Influence of the intrinsic properties of pavement surface aggregates on skid resistance: development of a predictive model
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it