Comparison of curing methods for bulk electrical conductivity testing of cement pastes
Bibliographic record
Abstract
Abstract Predicting the service life of concrete is one of the main problems faced by civil engineers. Measuring its electrical conductivity is a promising method to quickly find a qualitative indication of the ionic diffusion behaviour of a cementitious mix and, therefore, of its durability. However, one limitation on the ability to quantitatively measure bulk conductivity is the effect of the curing method on the measurement. The main phenomenon that occurs during curing and affects the conductivity is the leaching of alkalis from the pore solution into the curing solution driven by the alkali concentration gradient between these two solutions. The pore solution is therefore depleted in alkalis, reducing both pore solution and bulk conductivity. However, previous work has shown that leaching is not the only effect that the curing solution can have on the bulk conductivity. To avoid these effects, the curing solution must simulate the pore solution at each moment of hydration and for each cementitious mix. For this purpose, different curing solutions based on a sacrificial powder, a reduced volume of curing solution, and a simulated pore solution are proposed and investigated experimentally. The results show that a small volume of curing solution is the most promising method to prevent leaching without affecting the hydration rate.
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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 | 0.000 | 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".