Estimation of friction coefficient of asphalt concrete road surfaces using the fuzzy logic approach
Bibliographic record
Abstract
Vehicle speeds have increased very dramatically with new developments in todays automotive industry, and thus the friction behavior of roads has become very important from the safety point of view. The main aim of this paper is to estimate the friction behavior of asphalt concrete road surfaces at any speed using the fuzzy logic approach. Friction is defined in the paper, and the effects of road surface characteristics, mainly macrotexture and microtexture properties, on the friction behavior of asphalt concrete road surfaces are explained. The data measured from the different asphalt concrete road pavements (on Belgium road networks) representing different road surface characteristics are analyzed for estimating the friction behavior of roads. Both the multiple linear regression analysis and the fuzzy logic approach are used to estimate the friction coefficient of asphalt concrete road surfaces. The two approaches are compared, and it is shown that the fuzzy logic approach precisely estimates the friction coefficient of asphalt concrete road surfaces at any speed better than multiple linear regression analysis. It is possible that there is a strong relation between the asphalt concrete road surface characteristics, mainly macrotexture and microtexture, and friction.Key words: asphalt concrete road surfaces, friction, fuzzy estimation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".