Performance of capsules in self-healing early-age concrete due to restrained shrinkage
Bibliographic record
Abstract
Performance of capsules in concrete was evaluated numerically using a simulated ASTM C1581 restrained shrinkage test and experimentally measured concrete shrinkage strain data to determine their effectiveness in healing cracks in early-age concrete. The model accounts for the concrete’s time-dependent mechanical properties, and the capsule’s geometrical and mechanical properties, and depth. A finite element model was developed to simulate the concrete volume change due to autogenous and drying shrinkage and the corresponding state of stress, and fracture mechanics to trace crack initiation and propagation in a restrained shrinkage ring. The performance of capsules in self-healing early-age concrete was found to depend on the capsules’ geometry, the stiffness ratio of concrete-to-capsule, and bond strength-to-rupture strength ratio of concrete-to-capsule. Results reveal that corresponding ratios of concrete effective stiffness and bond strength at 28 days to the stiffness and rupture strength of the capsules provide a threshold limit after which capsules debond in self-healing early-age concrete. • Hybrid analysis was used to evaluate capsule performance in early-age concrete. • Capsule performance depends on the properties of early-age concrete. • Debonding depends on the strength and stiffness ratios of the concrete and capsule. • ASTM C1581 can be used to test self healing concrete at early-age.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".