Investigation of calcium leaching deformation and damage in cement-based materials
Bibliographic record
Abstract
Traditional length measurement methods are difficult to quantify the local deformation and damage in cement-based materials caused by calcium leaching. In this study, Digital Image Correlation (DIC) was employed to quantify local deformation and damage, while Lattice fracture model was utilized to investigate the initiation and propagation of microcracks. The results showed that the bulk shrinkage deformation characterized by DIC was consistent with the data measured by the length comparator. Higher W/C ratio leads to increased shrinkage. The effect of sample types on shrinkage follows this order: cement paste > mortar > concrete. The calcium leaching damage factor in concrete can reach 148.4 μm·m−1 after leaching for 42 d, with damage being more severe on the surface and gradually decreasing toward the interior. A strong correlation was observed between the simulated microcracks and the damage field, as well as between the total microcrack area and the calcium leaching damage factor.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 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".