New approach for the mix design of high-strength concretes valorization of local aggregates
Bibliographic record
Abstract
Abstract High performance concrete (HPC) is an innovative concrete that finds its place in modern buildings. The new high-performance concrete formulation and conception techniques have improved sustainability and acquiring remarkable mechanical properties compared to conventional concretes. The objective of this work is to make a contribution to understanding the influence of factors such as the W/ C ratio and the granular class on the mechanical and physical properties of high performance concretes. In the formulations of HPC aggregates by their high mass and volume proportion, play an important role, the Sherbrook and Dreux-Gorisse method has been used. When selecting aggregates it is necessary to know their intrinsic properties. These properties influence the performance of concrete in particular the quality of the granulate cimentary adhesion. At the end of our work, the examination of the results obtained made it possible to establish the correlations between the formulations studied and the physico-mechanical characteristics of the concrete compositions (HPC 25, HPC 16, HPC 8). The results of this study show that the use of three granular classifications (DMAX8, DMAX16 and DMAX25) and three Report W / C (0.25, 0.30, and 0.35) in two different conservation environment (EP drinking water and sea water ), clearly proves that the composition of high performance concrete is HPC25 concrete with an W/ C = 0.25 ratio has reached the largest mechanical strength of 80 MPa for different environments of conservation. When selecting aggregates it is necessary to know their intrinsic properties, these properties influence the performance of concrete. In general, there is a slight decrease in the compressive resistance of the specimens stored in seawater, it can be said that the conservation life has not had effect on the resistance (28 days). The effect of aggressive environment can appear in the long term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".