Valorization of Dredged Sediments and Recycled Concrete Aggregates in Road Subgrade Construction
Bibliographic record
Abstract
Large quantities of dredged sediments and recycled concrete materials are generated every year all over the world. The disposal of these large quantities in landfills represents serious environmental problems. Furthermore, high-quality raw materials for construction are depleting, and their use cannot be sustained. The valorization of dredged sediments and recycled concrete materials as alternative construction materials has the potential to reduce the impact of these two issues. In this context, this study aims at investigating the feasibility of using dredged sediments and recycled concrete aggregates as alternative raw material for road subgrade construction. Various mix designs were prepared using dredged sediments and recycled concrete aggregates. The mixes were then treated with quicklime and road binder as specified in the French soil treatment guide. Their physical, mechanical, and geotechnical properties confirmed the feasibility of using recycled concrete aggregates and dredged sediments up to a certain percentage in road subgrade construction. Moreover, they showed that the mixes containing 20% of dredged sediments met road subgrade minimum physical and mechanical properties, such as immediate bearing capacity, unconfined compression strength, indirect tensile strength greater, and UCSI/UCS60 ratio. Finally, leaching tests were conducted to ensure the environmental safety of the various mixes. The results showed that the mixes met the thresholds for their use in road subgrade construction. The feasibility of using dredged sediments and recycled concrete aggregates in foundations and base layers will be studied in future projects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".