Additive manufacturing of geopolymer composites for sustainable construction: critical factors, advancements, challenges, and future directions
Bibliographic record
Abstract
Abstract Increasing pollution poses enormous pressure on the global ecosystem, with a need to limit the carbon emissions from the construction materials industry. Mitigation of this carbon is possible by converting industrial wastes into alternative cement and optimisation in the building process. Taking this into account, advancement is taking place in sustainable geopolymer composites-based additive manufacturing (AM) technology. Typical precursors for geopolymer binder are industrial waste by-products (such as slag, fly ash, and metakaolin). In another aspect, AM entails several benefits such as easy fabrication, freedom of design, the ability to generate sophisticated structural elements and reduce: expenses, time, waste generation, and labor demands. This review journal paper on geopolymer AM presents a bibliometric study followed by an overview of AM methods and influencing parameters, techniques in geopolymer AM (such as extrusion and powder bed), materials, improvements in AM process, and fresh-state and hardened-state properties. Recent developments in AM processes within the geopolymer are critically discussed while investigating the properties and applications of the same. The discussion includes an analysis pinpointing research gaps essential in developing geopolymer AM. Graphical abstract
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".