Novel multi-scale experimental approach and deep learning model to optimize capillary pressure evolution in early age concrete
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Early-age shrinkage of concrete can initiate pre-mature cracking, which can compromise the durability of concrete structures. Monitoring capillary pressure, the leading cause of concrete shrinkage, and understanding its evolution is crucial for the performance-based design of concrete, particularly at early-stages when it is more prone to cracking. This study deploys an innovative multi-scale experimental program using high-capacity tensiometers to monitor the capillary pressure up to 2000 kPa. This allowed investigating the effects of key design parameters, including the water-to-cement ratio, GGBS, SRA, and measurement depth, on the capillary pressure evolution in concrete. A new robust deep neural network model was developed to conduct extensive numerical experiments to predict the capillary pressure evolution of diverse mixtures. The net effect of multi-parameters on the capillary pressure can be investigated with this model, providing insights into the optimum design of more durable concrete mixtures with the lowest capillary pressure evolution, and guiding the implementation of appropriate cost-effective shrinkage-mitigating strategies.
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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 | 0.001 | 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 | 0.001 |
| 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 it