Comportement rhéologique et pressions latérales des bétons autoplaçants sur les coffrage
Bibliographic record
Abstract
An experimental program was undertaken at the Université de Sherbrooke to evaluate lateral pressure developed by self consolidating concrete (SCC) mixtures. Special emphasis was given to the influence of concrete thixotropy on pressure development. Various mixture parameters, such as volume of paste and concentration of colloidal agent were examined. Moreover, the effect of height of concrete and casting speed was evaluated on experimental columns. Results obtained show that, in general, measured lateral pressure was lower than hydrostatic pressure. Initial relative pressure (Ko ) does not evolve linearly with height of concrete. For heights of up to 3 m, pressure measured showed a quasi-hydrostatic profile. However, for greater heights, pressure is 60 to 80% of hydrostatic pressure depending on mixture composition. The study has shown that lateral pressure exerted by SCC is closely linked to degree of thixotropy of concrete, which can be improved by either incorporating a colloidal agent or increasing coarse aggregate content (reduction of paste volume). Mixtures with a greater degree of thixotropy develop low pressures. Indeed, test results showed that SCC mixtures containing more paste develop greater pressure and present sharper rate of drop. The use of colloidal agent (CA) is not beneficial to accelerate rate of pressure drop with time. In fact, incorporation of CA can increase high rang water reducing admixture demand, resulting in less cohesiveness and slower pressure drop. At the end of this study, we observed that the thrust of the SCC increases with the casting rate (concrete had less time to build-up), to around hydrostatic pressure for speeds exceeding 24 m/h. The great amount of results in this project has enabled a better understanding of the behaviour of SCC within the formwork, and opens new perspectives in terms of modelling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".