Chloride Diffusion in Cement Materials at Different Leaching States: An Experimental and Numerical Study
Bibliographic record
Abstract
Due to common heavy snowfall in winter, concrete structures in cold countries are regularly in contact with water. Hence, concrete undergoes abnormal leaching of ionic species present inside the liquid pores of the material. The leaching slowly degrades the structures and may have an influence on the diffusive properties of the chloride ions, coming from the de-icing salts and responsible for the corrosion of the steel reinforcements present in the reinforced concrete. In this work, an experimental and numerical study is propounded to better understand the influence of the leaching state of cementitious material on the diffusive behaviour of chloride ions. Natural and accelerated diffusion tests were performed on samples of hydrated cement paste and concrete samples with lower chloride concentrations (50 mol/m3). The materials were conserved at different leaching state with care not to degrade the microstructure: safe (S), partially leached (PL), and leached (L). The simulation of those various tests was carried out on the reactive transport software PhreeqC to quantify the chloride diffusion. This numerical model allows distinguishing the influence of the geometry of the material (porosity, tortuosity, constrictivity…) to the chemical reactions occurring between chloride ions and the cementitious phases. It then appeared that chloride ions do not diffuse in the same way depending on the leaching state of the cementitious material. Same observations are made on hydrated cement paste and concrete samples. In a safe material, the chloride ions diffuse slowly than in a leached material. It seems not to be due to the material geometry nor chemical reactions. The numerical model developed confirms this hypothesis as it allows to distinguish and quantify their both influence on the chloride diffusion. This study, therefore, highlights the importance of considering the leaching effects in the prediction model for concrete structure durability in cold countries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".