Impact of Substitution of Portland Cement by Untreated Dredged Sediments on Concrete Properties
Bibliographic record
Abstract
In a context where the construction industry is seeking to reduce its carbon footprint, this article explores the integration of St. Lawrence River dredged sediments as a partial substitute for Portland cement in the design of low environmental impact concretes.The aim is to assess the technical and environmental feasibility of this approach in relation to construction materials management practices in Quebec.The sediments studied are highly plastic silty clays, subjected to a treatment including drying, crushing and grinding.Their physical (particle size, density, consistency), chemical (elemental analysis by X-ray fluorescence) and mineralogical (identification of crystalline phases by X-ray diffraction) characteristics were analyzed to assess their compatibility with the requirements of cementitious materials.In terms of mechanical performance and durability, compression tests were carried out on mortars substituting 20% of GU cement with sediment, with measurements taken at the ages of 1, 7, 28 and 112 days.The microstructural study assessed the pore distribution and connectivity using mercury intrusion porosimetry and computed tomography.The results show a compressive strength of 51.2 MPa for the control mortar, made up solely of GU cement, versus 50.2 MPa for the mortar containing 20% of the sediment powder after 112 days.This represents approximately 98% of the performance of mortar made from 100% GU cement.From an operational point of view, this approach offers a dual advantage: efficient reclamation of harbour sediments, thereby reducing the costs associated with their management, and a reduction in the carbon footprint associated with cement production.These results offer promising prospects for materials engineering professionals and port managers, facilitating the integration of sediments into civil engineering applications while meeting performance and sustainability requirements.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".