Square-root law prediction of chloride penetration rates in stabilized cement pastes
Bibliographic record
Abstract
The chloride penetration rate in a cementitious system characterizes its ability to resist chloride-induced corrosion. Assessing this property involves determining a diffusion coefficient obtained from diffusion or migration tests, or from models. The evolution of penetration depth can be used to predict the durability of a cementitious system, as it follows a linear relationship with the square root of time, known as the square root law. However, given the many assumptions behind this law, it remains unclear when and how it can be used to predict future penetration depths. This study investigates the applicability of the law for seven binders and shows that it can be used to monitor the evolution of penetration depth before the stabilization of the properties of the specimen, except when glass powder is used. However, predicting future penetration depths is more accurate when both the microstructure and surface content are stable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".