Reducing water footprint of building sector: concrete with seawater and marine aggregates
Bibliographic record
Abstract
Abstract Freshwater resources are currently under great pressure all over the world due to many factors, such as climate change and growing urbanization. Industrial products like concrete pauperize a significant share of available freshwater during their life cycle. Therefore, cutting down the amount of freshwater consumed by these products might be a solution to reduce the stress in regions affected by water scarcity. In this study, the potential freshwater savings linked to the adoption of innovative concrete mixtures were investigated via the Life Cycle Assessment (LCA) method. In particular, the use of marine aggregates instead of land-based ones and seawater rather than freshwater in the mixing process of concrete were examined. To improve the validity of the analysis, the applicability to the Italian context using geo-referenced data for the distance to the coastline and the availability of freshwater was explored. Results confirmed the positive effect that the use of seawater and marine aggregates might have in reducing the water footprint of the Italian construction sector, leaving freshwater available for human consumption. Mixing concrete with seawater would lead to a reduction of its water footprint up to 12%. Moreover, if land-won aggregates were replaced with marine ones, an 84% reduction of the water footprint could be achieved. In both cases, possible burden shifting (e.g. increase of greenhouse gases emissions) should be investigated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".