Leveraging physics with deep learning: physics-informed neural networks (PINN) for IRI prediction in flexible pavements
Bibliographic record
Abstract
This study presents a physics-informed neural network (PINN) framework for predicting the international roughness index (IRI) of flexible pavements, utilizing the long-term pavement performance database. A total of 390 observations from 74 pavement sections across the United States were utilized. The architecture of the PINN combines a mean square error (MSE) loss function with a custom physics-informed MSE (PMSE) loss function. It integrates mechanistic IRI equations sourced from the mechanistic-empirical pavement design guide (MEPDG) into the training process. Comparative analysis was performed against standard regression models and the MEPDG IRI prediction equation. Results indicate that the PINN model significantly improves prediction accuracy, achieving a coefficient of determination ( R 2 ) of 0.743 and a root mean squared error of 26.57, outperforming traditional methods. Furthermore, a closed-form equation from the model was derived. By utilizing this new simplified equation, transportation agencies can allocate resources and prioritize maintenance activities efficiently through the IRI forecast PINN Model for asphalt pavements.
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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 | 0.000 |
| 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 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".