Effectiveness of Polymer-Modified Cement Mortar with Corrosion Inhibitor in Preventing Chloride-Induced Steel Corrosion in Concrete
Bibliographic record
Abstract
In Japan, the polymer-modified cement mortar (PCM) with the corrosion inhibitor has often been used as repair materials for deteriorated reinforced concrete structures due to the chloride-induced steel corrosion. In this study, the reinforced concrete specimens with two levels of chloride content were prepared and exposed to wet and dry cycles in the chamber for a long period to clarify the macro-cell corrosion of steel bar in the concrete. The effectiveness of the type and dosage of corrosion inhibitor, LiNO2 or Ca(NO2)2, in PCM and the degree of chipping around steel bars in repair work in preventing the steel corrosion in concrete were comparatively investigated by means of electro-chemical measurements such as linear polarization resistance and AC impedance methods.From the experimental results, it was found that PCM with both corrosion inhibitors of LiNO2 and Ca(NO2)2 had almost the same effect in preventing the steel corrosion when their NO2- molecular ratio to Cl- was constant, and that the chipping and patching work beyond steel bars was more effective in restoring the passive state of steel bars. Furthermore, both the linear polarization resistance and the AC impedance methods were very useful as a nondestructive inspection method for estimating the corrosion behavior of steel bars in repaired and unrepaired concrete with PCM.
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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) | 0.001 | 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 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".