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Effect of polishing time, mechanisms and mineralogy on the microtexture evolution and polishing resistance of pavement surface aggregates

2025· article· en· W4409377488 on OpenAlexaffabout
Mbayang Kandji, Benoît Fournier, Josée Duchesne, Félix Doucet

Bibliographic record

VenueConstruction and Building Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversité LavalMinistry of Transportation of OntarioGeological Survey of Canada
Fundersnot available
KeywordsPolishingMaterials scienceComposite materialChemical-mechanical planarizationSurface (topology)MetallurgyGeometry

Abstract

fetched live from OpenAlex

The LC 21–102 test standard is an accelerated polishing by projection test used in the province of Quebec (Canada), with the objective of theorically bringing pavement surface coarse aggregates to their maximum wear. Their suitability for use in surface layer on high trafficked roads, regarding skid resistance, is then evaluated by measuring their residual friction coefficient using a British pendulum. This study investigates the influence of polishing by projection time, and aims to provide a better understanding of its wear mechanisms by analyzing the evolution of the aggregates microtexture. Four aggregates with different mineralogy and polishing resistance were selected for the study. Their mineralogical, physical and mechanical properties were determined using various techniques, including optical microscopy, X-Ray Diffraction, Los Angeles and Micro-Deval tests. A high-precision 3D laser microprofilometer was used to capture the surface relief of aggregate particles and to determine their microtexture parameters such as peak density, shape and height. A British pendulum was used to measure the residual friction coefficient. Tests were performed prior to polishing and at incremental stages that went beyond the standard time. The results show a continuous decrease in friction values beyond the standard polishing time for all tested aggregates, suggesting the need to extend the polishing time to reach maximum wear. Polishing by projection also appears to operate through a distinct mechanism compared to other well-known methods: it acts more by indentation, digging into the aggregate surface and generating a new microtexture with, on average, less dense but higher and sharper peaks. Furthermore, aggregate type, grain size, general and differential hardness (to a lesser extent) seem to influence the initial microtexture formation and its evolution during polishing. • RHD, DH, and grain size are key factors for initial microtexture and its evolution. • Polishing by projection mechanisms differ from other commonly used methods. • Polishing by projection digs into aggregate surface making peaks taller and sharper. • Polishing aggregates for 20 cycles may overestimate their skid resistance. • Optimal polishing cycle count appears to be around 35 cycles.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.351

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 science0.0000.000
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 head0.004
GPT teacher head0.216
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

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

Citations10
Published2025
Admission routes2
Has abstractyes

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