Predicting the Magnitude of Microsphere Parameters Obtained from Microscopical Examination of Hardened Concrete
Bibliographic record
Abstract
ABSTRACT Geometric probability concepts are used to establish a quantitative basis for predicting the magnitude of microscopically determined parameters of polymeric microsphere systems in hardened concretes relative to the actual magnitude of the parameters. Both a hypothetical discrete size distribution and a representative continuous size distribution of the microspheres are considered in the analysis. It is predicted that for a random section through the concrete, the magnitudes of the measured microsphere volume fraction and specific surface relative to the respective actual values would depend on the proportion of the total number of microspheres counted on the section. The lower the proportion of microspheres counted, the lower the ratios of measured-to-actual volume fraction and measured-to-actual specific surface would be. For the test data presented, the proportion of microspheres counted was calculated to have an average value of 0.75. Ratios of predicted-to-actual volume fraction and predicted-to-actual specific surface are compared with the respective measured ratios and found to be quite accurate. When there is a significant spread in the microsphere size distribution and relatively few microspheres are missed during a microscopical examination of a single section of concrete, the measured volume fraction would be higher and the measured specific surface would be lower, relative to the respective actual values. This is because a random section through the concrete has a greater chance of intersecting large microspheres than small ones, with large microspheres having a relatively higher contribution to volume and a relatively lower contribution to specific surface than small microspheres. These findings are relevant for air-entrained concrete as well when measurements obtained by microscopical examination of hardened concrete are compared with air content measured by the pressure method or with air content and specific surface measured by an air void analyzer.
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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 | 0.002 |
| 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 | 0.000 |
| 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 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".