Performance evaluation of limestone-blended cement and cellulose nanomaterials in 3D concrete printing
Bibliographic record
Abstract
Three-Dimensional Concrete Printing (3DCP) has emerged as a transformative technology to enhance efficiency, productivity, and safety in construction. However, the high cement content typically used in 3DCP presents sustainability challenges. This study explores the potential of low-carbon and sustainable materials, specifically portland limestone blended cement (GULb) and cellulose nanomaterials, to meet both rheological and mechanical performance criteria while addressing environmental concerns. The response surface methodology was employed to statistically evaluate the effects of water-to-binder ratio ( w/b : 0.30–0.38), nano-fibrillated cellulose (NFC: 0–0.25%), and cellulose nanocrystals (CNC: 0.05–0.15%) on the properties of 17 GULb-based 3DCP mixtures. Key performance indicators included flowability, yield stress, setting time, compressive strength (in both horizontal and vertical directions), and interlayer bond strength. Thermal and microstructural analyses complemented these assessments to validate the observed trends. At a w/b of 0.38 and higher dosages of NFC (0.25%) and CNC (0.15%), superior rheological properties, mechanical strength, and interlayer adhesion were achieved. Numerical optimization revealed that GULb-based 3DCP formulations modified with cellulose nanomaterials can satisfy rheological requirements while achieving a balance of mechanical and interlayer properties. However, the optimum material proportions vary depending on specific design targets.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".