Influence of the intrinsic properties of pavement surface aggregates on skid resistance: development of a predictive model
Bibliographic record
Abstract
This study investigates how the intrinsic properties of aggregates influence their resistance to polishing by projection. Eighteen aggregates (greywackes, granites, gneiss, basalts, dolostones, limestones) were characterised through optical microscopy, chemical analysis, X-ray diffraction, Los Angeles and Micro-Deval tests. Frictional and microtextural properties were measured before and after polishing using a British Pendulum Tester and a 3D laser microprofilometer. Greywackes exhibited the best polishing resistance, followed by granites, gneiss, basalts, dolostones, and limestones. Greywackes, granites and gneiss also showed greater microtextural regeneration during polishing by projection. Relative hardness-RHD, differential hardness-DH, mineral contents (quartz, calcite, feldspars), average grain size-φm and distribution parameters (coefficient of variation-CV and Gini-style index-GSI) exhibited strong to moderate correlations with the final British Pendulum Number (BPNf) and its variation (ΔBPN). Strong correlations were also observed between BPNf, ΔBPN and microtextural parameters (peak height-H, density-Np and shape-α). Microtextural parameters showed significant associations with mineralogical properties like RHD and mineral contents. Six multiple linear regression models were developed to predict BPNf. Models based on mineralogical and petrographic properties achieved high accuracy (R² = 0.89 to 0.94), confirming mineralogy as a key factor of polishing resistance. Additional models that included microtextural parameters also achieved satisfactory predictions (R² = 0.89 to 0.93), showing their critical importance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".