Combining Chloride Migration and Diffusion Tests to Estimate Freundlich Chloride Binding Parameters and Improve Predictions of Chloride Ingress
Bibliographic record
Abstract
Abstract Chloride binding plays a major role with respect to chloride ingress. On one hand, engineering models often rely on “apparent” diffusion coefficients, which account for both the diffusion of chlorides in the pore solution and chloride binding. On the other hand, mechanistic models intend to consider the two phenomena separately. However, the inputs to model chloride binding are quite complex to acquire, because they require dedicated experiments that are usually only conducted for research purposes. In this paper, an approach is proposed to estimate binding parameters from chloride ingress profiles. First, the effective diffusion coefficient is measured with a chloride migration test. Then, a chloride profile from a diffusion test is fitted with a model where transport of free chlorides follows Fick’s second law, and free and bound chlorides are linked by a Freundlich equation. Finally, the surface concentration and the Freundlich parameters are adjusted to obtain the best possible fit. The derived Freundlich parameters are fairly close to the ones measured in dedicated chloride binding experiments. A case study applying this approach to concrete elements exposed to seawater in a Danish harbour is also presented.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".