Unravelling the correlation between natural and accelerated carbonation of low-carbon concrete using machine learning
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 it