Influence of chloride cations on pore solution chloride and critical chloride threshold of carbon steel rebar
Bibliographic record
Abstract
As part of ongoing research to validate the efficacy of a potentiodynamic polarization technique of determining the critical chloride threshold (CCRIT) value of steel reinforcing bars exposed to chloride-contaminated concrete, the present work reports CCRIT values of carbon steel exposed to NaCl, CaCl2 and MgCl2. The increasing use of locally available multi-chloride anti-icing agents in Ontario during the winter season, which are brine solution with ~21% chloride as NaCl, CaCl2 and MgCl2, resulted in a need to understand the impact of these salts on the threshold values. The potentiodynamic polarization method allowed the influence of these salts on CCRIT values of carbon steel rebar to be determined in a significantly shorter period than existing standard methods.Cement pastes with varying admixed chloride as NaCl, CaCl2 and MgCl2 were cast with 0.5 w/cm ratio and their pore solution content was expressed after 28 days curing for cation and anion analyses. The resulting pore solution composition revealed increasing Ca, K and Na cations and increasing chloride and sulphate anions when the cement pastes were admixed with chloride as NaCl. However, sulphates decreased in the pore solution when the pastes were prepared with CaCl2 and MgCl2. The consequence of the latter findings was that the threshold values by mass of pore solution of the carbon steel rebar exposed to these chloride compounds were significantly lower than bars exposed to pore solution with NaCl. Nevertheless, the conversion of these values to percentage by mass of cementitious materials gave surprisingly similar values for all three chlorides
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".