A Case Study on Practical Prediction of Natural Carbonation for Concretes Containing Supplementary Cementitious Materials
Bibliographic record
Abstract
The present study investigated the prediction of natural carbonation, focussing on concrete construction containing supplementary cementitious materials (SCMs). Modern concrete construction predominantly employs standard common cements containing SCMs of various types and proportions. However, the use of SCMs in concrete complicates carbonation modelling, since various types and proportions of pozzolanic materials give varied levels of carbonation rate. Altogether, 553 data values of natural carbonation were taken from the literatures and employed in the natural carbonation prediction (NCP) model. The model's robustness is also partly examined through employment of contrasting carbonation exposure conditions comprising the subtropical weather of South Africa and Canada's temperate cold winter climate. The data covers a wide range of concretes containing various SCMs comprising silica fume, fly ash or slag, incorporated in various proportions meeting the requirements for standard and/or blended cement types. Realistic predictions of the measured natural carbonation results were obtained, giving similar levels of accuracy for concretes made with or without SCMs. The range of prediction accuracy for carbonation, was the same or similar to that for other natural phenomena of concrete behaviour. Findings of the present study also affirm the veracity of the carbonation modelling approach employed, and shows its applicability for concrete construction made with standard cement types, or other Portland cements containing known proportions of conventional SCMs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".