Unravelling the correlation between natural and accelerated carbonation of low-carbon concrete using machine learning
Bibliographic record
Abstract
Understanding correlation between accelerated and natural carbonation is paramount to accurately predicting concrete's long-term carbonation resistance in real-world conditions. However, this relationship is highly dependent on material properties, mix design, and environmental exposure, making the development of a generalized correlation formula unrealistic and nonviable. To address this complexity, this research proposes a machine learning framework to estimate the correlation index for “low-carbon” concrete specific to mix design and regionally relevant climatic exposures. Two probabilistic deep learning models, achieving testing R 2 of 0.95 in predicting natural and accelerated carbonation depths, were utilized to perform 768 carbonation simulations. The results demonstrate that the developed models provide a unique capability to link the carbonation rates of mixtures under different accelerated testing conditions (e.g., CO 2 concentrations) to the carbonation rates of the same mixtures under region-specific climatic exposure. This framework offers a practical tool for the rapid evaluation of long-term carbonation in low-carbon concrete. • High-dimensional accelerated and natural carbonation of concrete datasets were collected. • Probabilistic neural networks for accelerated and natural carbonation prediction were developed. • Sensitivity analysis of predictive models to material-related and environmental exposure parameters was conducted. • Linear correlations between accelerated and natural carbonation rates were established.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".