Comparative analysis of machine learning models for multi-depth pavement temperature prediction
Bibliographic record
Abstract
In cold regions, temperature fluctuations critically affect the stability of granular base course and subgrade layers. To improve pavement design and maintenance, an instrumented test section was built in Edmonton, Alberta, with thermistors placed at depths of 0.25, 0.70, and 2.25 m. From May 2023 to December 2024, environmental data—including solar radiation, air temperature, and day of year—were collected. Three machine learning algorithms, Categorical Boosting, Extreme Gradient Boosting, and k-nearest neighbors, were tested for pavement temperature prediction. Models developed for individual depths consistently outperformed a single global model, with R 2 values above 0.9. Shapley additive explanations confirmed both robustness and interpretability, supporting their reliability for temperature prediction. These findings highlight the potential of depth-specific models to improve profiling in cold climates, enhancing pavement monitoring and enabling proactive maintenance strategies.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".