Experimental investigation of marine soil stabilization with recycled aggregates and MgO: implications for CO<sub>2</sub> sequestration
Bibliographic record
Abstract
Dredged marine soils are increasingly recognized as a valuable resource amidst growing environmental concerns and the need for sustainable waste recycling. This study presents an innovative soil stabilization technique combining recycled aggregate (RA) and magnesium oxide (MgO) with a dual focus on enhancing soil properties and promoting carbon dioxide (CO 2 ) sequestration. The stabilizing effects of RA and MgO were evaluated independently and synergistically under varied curing conditions and durations, with microstructural and mechanical properties analysed using scanned electron microscopy, X-ray diffraction, and uniaxial tests. Carbonation experiments quantified CO 2 fixation potential, with the formation of hydration and carbonation products, along with dynamic moisture content and pH conditions, playing a significant role in enhancing the structural reinforcement of the soil. The combined RA-MgO treatment achieved superior mechanical stability (1.28–3.02 MPa) and a CO 2 sequestration capacity of up to 11 g/kg without compromising performance. This study highlights the dual environmental and structural benefits of utilizing RA and low-content MgO for marine soil stabilization, offering a sustainable pathway to reduce carbon emissions, promote waste recycling, and support resilient infrastructure development.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".