Ensemble and evolutionary prediction of layers temperature in conventional and lightweight cellular concrete subbase pavements

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.

KEYWORDS:

• Lightweight cellular concrete
• flexible pavements temperature predictions
• extreme gradient boosting
• genetic programming
• SHAP analysis
• pavement insulation

Acknowledgments

The authors of this research gratefully acknowledge CEMATRIX (CANADA) Inc and Natural Sciences and Engineering Research Council of Canada (NSERC, Grant Number: CRDPJ 514908–2017), the Region of Waterloo, Ontario, Canada, and Centre for Pavement and Transportation Technology (CPATT), the University of Waterloo for supporting this research. The authors also acknowledge the support of the National Key R and D program of China (Grant Number: 2021YFB2600604, 2021YFB2600600), National Natural Science Foundation of China (52108403), Fundamental Research Funds for the Central University of China (grant number: 2242022R10054), and the Jiangsu Provincial Department of Science and Technology (Grant Number: BZ2023019). The authors would like to thank the anonymous referees for constructive comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.