Preliminary Estimation of Asphalt Pavement Frictional Properties from Superpave Gyratory Specimens and Mix Parameters
Bibliographic record
Abstract
Changes in pavement texture because of temperature, moisture, and polishing reduce the available friction for vehicles to perform routine maneuvers under normal operating conditions and thereby increase the potential for skid-related accidents. Optimization of texture and frictional properties at the mix design stage requires that specimens prepared in the laboratory accurately represent the pavement surface in the field. Initial findings from an investigation of the texture and frictional properties of specimens prepared in the Superpave ® gyratory compactor compared with field measurements are presented. In addition, the mix design properties that may be altered for increased friction are presented. The surfaces of the field specimens were different from their respective gyratory surfaces but were well correlated in the case of macrotexture measurements from the sand patch test. High correlation also was observed between field macrotexture and select mix properties, including the fineness modulus, voids in the mineral aggregate, percentage passing the 4.75-mm sieve, and bulk relative density. Poor correlation was observed between the British pendulum numbers recorded on unpolished field specimens and gyratory specimens, although the bottom gyratory surfaces best matched with field values. Preliminary results suggest the gyratory compactor orients the aggregate particles in a different manner from field compaction equipment. Further, the aggregate breakdown imposed by the gyratory compactor results in additional microtexture exposure not observed on newly compacted pavements in the field until trafficking removes the upper layer of asphalt cement from the coarse aggregate particles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".