A Hybrid Approach to Pavement Performance Prediction in Cold Regions: Machine Learning and Falling Weight Deflectometer Analysis
Notice bibliographique
Résumé
Cold region pavements are subjected to extreme environmental conditions, including prolonged periods of low temperatures and repeated freeze-thaw cycles. These harsh conditions accelerate pavement deterioration, leading to an increase in maintenance requirements and significantly higher rehabilitation costs. To address these issues and explore the potential application of sustainable waste materials in pavement design, the Integrated Road Research Facility (IRRF) test section was constructed in Edmonton, Alberta, in 2012. This test section was specifically designed to incorporate sustainable materials, such as bottom ash, polystyrene, and tire-derived aggregates, to reduce environmental impact and enhance pavement durability. Environmental sensors were embedded into the pavement to monitor its performance. However, due to sensor malfunctions over time, the need for enhanced monitoring led to the construction of a new test section in 2022, featuring upgraded instrumentation to ensure improved data accuracy and reliability. This study focuses on temperature data collected from the new test section between June 2023 and December 2024, specifically examining the effects of temperature fluctuations on pavement performance. Using novel machine learning models, this research aims to predict key parameters such as frost depth penetration, as well as the duration and timelines of freeze and thaw cycles, A multi-depth prediction approach was employed to compare both holistic and multi-depth temperature variations from June 2023 to December 2024, ensuring highly accurate insights into the environmental impacts on pavement structures. By incorporating data for the full unbound pavement layers, this approach enhances the predictive capability of the model, providing more detailed insights into temperature variations at multiple depths within the pavement. To assess the long-term performance of the entire test section, including both the insulation layer and tire embankment sections, Falling Weight Deflectometer (FWD) testing was performed in 2015 and 2024. The decade-long comparison of changes in pavement dynamic modulus shows long-term durability and structural behaviour of the pavement. The findings of this study confirm the effectiveness of machine learning models in accurately predicting temperature variations within pavements, a key factor in optimizing pavement design and maintenance strategies. Additionally, the integration of sustainable materials into the test section demonstrates their potential to significantly improve pavement resilience in response to climate change. The results highlight the importance of utilizing innovative materials and advanced modeling techniques in developing adaptive and sustainable pavement management practices. These findings will contribute to more efficient, durable, and environmentally-friendly pavement infrastructure, paving the way for the future of cold-region construction.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».