Short-term durability and environmental integration of dredged sediment-based concrete in seawater
Bibliographic record
Abstract
The increasing demand for natural sand in construction and tightening environmental regulations have prompted interest in alternative materials for concrete production. This study explores the use of untreated dredged marine sediments as a 30 % partial sand replacement in marine concrete. Two mixtures, a reference concrete (RC) and a marine sediment concrete (MSC), were evaluated before and after 70 days of seawater immersion. The experimental program evaluated compressive strength, durability (porosity, permeability, and chloride ingress), biocolonization potential, and environmental and economic viability through Life Cycle Assessment (LCA) and Life Cycle Cost Analysis (LCCA). The results indicate that MSC achieves compressive strength comparable to RC (58.6 MPa vs. 59.7 MPa at 28 days) with lower gas permeability (64.3 × 10⁻¹⁸ m² for MSC vs. 88.9 × 10⁻¹⁸ m² for RC), suggesting a denser microstructure. Although MSC showed a higher initial chloride content (0.11 % vs. 0.05 %), this is attributed to the chloride-bearing sediment. Notably, MSC demonstrated faster biocolonization, indicating stronger ecological integration in marine environments. From a sustainability perspective, MSC reduces environmental impacts associated with sand extraction, promotes the reuse of dredged sediment, and lowers production costs. These findings suggest that MSC could be a promising material for eco-designed coastal infrastructure such as reefs, breakwaters, and offshore foundations. However, further research is needed to evaluate long-term performance, optimize mix design, and validate large-scale applications for sustainable marine construction. • MSC shows strong mechanical performance even after seawater exposure. • MSC supports biocolonization for eco-designed marine structures. • MSC reduces the carbon footprint and overall production costs of marine concretes.
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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".