Beyond carbon: An integrated LCA–MCDA framework for circularity measurement of ordinary and geopolymer concrete
Bibliographic record
Abstract
In recent years, circular approaches to concrete production have gained increasing attention, yet comparative evaluations between different circular concrete types remain limited and inconsistent. In this context, previous comparative studies between circular ordinary concrete (COC) and circular geopolymer concrete (CGC) have been hindered by biases, including a narrow focus on carbon emissions, neglect of service life differences, and inconsistent compressive strength (CS) comparisons. This study presents a novel integrated framework that combines life cycle assessment (LCA), circular economy strategies, and multi-criteria decision analysis (MCDA) to robustly evaluate the sustainability of COC and CGC across six CS ranges. The case study is based in Tehran, reflecting local material availability, environmental conditions, and priorities, influencing mixture designs and MCDA weightings. This underscores the necessity of region-specific approaches in sustainability assessments. Accordingly, circular mixtures were optimized using response surface methodology (RSM), service life was incorporated via the fib model, and a cradle-to-cradle LCA was performed. MCDA, informed by local expert opinions, prioritized environmental indicators. Results demonstrate that CGC consistently outperforms COC across all examined CS ranges (23–41 MPa), with its sustainability performance improving at higher CS. This advantage is attributed to CGC's lower sensitivity to CS-related environmental burdens and substantially longer service life. Moreover, circularity metrics confirm the benefits of circular designs over linear alternatives, with resource inflow circularity rates of approximately 88 % for COC and 71 % for CGC. This research provides a comprehensive decision-making tool by emphasizing the need for holistic and context-specific assessments to advance circularity in the concrete construction industry.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".