specific tires, and all tests were conducted only under smooth, steady-state rolling conditions. Thus, results should neither be generalized to all tires nor extrapolated to the prediction of actual field performance.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.089
GPT teacher head0.346
Teacher spread0.257 · 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 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

Citations15
Published2013
Admission routes2
Has abstractyes

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