MétaCan
Menu
Back to cohort
Record W640920949 · doi:10.33593/iccp.v10i1.441

The Search for the Best Concrete Surface Texture Relative to Noise and Skid Resistance in Québec

2025· article· en· W640920949 on OpenAlexaboutno aff
Julie Roby, Marina Beaudoin, D Thébeau

Bibliographic record

VenueProceedings of the International Conference on Concrete Pavements · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsSkid (aerodynamics)Resistance (ecology)Noise (video)Forensic engineeringGeotechnical engineeringSurface finishMaterials scienceGeologyEnvironmental scienceComposite materialEngineeringComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Concrete pavements under the ministère des Transports du Québec (MTQ) are located on the highest trafficked roads in the province, often in urban areas. In the late 1990s, the MTQ noted a skid resistance problem for most of the concrete pavements rebuilt since 1994. The texture specified at the time for the new pavements was the result of dragging an Astroturf mat on the surface of the fresh concrete. This produced a low noise pavement, but skid resistance measurements were disappointing. Since 2000, the MTQ has specified the transverse tining on fresh concrete; this technique is known to provide better skid resistance. However, it is sometimes noisier and complaints were recorded in some areas. It was also found that skid resistance issues still occurred because of the limestone coarse aggregate used in almost all concrete pavements since 1994. It has been banned since 2003. Since 2004, to counter the noise effect, the longitudinal tining technique has been increasingly used by MTQ project engineers. Various other techniques were tested on new slabs such as exposed aggregate, shot-blasting and grinding. All our experimental sections were subject to tire-pavement noise measurement by our custom-made equipment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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 designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the International Conference on Concrete PavementsSame topicSmart Materials for ConstructionFrench-language works237,207