Pumping Concrete: A Fundamental and Practical Approach
Bibliographic record
Abstract
The pumping and shooting of high performance wet-mix shotcrete usually involves a certain amount of compromise. On the one hand, engineers design a mixture with high workability for ease of transport through the hose, and on the other hand, they strive for a mixture that is relatively stiff, adhesive, and cohesive to achieve good adhesion and build-up on vertical or overhead shooting surfaces. Although a decade of developments in set accelerating admixtures and dosing equipment have greatly simplified the application of wet-mix shotcrete in underground environments, only a few fundamental or practical studies have been made on the pumpability of concrete. This paper presents some of the most recent research on the understanding of the key parameters affecting concrete mobility and stability under pressure, i.e. pumpability. Taking into account the mechanics of life-size pumping equipment, complete pressure profiles and pump cylinder fill-rates, along with rheological and tribological properties, are used to predict the mobility and pumpability of a given concrete mixture. A model that accounts for fluid properties and friction is also presented. Experimental results used to validate the model allow explanation of behavioral, variations between the different concrete mixtures.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".