Enhanced Prediction and Uncertainty Modeling of Pavement Roughness Using Machine Learning and Conformal Prediction
Bibliographic record
Abstract
Pavement performance models are considered a key element in pavement management systems since they can predict the future condition of pavements using historical data. Several indicators are used to evaluate the condition of pavements (such as the pavement condition index, rutting depth, and cracking severity), and the international roughness index (IRI), which is the most widely employed worldwide. This study aimed to develop an accurate IRI prediction model. Ten prediction methods were trained on a dataset of 35 independent variables. The performance of the methods was compared, and the light gradient boosting machine was identified as the best-performing method for IRI prediction. Then, the SHAP was synchronized with the best-performing method to prioritize variables based on their relative influence on IRI. The results suggested that initial IRI, mean annual temperature, and the duration between data collections had the strongest relative influence on IRI prediction. Another objective of this study was to determine the optimal uncertainty model for IRI prediction. In this regard, 12 uncertainty models were developed based on different conformal prediction methods. Gray relational analysis was performed to identify the optimal uncertainty model. The results showed that Minmax/80 was the optimal uncertainty model for IRI prediction, with an effective coverage of 93.4% and an average interval width of 0.256 m/km. Finally, a further analysis was performed on the outcomes of the optimal uncertainty model, and initial IRI, duration, annual precipitation, and a few distress parameters were identified as uncertain. The results of the framework indicate in which situations the predicted IRI may be unreliable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".