Pavement Deterioration Modeling of the International Roughness Index Based on Fuzzy Logic Approach
Bibliographic record
Abstract
The maintenance and rehabilitation of flexible pavements are crucial for achieving optimal performance and ensuring higher quality, enabling transportation planners to promptly formulate economically viable and sustainable pavement maintenance and rehabilitation strategies. Employing the fuzzy logic technique constitutes a productive methodology for assessing the degradation of flexible pavement. The fuzzy technique offers a convenient instrument for integrating subjective analysis uncertainty within the International Roughness Index (IRI) and evaluating maintenance requirements. This paper strives to construct a system rooted in fuzzy logic to appraise the requirements for maintenance and (IRI) evaluation within a network of pavement roads. This system utilizes data on pavement distress collected from the United States and Canada to achieve its objectives. Various types of pavement distress, such as fatigue cracking, rutting, longitudinal cracking, block cracking, transverse cracking, patching, ravelling, and potholes, are input variables; these parameters are fuzzified into fuzzy subsets with triangular membership functions. The performance evaluation of the analytical models was conducted using several performance indicator metrics, including the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE).
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.002 | 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 teacher head, 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".