Recent advances in CDW-based geopolymers: A review of mechanical performance, structural application, 3D printing, durability and sustainability
Bibliographic record
Abstract
The increasing demand for sustainable construction materials has intensified research into geopolymers, with a particular focus on construction and demolition waste (CDW). CDWs present a promising opportunity for reducing the environmental impact of construction by serving as recycled precursors and aggregates in sustainable geopolymeric materials. Recent studies have confirmed the viability of CDW-based geopolymers, demonstrating numerous advantages, including enhanced mechanical durability, cost-efficiency, and overall sustainability characteristics compared to Ordinary Portland Cement (OPC)-based materials. Nevertheless, several challenges continue to impede their broader adoption in the construction industry. This review synthesizes the latest findings on the mechanical properties, durability, 3D printing applications, structural performance, and sustainability features of CDW-based geopolymers, highlighting critical factors such as the influence of CDW precursors and aggregates, mix design parameters, particle packing and shape characteristics. Literature underscores the necessity for enhanced design methodologies and standardized criteria to improve the practical application of CDW-based geopolymers, particularly in optimizing the mechanical performance, durability, and 3D printing formulations. Important limitations regarding the need for research in critical areas of CDW-geopolymers have been given.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".