Measuring the performance of CO <sub>2</sub> injection into field-cured concrete in the Arabian Gulf climate: an experimental study
Bibliographic record
Abstract
Concrete has experienced significant improvements in recent decades, resulting in lower Portland cement consumption as well as a lower carbon footprint through CO 2 injection while keeping comparable fresh, mechanical, and durability performance.The use of CO 2 injection in concrete (CarbonCure) in the United States and Canada has enabled advances such as green concrete under normal and severe conditions.While incorporating novel developments, this study focuses on CarbonCure concrete modification in the extreme weather of the Arabian Gulf region.This is a relatively new option that has entered the field in the Middle East.The materials, process, and fresh and hardened properties of the CarbonCure concrete made and field cured in the Arabian Gulf region will be considered here and evaluated in detail.Along with adding CO 2 to the mix, the cementitious material was lowered without impacting the quality or performance of the structural concrete.The developed green mix included up to 0.2% CO 2 by mass of cementitious material.Because of the benefit of early carbonation, which strengthened the concrete product even with less cementitious material, this green concrete mix retained a 28% increase in slump compared to the standard concrete mix without CO 2 , a 28-day compressive strength of 48 MPa, low water absorption of 1.3%, and resistance to aggressive chemicals, all within the limits defined by the standard codes.Due to these benefits, and because this green concrete was tested against the severe coastal climate, it would be ideal for maritime applications.
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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 | 0.001 | 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".