Unified Analysis of Road Pavement Profiles for Evaluation of Surface Characteristics
Bibliographic record
Abstract
The research deals with the measure and evaluation of the unevenness and texture of road pavements, by means of unified procedures both for surveys and processing of acquired data, with the aim to represent the surface characteristics as a spectrum in the domain of spatial frequencies (or wavelengths). The texture properties, in fact, can be referred to many aspects of pavements performances, so allowing to establish thresholds for the acceptability of new construction or to ensure good working conditions for existing road infrastructures. The advantages of the proposed unified procedures are that the measurements are taken with modern and advanced equipment, minimizing the impact on the normal road exercise; moreover, it is possible to propose an optimized area in the frequency vs. texture level graph, where the spectrum has to fall into, in order to balance some conflicting requirements. The boundaries of the area can be also referred to the specific characteristics of the examined infrastructures; if a spectrum fits into the area, an optimal behaviour of the surface is ensured, respect to the interaction phenomena between tires and pavement which are influenced by surface texture. The proposal was tested with a case study, in which thresholds of performance parameters and boundaries of the optimized area were decided onto the basis of correlations between road indexes and texture properties, coming from the scientific literature or proposed on the basis of empirical results.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".