The nature of reactive powder concrete affecting its viability as a large scale construction material
Bibliographic record
Abstract
Over the last twenty years, high performance concretes have gained recognition as important large scale construction materials. These ‘traditional’ high performance concretes (HPC’s) have been used in mainstream construction with Characteristic strengths ranging between 50 and 120 MPa (Bonneau et al 1997). Although very similar to normal strength concretes, various admixtures and water reducing agents set HPC’s apart and give them their unique characteristics. Reactive Powder Concretes follow a totally different approach in the way they are prepared and used in structural applications. The contents of this report will occasionally compare these differences.Reactive Powder Concrete was first developed in the early 1990's by researchers from the French construction company Bouygues S.A., Paris, France. Marcel Cheyrezy is the man credited as the leading force in the developmental phase of RPC. Cheyrezy is the director of research and development of the Scientific Division at Bouygues. Pierre Claude Aitcin, Scientific Director of Concrete Canada at the University of Sherbrooke, was instrumental in moving RPC from the lab to the field with the application of RPC on the Sherbrooke Pedestrian/Bikeway Bridge in Sherbrooke, Quebec, Canada. This first example of RPC in construction is being closely followed by other experimental structures all over the world.....
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".