Life cycle assessment of limestone calcined clay concrete: Potential for low-carbon 3D printing
Bibliographic record
Abstract
Limestone calcined clay cement is being promoted throughout the construction industry as a way to considerably reduce the cement proportion in concrete mixtures. At the same time, concrete 3D printing could save resources by placing concrete material only where its functionalities are maximized. This study addresses the need for a quantification of the environmental impacts related to the material acquisition phase regarding a low-clinker 3D printing material. Compared to a 30 MPa 3D printing material from the literature, a 3D printable LC3-based concrete with low clinker content in the Quebec context displays a 36 % climate change score reduction with the same compressive strength (46 % reduction in the French context). A small impact shift is noticed in 6 out of 16 midpoint indicators (5 in the French case), mainly due to the calcined clay production. However, it is offset by the significant reduction in other indicators when considering an endpoint assessment. When considering different sourcing scenarios, a global warming potential variability of up to 15 % is observed. LC3 remains a viable solution for the mitigation of greenhouse gas emissions in the context of concrete 3D printing as it provides mechanical strength and enhanced structuration rate for low-clinker materials. However, a specific attention should be accorded to the calcined clay plant locations, especially when the calcined clay content is higher than the value used in this study. As a perspective, a mix design tool could allow the optimization of environmental impacts depending on expected fresh and hardened properties. • A printable blend is assessed in the Quebec and French contexts. • LC3 allows a 36 % to 46 % reduction in GWP for concrete 3D printing. • For other indicators, improvements prevail over degradations at the endpoint level. • Calcined clay sourcing induces an LC3 GWP variability of 15 % in the Quebec context.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".