Valorisation des sédiments de dragage dans des bétons autoplaçants : optimisation de la formulation et étude de la durabilité
Bibliographic record
Abstract
The demand for building materials has increased enormously, and the vast majority are of natural origin. Since these materials are not renewable, it is essential to find alternatives. Recent studies at IMT Lille Douai have shown that dredged materials have interesting pozzolanic properties and can be used as alternative cement additions or as filler. Given the large volume dredged annually, this recovery will have very positive environmental and economic impacts.The objectives of this research relate to: (1) the recycling of sediments from Grand Port Maritime de Dunkerque (GPMD, France) in self-consolidating concrete (SCCs) as supplementary cementitious materials (SCMs); (2) the recycling of fluvial sediments of Château l'Abbaye (France) in mortars as SCMs in order to assess the mobility and stability of heavy metal elements; (3) the solidification of the dredged sands of Iles-de-la-Madeline (Quebec) by hydraulic binder in order to produce false rocks which aim to play the role of the bumper against coastal erosion.Overall, the results of this work on sediments highlight the substantial contribution of these materials to improving the performance of concrete and support their use as SCMs. These results contribute to reducing the footprint of CO2 in concrete, as well as mining. Also, contributes to understanding the behavior of sediments in concrete from certain analyzes and treatment. Thus, identify aggressive environments suitable for the use of sediments.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".