Measuring the Impacts of Climatic Exposure to Pavement Surface Deterioration with Low Cost Technology
Notice bibliographique
Résumé
Pavements play a significant role in social and economic development. Canada spent approximately 12 billion dollars annually on pavements. However, roads are exposed to climatic changes and truck loads which affect their serviceability and reduce their lifespan. Most of Canada is exposed to freeze-thaw cycles which have a drastically impact on the pavement structure. A large portion of the deterioration occurs during the spring thaw period. \n\tThis research uses a smartphone to estimate pavement roughness on a weekly basis during 30 weeks in an attempt to test if such indicator can be used to identify the beginning of the load restriction and the overall damage experienced after one environmental cycle. A pavement section located on highway 20 near Montreal was visited during 2016 and 2017 season. The studied segment is about 8 km long. A pavement roughness index (RI) was estimated before, during, and after the winter season. The air temperature was registered in order to characterize the number of freeze thaw cycles experienced. It was impossible to use the RI measurements to identify the beginning of the thawing period as RI reflected the wheel-path driven and in many occasions changed were imperceptible. It was only after taking dates with larger time separation that overall decay in roughness condition was observed. \n\tOne day during Fall, Winter, and Spring selected as an excellent case to present the freeze-thaw cycle effect and to show the variations in the pavement surface condition. It has been found that the freeze-thaw cycle impacted the subgrade soil layer which reflected on the pavement surface. The average RI value before the frost season was found to be 3.99 m/km in average, while during winter season was 4.52 m/km, and in spring season was 5.30 m/km on average. The pavement deterioration was increased by average of 1.31 m/km. The results of RI change were then transferred into other Canadian location with dissimilar freezing index and annual precipitation, and annual impact of expected roughness decay estimated for various cities.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».