Automatic image analysis process to appraise segregation resistance of self-consolidating concrete
Bibliographic record
Abstract
The segregation of coarse aggregate in self-consolidating concrete (SCC) can result in some defects in the long-term properties of concrete. In this study, an image processing algorithm has been proposed and implemented, using standard coding languages, to assess segregation of the SCC mixtures. This algorithm has been designed to evaluate the distribution of coarse aggregate and the average thickness of the mortar band layer of SCC. Seven mixtures containing fly ash and silica fume as partial replacements for cement were analysed. Slump, J-ring flow and compressive strength of the samples were also measured. The results revealed that an increase in fly ash replacement resulted in a higher segregation index and a higher ratio of mortar band thickness. However, substitution of silica fume caused a lower segregation index and a lower ratio of mortar band thickness. The results demonstrate that there is a high degree of correlation between the segregation index and the mortar band thickness in all the mixtures.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".