Exposition au chlorure et pénétration dans le béton dans des conditions hivernales rigoureuses
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".