Effect of CO2 sequestration on long-term concrete performance and durability
Bibliographic record
Abstract
As global greenhouse gas emissions have increased, all industries have been exploring green and sustainable materials and technologies. Studies have shown that adding CO 2 to concrete while it is mixed improves its hardened properties. This experimental approach aims to understand the impact of adding CO 2 during the mixing stage on the transport properties, freeze-thaw (F-T) resistance and corrosion resistance of embedded rebars. Key parameters like water permeability, rapid chloride ion penetration, resistivity, dynamic modulus, corrosion potential and rate were measured to assess the long-term durability. For CO 2 dosages ranging from 0.25% to 1% by weight of cement, a 50–90% reduction in the permeability coefficient, a 25–40% decrease in chloride ion penetration values, and a 10–20% increase in resistivity were observed, in comparison to control. Additionally, CO 2 dosages between 0.5%–0.75% showed improved resistance to F-T cycles, as observed by lower mass loss, less surface scaling, and increased stiffness. Concrete slab panels subjected to alternative wetting and drying cycles at elevated temperatures and salt-free environments showed improved corrosion resistance at CO 2 dosages between 0.5% and 0.75%. However, similar resistance could not be obtained in saline conditions, highlighting the need for supplementary protection to mitigate corrosion. This study also applies Tuutti’s model to predict the service life of reinforced concrete to assess the effectiveness of CO 2 sequestration.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".