Predicting layer temperatures in flexible pavement with lightweight cellular concrete subbase using explainable machine learning
Bibliographic record
Abstract
In cold regions, extreme temperatures critically influence the material properties of flexible pavement. While temperature profiles within pavement layers are evaluated using embedded sensors, long-term monitoring remains challenging. This study explores the application of machine learning (ML) to predict temperature distributions in flexible pavement incorporating lightweight cellular concrete as an insulating subbase material. Temperature data were obtained from sensors embedded in the Erbsville test road in Waterloo, Canada. Six ML models alongside gene expression programming (GEP), were evaluated, with input variables including sensor depth, day of the year, and ambient temperature. XGBoost exhibited the highest predictive accuracy during validation, achieving an R² > 0.965 and error < 1.475°C at a depth of 0.75 m. SHapley Additive exPlanations analysis elucidated variable influence, while parametric analysis validated the GEP expression. XGBoost and GEP offer a robust, high-precision alternative for temperature profile estimation in insulated pavements, outperforming conventional regression models and existing literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 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".