Exposition au chlorure et pénétration dans le béton dans des conditions hivernales rigoureuses
Notice bibliographique
Résumé
The application of de-icing salts on roads is an essential practice in cold regions to maintain safe driving conditions during winter months. However, the use of these salts has been found to have negative effects on the environment and the durability of concrete infrastructure due to chloride-induced corrosion.Previous studies have highlighted the significance of considering boundary conditions when predicting chloride penetration in concrete and its impact on service life models. However, quantifying environmental actions and real exposures is challenging, which makes precisely predicting chloride ingress difficult.This research aimed to conduct a comprehensive study during the winter periods, leading to salting, environmental exposure, and penetration into concrete.Through field experiments and monitoring weather station, salt operations, firstly, the study aims to predict the duration between salt application by trucks and the completion of snow melting. However, road condition monitoring via cameras and sensor data revealed that the efficiency of winter maintenance operations could be improved under certain conditions for our studied site. To mitigate this issue, the research employs hazard ratio analysis and Cox regression modeling to determine whether the melting process is complete under specific conditions, facilitating better decision-making for salt application and minimizing excessive use.Assuming effective salting operation, after melting process, the concentration levels of this residual salt on the road surface over time become a crucial input for predicting chloride ingress into concrete structures near the roadways. To model this ingress phenomenon accurately, the research monitors real salting operations, data collects from climate stations, and salt concentration sensors, and develops data-driven models to predict the evolution of salt concentrations on the road over time after salting events. This temporal data is integrated into service life models for corrosion initiation predictions.In a parallel investigation, the research focuses on assessing the durability of concrete structures exposed to de-icing salts by evaluating the extent of chloride ingress. To replicate real-world conditions, concrete samples were deployed along a roadway at varying distances, heights, and positions from the roadside over a three-year period. The chloride profiles in these samples were measured after successive winters to assess the spatial severity of chloride ingress under realistic de-icing conditions.The TransChlor® model, a specialized tool for predicting chloride ingress in concrete, was selected, and refined to enhance its accuracy. Improvements were made by incorporating modified boundary conditions, such as a new relative humidity assumption based on the exposure conditions, an on-road salt evolution model, and splash/mist transport functions derived from on-site measurements. The refined model demonstrated improved predictions of measured chloride profiles under the range of curing regimes and sample locations tested, providing a more reliable tool for forecasting concrete infrastructure deterioration in cold climates.Furthermore, the research investigated the durability of various components of the old Champlain Bridge in Montreal, focusing on chloride ingress under de-icing conditions. Chloride penetration was measured in both the original and repaired concrete sections, and non-destructive air permeability tests were conducted to determine the transport characteristics of the concrete. The TransChlor® model was then employed to predict chloride ingress into the exposed repair sections and the substrate over the bridge's service life.Finally, this research contributes significantly to understanding the impact of de-icing salts on concrete durability by combining field data, monitoring stations, advanced modelling tools, and case studies on existing infrastructure. The findings propose an approach to distinguishing effective winter maintenance, providing a foundation for developing better boundary condition modelling, and predicting the chloride extent in concrete infrastructure in cold climates exposed to de-icing salts in the short or long term, with or without repair.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».