Ensemble and evolutionary prediction of layers temperature in conventional and lightweight cellular concrete subbase pavements
Bibliographic record
Abstract
Extreme and fluctuating weather has a significant impact on the material properties of flexible pavements. Lightweight cellular concrete (LCC) can effectively mitigate weather effects due to its favourable insulating properties. To date, there has been little research on predicting temperature for different layers of conventional and LCC subbase pavements. This study investigates the application of LCC as a subbase material and its impact on layer temperature. Temperature profiles of two test roads, Erbsville and Notre Dame Drive (NDD), in Canada, have been collected for evaluation. Extreme gradient boosting (XGBoost) and genetic programming (GP) models were employed to forecast layer temperatures of Erbsville control and LCC-subbase sections based on inputs including ambient temperature, day of the year and constant depth. Shapley adaptive explanations (SHAP) were utilised for XGBoost, and parametric analysis was conducted for GP. Results indicated the superior performance of XGBoost (R2> 0.98, MAE < 1.5°C) over GP (R2> 0.97, MAE < 1.87°C), with both models demonstrating better predictive accuracy for LCC-subbase compared to the control section. SHAP, parametric analysis and external validation using NDD sections further validated the models' effectiveness in predicting temperatures for both control and LCC sections at various densities up to a depth of 0.8 m.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".