Influence of pouring techniques and mixtures fresh properties on the structural performance of self-consolidating concrete beams
Bibliographic record
Abstract
An experimental investigation was conducted to study the influence of pouring techniques and mixture’s fresh properties on the shear strength, cracking behavior and mid-span deflection of large-scale Self-Consolidating Concrete (SCC) beams. SCC beams were poured in two different techniques: the first technique was to pour the concrete from one side of the formwork only, while the second technique was to move the pouring point along the full beam length. The variables were: the concrete type, length and depth of beams, and the viscosity and yield stress of SCC mixture. The study also included a comparison between the experimental shear strength results and the predictions of some major code-based equations. The results obtained from this investigation proved that different pouring techniques, viscosity and/or yield stress of SCC mixtures did not have a significant effect on the structural performance of SCC beams. However, beams cast with lower yield stress appeared to have slightly higher shear strength and minimum average crack heights. Also, SCC beams with higher viscosity mixture tended to have lower stiffness compared to SCC mixtures with normal viscosity mixture. Key words: Self-consolidating concrete, pouring techniques, shear strength, structural performance, code-based analysis, cracking, load-deformation response.
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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) | 0.001 | 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".